Technical Field

[1] The present invention relates to systems, methods and devices for performing measurements of a borehole, and specifically to generating improved borehole measurement data by automatically controlling the operation of a measurement device disposed within the borehole in response to the data generated by the same.

Background

[2] The term “borehole” is used to collectively refer to any of the various types of holes that may be drilled into a ground surface, whether above ground or underground, for example in order to perform resource exploration or geotechnical investigation or assessment of a site, such as a mine site, to enable the collection of soil samples, water samples or rock cores, or to install monitoring wells or piezometers.

[3] It is often desirable to obtain measurements from a borehole to provide an indication of, for example, the occurrence of particular geological features of strata and/or formation surrounding the borehole (e.g., the occurrence of mineral deposits). Measurement of a borehole is typically performed during a geological survey of the borehole, and the generation, storage, and/or processing of the measurement data is often referred to as “logging” the borehole.

[4] The ability to generate relevant and accurate logging data for a borehole may lead to an improved ability to model geological information of the strata in and/or surrounding the vicinity of the borehole. The ability to perform effective borehole logging therefore provides utility in various applications, including, for example, green fields exploration, or in other activities where exploratory holes have been drilled.

[5] The logging of one or more boreholes is a particularly significant activity in mining applications. The ability to source and extract mineral and resource deposits is becoming increasingly difficult, particularly as the deposits in more accessible areas have been identified and extracted with priority and have therefore become depleted over time. Understanding the geology of a mine site is therefore increasingly important, and this is typically reliant on the ability to perform logging for many boreholes across a particular area of interest (referred to as a “bench”). This enables the creation of a model of the sub-surface (formation/strata) in terms of its geological properties. This information can feed into a geological profile that provides utility for assessing the subsurface and each individual borehole within. Such models are used to increase efficiencies in planning and operating a mine site, for example, during the blasting process.

[6] Borehole logging is therefore an integral process in the collection of geological data across the bench of a mine site. For example, logging may be performed in the context of a geological survey of the borehole (i.e., post-drilling) and/or in combination with information obtained from, for example, “measurement while drilling” (MWD) or “logging while drilling” (LWD) processes.

[7] The ability to obtain logging data from multiple processes, and from different sources (e.g., across a set of boreholes located over a common bench) distinguishes borehole logging operations and systems in mining from other applications. Specifically, conducting an effective borehole logging of a mine site enables greater information to be obtained in relation to, for example, the waste/ore boundaries, identification of grades of minerals etc., that then enables increased mining efficiencies to be obtained. For example, in some cases an explosives loading plan may be modified to take account of the waste/ore boundaries, where the waste material is of a larger blast size than the ore, leading to improved blast efficiencies and yields.

Summary

[8] There is provided a method for logging a borehole executed by at least one processor of a computing device, the method comprising: receiving first measurement data of the borehole, wherein the first measurement data is generated by a measurement device operating in a first operational mode, and during a first movement of the measurement device along a path within the borehole, and in a direction heading into the borehole; generating borehole evaluation data to evaluate the first measurement data of the borehole; determining, based on the borehole evaluation data, a second operational mode of the measurement device to enable the measurement device to generate second measurement data during a second movement of the measurement device in the direction or an opposing direction along the path; and logging the borehole using the measurement device operating in at least the second operational mode, wherein each operational mode determines at least one of: one or more speeds of the measurement device within the borehole; and one or more states of one or more sensors of the measurement device for generating the first and second measurement data.

[9] In some embodiments, the first measurement data is received in real-time, or substantially real-time, with the generation of the first measurement data by the measurement device.

[10] In some embodiments, the path extends substantially between the collar and the end of the borehole.

[11] In some embodiments, the first measurement data and the second measurement data each include: one or more geological measurements of the borehole; and an indication of a corresponding measurement position of the measurement device within the borehole.

[12] In some embodiments, the borehole evaluation data includes region of interest (ROI) data indicating one or more determined ROIs within the borehole.

[13] In some embodiments, determining the one or more ROIs includes processing the first measurement data by: i) retrieving one or more geological model values; and ii) comparing the geological model values to the geological measurements of the first measurement data. [14] In some embodiments, the ROI data includes, for each determined ROI, an indication of the location of the ROI within the borehole.

[15] In some embodiments, the location of each ROI within the borehole is determined over an interval of measurement positions of the first measurement data, and provides an indication of the occurrence of the ROI relative to the path within the borehole.

[16] In some embodiments, the ROI data includes, for each determined ROI, indication of a classification of the formation and/or strata of the borehole within or at the ROI.

[17] In some embodiments, each state of the one or more sensors of the measurement device comprises an activation state indicating whether the corresponding sensor is enabled to generate the measurement data.

[18] In some embodiments, determining the second operational mode of the measurement device comprises generating adjustment data indicating one or more adjustments to at least one of: the one or more speeds; and the one or more activation states of the one or more sensors, of the first operational mode to measure the borehole in the determined ROIs.

[19] In some embodiments, the one or more adjustments include a determination of a speed of the measurement device for generating the second measurement data over a corresponding determined ROI, wherein the determined speed of the measurement device is based, at least in part on, the one or more sensors of the measurement device that are activated for generating the second measurement data over the corresponding determined ROI.

[20] In some embodiments, the method further comprises: retrieving profile data representing characteristics of one or more other boreholes; processing the retrieved profile data to generate one or more of: device configuration data representing, at least, an operational mode of the measurement device; and evaluation criteria data for generating the borehole evaluation data.

[21] In some embodiments, the one or more other boreholes that each have a collar distance to the borehole that is less than or equal to or a pre-determined threshold distance.

[22] In some embodiments, the profile data is maintained by a remote computing system configured to: transmit the profile data from a storage medium of the remote computing system to the at least one processor; receive, from the at least one processor, updated profile data representing one or more characteristics of the borehole as determined based on at least one of the first measurement data and the second measurement data; and store the updated profile data in the storage medium of the remote computing system.

[23] In some embodiments, the method further comprises generating a measurement control signal to instruct a logging device to operate the measurement device to generate the first or second measurement data according to the first and second operational modes.

[24] In some embodiments, the method further comprises: (i) in response to logging the borehole using the measurement device, obtaining logging data of the borehole, the logging data comprising measurement data generated using the measurement device; (ii) processing the logging data to determine whether to re-log the borehole; and (iii) in response to a positive determination in step (ii), performing a relogging of the borehole using a selected measurement device.

[25] In some embodiments, the method further comprises setting or adjusting, based on the logging data, an operational mode of the selected measurement device to perform the re-logging of step (iii) above. [26] In some embodiments, the selected measurement device is selectively determined from a plurality of measurement devices of a logging platform.

[27] In some embodiments, the selected measurement device is determined based on one or more of: a set of device characteristics of the one more measurement devices of the platform; a workflow state of the one or more measurement devices of the platform.

[28] In some embodiments, the selected measurement device used to re-log the borehole in step (iii) is the same measurement device enabling the obtaining of the logging data in step (i).

[29] In some embodiments, the method further comprises: (iv) repeating steps (i) to (iii) in response to the re-logging of the borehole.

[30] There is also provided a method for measuring a borehole using a measurement device disposed within the borehole, the method comprising: generating, by one or more sensors of the measurement device, first measurement data of the borehole, wherein the measurement device operates in a first operational mode and moves in a first movement along a path within the borehole, and in a direction heading into the borehole; processing the first measurement data to generate region of interest (ROI) data indicating: (i) one or more geological ROIs within the borehole; and (ii) one or more corresponding operational mode parameters to enable the measurement device to measure the borehole; determining a second operational mode of the measurement device based on the ROI data; and generating, by the one or more sensors of the measurement device, second measurement data of the borehole by operating the measurement device in the second operational mode during a second movement in the direction or an opposing direction along the path, wherein each operational mode determines at least one of: one or more speeds of the measurement device within the borehole; and one or more states of the one or more sensors of the measurement device. [31] In some embodiments, the second operational mode is determined by modifying the first operational mode of the measurement device according to the corresponding operational mode parameters.

[32] In some embodiments, the one or more ROIs are determined by processing the first measurement data by: i) retrieving one or more geological model values; and ii) comparing the geological model values to the geological measurements of the first measurement data.

[33] In some embodiments, the method further comprises: retrieving borehole profile data representing one or more characteristics of one or more like boreholes associated with the borehole; processing the retrieved borehole profile data to generate one or more of: device configuration data determining the first operational mode of the measurement device; and evaluation criteria data determining the generation of the borehole evaluation data.

[34] There is also provided an apparatus for logging a borehole, comprising: a measurement device; a logging device; and a controller having: a communications interface to receive data; at least one computer processor to execute program instructions; and a memory, coupled to the at least one computer processor, to store program instructions for execution by the at least one computer processor, wherein the logging device is configured to: receive a measurement device control signal from the controller; in response to receiving the measurement device control signal, cause the measurement device to operate within the borehole, wherein the measurement device is configured to: generate first measurement data of the borehole in response to the measurement device being caused to operate in a first operational mode, and during a first movement of the measurement device along a path within the borehole, and in a direction heading into the borehole; generate second measurement data of the borehole in response to the measurement device being caused to operate in a second operational mode, and during a second movement of the measurement device in the direction or an opposing direction along the path; and transmit, to the controller, the first measurement data and the second measurement data, wherein each operational mode determines at least one of: one or more speeds of the measurement device within the borehole; and one or more states of one or more sensors of the measurement device for generating the first and second measurement data, and wherein the controller is configured to: receive the first measurement data of the borehole; generate borehole evaluation data to evaluate the first measurement data of the borehole; determine, based on the borehole evaluation data, the second operational mode of the measurement device; generate the measurement device control signal to cause the measurement device to operate in the second operational mode to log the borehole; and transmit the control signal to the logging device to cause logging of the borehole by using the measurement device operating in at least the second operational mode.

Brief Description of Drawings

[35] Some embodiments are described herein below with reference to the accompanying drawings, wherein:

[36] Figure la illustrates a desired configuration of a borehole to be drilled, in accordance with some embodiments;

[37] Figure lb illustrates the borehole of Figure la as formed following drilling, in accordance with some embodiments;

[38] Figure 1c illustrates an automated borehole logging platform for logging the borehole of Figure la, in accordance with some embodiments;

[39] Figure Id illustrates a first movement of the measurement device (MD) within the borehole, in accordance with some embodiments;

[40] Figure le illustrates a second movement of the MD within the borehole, in accordance with some embodiments;

[41] Figure 2 illustrates a block diagram of the components of the platform, in accordance with some embodiments; [42] Figure 3 illustrates a block diagram of the components of the MD, in accordance with some embodiments;

[43] Figure 4a illustrates a flow diagram of a method for logging the borehole, in accordance with some embodiments;

[44] Figure 4b illustrates an example of performing a Region of Interest (ROI) analysis on first measurement data of the borehole, in accordance with some embodiments;

[45] Figure 4c illustrates an example of generating geological data of the second measurement data of the borehole based on the ROI analysis of Figure 4b, in accordance with some embodiments;

[46] Figure 5 illustrates a flow diagram of a process for evaluating the borehole, in accordance with some embodiments;

[47] Figure 6 illustrates a flow diagram of a process for performing a ROI analysis of the measurement data to evaluate the borehole, in accordance with some embodiments;

[48] Figure 7 illustrates a flow diagram of a process for determining a second operational mode to enable the generation of the second measurement data of the borehole, in accordance with some embodiments;

[49] Figure 8a illustrates a flow diagram of a process by which the logging platform using external data to improve the logging of the borehole, in accordance with some embodiments;

[50] Figure 8b illustrates a flow diagram of a process by which the logging platform utilizes bench data to improve the logging of the borehole, in accordance with some embodiments; and [51] Figure 9 illustrates a flow diagram of a process by which the logging platform performs re-logging of the borehole, in accordance with some embodiments.

Description of Embodiments

[52] In this specification and claims, except where the context requires otherwise due to express language or necessary implication, the following definitions apply.

[53] “Bore hole”, “hole” and “Borehole” refer to a hole drilled by a drill rig in a formation or area of interest or bench which is to be surveyed.

[54] “Surveying” (of a borehole) refers to the process of determining measurements of one or more parameters of the borehole by a measurement device, as the measurement device is moved through the borehole, along a path, over time.

[55] “Geological surveying” refers to the process of determining at least geological data indicating, for example, the mineralogical, structural, or physical characteristics of the formations penetrated by a borehole, using a geo-sensing component of the measurement device.

[56] “Geological data” refers to any data relating to the geophysical, petro-physical, mineralogical, hole geometry, chemistry, thermal, and/or compositional data of the borehole itself, and/or of material in and/or surrounding strata/formation of the borehole itself, as described herein below.

[57] “Depth data” refers to data values indicating a depth within a borehole of a reference device, typically a measurement device also used to generate geological data, the depth being an indication of the substantially linear distance between the position of the device, and a collar position of the borehole, along the axis of the borehole.

[58] “Measurement data” refers to data generated during a surveying process of a borehole by a measurement device at one or more time instants (typically as the measurement device is moved through the borehole), and may comprise, in some cases, of geological data and corresponding depth data.

[59] “Logging” refers generally to making a record of geological data associated with a borehole. In some cases, logging refers to the storage of measurement data generated during a surveying process of the borehole (the “logging data”), where the storage occurs either within the measurement device obtaining the measurements (“on- device”) (e.g., when a wireline is not used), or on another device (“off-device”) (e.g., when a wireline is used to transmit the measured data to another device at the surface). In other cases, logging also refers to the collection, generation, and/or processing of the measurement data. In some cases, logging the borehole may involve transmitting or sending measurement data generated by the measurement device to one or more external devices or systems for subsequent recording of the data.

[60] “Borehole profde data” refers to a collection of data that describes one or more characteristics or properties of a particular borehole, which may include, but is not limited to, logging data of the borehole. For example, borehole profile data may include logging data and additional data, such as locational data (e.g., specifying a position of the collar of the borehole in a mine site), and/or model data (e.g., representing a reconstruction or simulation of the borehole).

[61] “Surface” refers to the top of the ground/formation and/or area of interest including, but not limited to, whatever earth, soil, or land that lies above superincumbent upon or about the collar of the borehole.

[62] “Sub-surface” refers to the region below the surface including, but not limited to, the collar of the borehole into which the borehole (cavity) extends.

[63] “Collar” (of a borehole) - the mouth or opening of the borehole onto the surface, typically created by a drilling operation carried out by a drill rig. [64] Conventional approaches to borehole logging rely on the deployment of a measurement device into the borehole to perform the logging operation as the device travels through the borehole. The measurement device typically includes a set of electronic measuring instruments, or tools, that are adapted to perform a geological survey by using, for example, electrical, acoustical, nuclear and/or magnetic energy signals to stimulate the formations and strata of the borehole and measure the response.

[65] In such conventional approaches, the borehole logging process involves the collection of measurement data during a movement of the measurement device within the borehole. For example, typically the measurement device is lowered into the borehole during an “in-run”, (e.g., via a wireline), and upon reaching the end of the borehole, the logging operations are conducted exclusively during the “out-run” as the measurement device is pulled through the borehole from the end to the collar. During this process, acquisition of the measurement data occurs, by the electronic measuring instruments, while the measurement device is moved at a substantially constant speed along the path, and with a pre-determined configuration of the electronic measuring instruments. This approach is referred to herein as “single-pass uniform” logging.

[66] However, the measurement data generated during the single-pass uniform logging approach is often inadequate. For example, it may be desirable to capture geological survey data at an increased or decreased spatial or temporal resolution for particular geological features occurring within the borehole, relative to the data recorded for other formations or features. The single pass uniform logging approach has no knowledge of where the particular geological features of interest are within the borehole prior to performing the logging. The measurement device is therefore often unable to generate the geological data at the desired resolution or using the appropriate energy signals to measure the particular geological features (particularly when logging a borehole that has multiple features of interest with different geological properties). That is, the single pass conventional approach is limited to generating measurement data with a fixed resolution, because there is no ability to perform a dynamic real-time assessment of the generated data and adjust the generation process accordingly. [67] It is also often desirable to identify errors in the measurements obtained during the logging process. Errors can occur as a result of an incorrect operation of the measurement device. For example, errors in the generated measurement data values may be caused by an incorrect deployment of the calipers through mud sticking to the calipers or the need for the tool to be recalibrated itself. Consequently, errors may occur in the geological data series and/or in the corresponding depth values providing an indication of the locality of the geological data.

[68] A drawback of single-pass logging approaches is a difficulty in detecting and accounting for these errors, and particularly in the errors associated with the geological data values. The identification of errors in the logged data often requires manual analysis by a human operator. As a result, errors are typically detected only after the completion of the logging activity. Cross-validation of the geological data values of the independently obtained data sets may be performed in an attempt to detect and compensate for potential errors. For example, previous approaches to borehole logging have involved collecting multiple data sets for each borehole in consecutive logging runs. However, this only further contributes to the cost and time inefficiency of these approaches since it requires performing multiple separate deployments of the measurement device into the borehole.

[69] Significantly, the conventional single pass approach to borehole logging does not facilitate or assist with validating the operation of the logging platform (e.g., the measurement device) dynamically during the logging activity. This is significant because of the desire to identify abnormal or erroneous measurement values, and to detect any underlying problems with the physical components or the processes of the logging activity (i.e., manifesting as errors in the logged data). That is, there is no capability for the conventional logging approaches to perform an assessment of the logging data to detect abnormalities or errors, and to characterize the source of the abnormalities or errors that are found. This results in an inadequate level of Quality Assurance (QA) or Quality Control (QC) for the measurement data obtained by a borehole logging platform that utilizes the conventional single-pass uniform approach, and an inability of the logging platform to make automated decisions about the quality of the measured data (and therefore whether there is a need to re-log the borehole).

[70] A further issue is the need for a specialized approach to borehole logging that facilitates the logging activities performed on a bench of a mine site. The analysis of a particular formation or geological feature occurring within the bench may involve the generation of logging data for a large number of boreholes (i.e., to construct a geological model that mimics the geology of the formation or features present across the site or bench). This requires the individual logging operations to be capable of generating measurement data with high accuracy and resolution with respect to the geological features of interest, for which the location may be initially unknown or are thought to be known. Profding the bench by performing many conventional logging operations on each borehole is inefficient particularly for a site with a large number of holes.

[71] Furthermore, unlike in other applications such as deep sea drilling, logging on a mine site involves the collection of geological data for a plurality of boreholes within a common bench. These boreholes are planned out to form a “pattern” which describes a drilling process followed by a drill rig. This pattern is likely to have been based on initial exploratory work and/or previously drilled boreholes to ensure that the pattern covers an area where minerals of interest are likely to be located. There are also operational constraints associated with mine site logging operations, and in particular due to the large number of holes that need to be logged in a timely manner (e.g., to enable the delivery, preparation and activation of explosives to blast each borehole, extract the ore, and provide the same to a plant).

[72] The borehole logging process is therefore critical to developing an understanding of the geology, properties and structure of the formation of the bench. The existence of prior determined logging data for related boreholes (such as those boreholes in relatively close physical proximity) provides a source of additional information that may be utilized to improve the logging of newly formed, or other associated, borehole(s) in the same bench. [73] The set of logging data for one or more holes of the bench may be encapsulated by bench profde data. However, utilizing bench profde data to improve the logging process performed on a single borehole is difficult, particularly since the logging data is typically managed by a remotely located computing system or device (e.g., a cloud-based processor operating as part of a bench management system). This is due to the need to maintain consistency in the data collected and logged across the multiple boreholes, potentially by multiple measurement devices operating simultaneously, while also minimizing the transfer of bench profile data to and from other devices (i.e., to minimize the associated communication and storage requirements of sending the data to, and maintaining the data on, the measurement device(s)).

[74] Accordingly, it is desired to develop apparatus, devices, and methods that address one or more of these problems, or other problems, or that at least provide a useful alternative.

Overview

[75] Described herein are embodiments of devices, apparatus, and methods for performing improved logging of a borehole 101 by controlling the operation of a measurement device (“MD”) 104 disposed within the borehole 101 in response to the dynamic evaluation of measurement data generated by the MD 104. A computing device (the “controller” or “controller device”) 120 is configured to receive and process at least an initial set of measurement data generated by the MD 104, and to adjust or set the operation of the device to enable the generation of improved measurements while the device remains within the borehole. Furthermore, unlike conventional single pass uniform logging approaches, the proposed techniques enable an assessment of the measurement data during the logging activity to detect abnormalities and/or errors, and to dynamically characterize the detected abnormalities and errors. This facilitates a borehole logging platform that can operate autonomously, semi-autonomously, such as via a robot or vehicle, to automatically or semi-autonomously (i.e., without human intervention) log a borehole and subsequently perform automated QA/QC, and/or equipment calibration, operations as a real-time response to the logging data generated. [76] The controller 120 generally operates as an edge-processing device configured to receive data and signals, including logging data, from the MD 104, retrieve data from one or more remote devices, such as for example from devices of a cloud-based bench management system 160, and transmit corresponding data and signals to the MD 104. The controller 120 is configured to perform operations including: processing a set of measurements of a borehole 101, including geological data and corresponding positional (e.g., depth) data, generated by the MD 104 according to an operational mode; and modifying the operational mode to enable the MD 104 to generate further measurement data with improved utility for logging the borehole 101.

[77] The first and second measurement data measure respective characteristics of the borehole 101, as represented by geophysical and/or geological values and corresponding indicators of a position of the MD within the borehole 101 during measurement. The evaluation of the first measurement data is performed dynamically by the controller 120 in real-time or substantially real-time with the generation of the first measurement data by the MD 104. In response to the processing, the controller 120 may modify the operational mode of the MD 104 to achieve an improved characterization of the borehole 101 via the generation of a second set of measurement data. The generation of the first and second sets of measurement data occurs as part of a logging process that is integrated with the movement of the MD 104 within the borehole 101 (e.g., during a pair of complementary in-run (first) and out-run (second) movements of the MD 104), such that the MD 104 remains within the borehole 101 throughout the process.

[78] According to the above, disclosed herein are embodiments of a method for logging a borehole 101 using a MD 104 disposed within the borehole 101, and executed by at least one processor of a computing device 120, including: i) receiving first measurement data of the borehole 101, wherein the first measurement data is generated by the MD 104 operating in a first operational mode; ii) generating borehole evaluation data to evaluate the first measurement data of the borehole 101; iii) determining, based on the borehole evaluation data, a second operational mode of the MD 104 to enable the MD 104 to generate second measurement data; and iv) logging the borehole 101 using the MD 104 operating in at least the second operational mode. In some examples, the second operational mode is determined by performing a modification or alteration to the first operational mode of the MD 104 (e.g., reducing a speed value of the first mode by a percentage amount). In other examples, the second operational mode is determined independently of the first operational mode.

[79] The first and second measurement data are generated by the MD 104 during corresponding movements of the device 104 within the borehole 101. In one configuration, the first measurement data is collected during an “in-run” movement in which the MD 104 moves along a path within the borehole 101 in a direction heading into the borehole 101 (i.e., from the borehole collar towards the end of the borehole). The second measurement data is subsequently collected, via the operation of the MD 104 in the second operational mode, during a corresponding “out-run” movement in which the device 104 moves in an opposing direction along the path. This is advantageous in that it enables improved borehole measurements to be obtained in realtime with the insertion and extraction of the MD, and while the MD remains within the borehole.

[80] In another configuration, the first measurement data and the second measurement data are both generated during a single “in-run” movement or a single “out-run” movement. In such a configuration, the first measurement data is generated during a first movement of the MD 104 in a particular direction along a path within the borehole 101, and the second measurement data is collected during a second movement in that same direction along the path. In some embodiments, the first and second movements are distinguished by at least one intermediate period in which the MD 104 is stationary, or substantially stationary, within the borehole 101. In other embodiments, the first and second movements occur continuously without the MD 104 being stationary, or substantially stationary, during any intermediate period. This is advantageous in that it enables measurements of the borehole 101 to be obtained in real-time during a single “pass” of the MD through a section of the interior of the borehole 101. [81] In the described embodiments, the measurement data includes one or more geological measurements of the borehole 101, represented as geological data. In the description herein below, geological data includes any one or more of the following non-limiting types of geological data relating to structural, geophysical, petro-physical, mineralogical, hole geometry, chemistry, thermal, and/or compositional data of the borehole itself, and/or of material in and/or surrounding strata/formation of the borehole itself. It will be apparent to the skilled addressee that, in other embodiments, other types of geological data may be used as an alternative to, or in conjunction with the above. These geological data measurements include, but are not limited to, any one, or a combination of any one or more of the following: gamma radiation emitted by material in and/or surrounding strata/formation of the hole, density of material in and/or surrounding strata/formation of the hole, reflectivity (reflection, absorption or transmission) of electromagnetic radiation, reflectivity (reflection, absorption or transmission) of acoustic or ultrasonic waves, magnetic susceptibility of material in and/or surrounding strata/formation of the hole, electrical resistivity/conductivity/impedance of material in the hole, magnetic vector field, hole dip, hole wall temperature, sonic velocity, contact hardness, hole azimuth, hole diameter, hole profile, hole volume and/or water level. Such geological measurements can be collected by an appropriate sensor of the MD 104.

[82] The measurement data further includes position data representing an indication of a corresponding measurement position of the MD 104, within the borehole 101, for the one or more geological values of the geological data. In some embodiments, the position data values are depth values indicating a displacement of the MD 104 along the path within the borehole 101 relative to a fixed position (e.g., the borehole collar or end). The depth data may be generated by the MD 104 as “self-depth” estimates (e.g., using inertial sensing components incorporated into the device), and/or by a related control apparatus that controls the movement of the MD 104 within the borehole 101 (e.g., using an encoder attached to a wireline configured to deploy the MD 104 into the borehole 101). [83] The operational mode of the MD 104 defines how the MD 104 performs measurements of the borehole 101. For example, the operational mode may determine at least one of: one or more speed of the movement of the MD 104 within the borehole 101 and one or more states of one or more sensors of the MD 104 for generating the measurement data. The state of a sensor may include, for example, whether or not a sensor is activated or enabled to generate measurement data (referred to as an “activation state”) and/or one or more measurement configurations of the sensor as used to generate the measurement data when the sensor is activated (e.g., a collection of settings such as a frequency or period of measurement for which the sensor generates data values). In some embodiments, an operational mode of the MD 104 determines only speed(s) or only sensor state(s) of the MD 104 (e.g., if the MD 104 is configured to operate with speed(s) or sensor state(s) that are invariant to change).

[84] In some embodiments, an operational mode further of the MD 104 determines the state of one or more deployment components of the MD 104, where the deployment components are configured to control the deployment characteristics of the MD 104 other than its speed and sensor state. The deployment characteristics of the MD 104 may include, for example, an alignment and/or positioning of the MD 104, a heading of travel of the MD 104 relative to the borehole axis, and a degree of stabilization of the MD 104, as the MD 104 moves within the borehole. In some embodiments, an operational mode determines any combination of one or more speeds, one or more sensor states, and/or one or more deployment component states of the MD 104, such as to achieve complete control over the deployment of the MD 104 within the borehole 101. The speeds, sensor states and/or deployment component states determined by a single operational mode may vary over time and/or with the position of the MD 104. Alternatively, the speeds, sensor states and/or deployment component states determined by a single operational mode may be fixed over time and/or with the position of the MD 104.

[85] The controller 120 is configured to control, set and modify the operational mode of the MD 104. In some embodiments, the second operational mode is determined by modifying the initial (first) operational mode of the MD 104. For example, the controller 120 may generate adjustment data indicating one or more adjustments to the first operational mode of the MD 104 to measure the borehole. In some embodiments, the adjustments are in the form of relative values that are applied to the current values of the corresponding operational parameters such as to selectively compensate, alter, or switch the operational characteristic of the MD 104 to a desired value (e.g., based on the evaluation of the first measurement data). In other embodiments, the controller 120 is configured to generate adjustment data including replacement values for the operational parameters of the current mode of the MD 104. For example, the adjustment data may represent a difference in a speed of the device 104 and/or a state of the sensor(s), relative to an operational mode, for generating the measurement data over a corresponding determined region within the borehole 101 (as described below).

[86] In some embodiments, an operational mode of the MD 104 determines a combination of the speed of the MD 104 within the borehole and the corresponding state of one or more geological sensors, components or instruments (collectively referred to as “sensors”) 132 of the MD 104 that are operated to measure the borehole 101 at any given time .

[87] The controller 120 may be configured to determine the speed of the device 104 (i.e., in a particular operational mode) based, at least in part on, the state of one or more of the sensors 132 of the MD 104. For example, Total Gamma and Spectral Gamma sensing often provides utility for logging boreholes on a mine site. However, obtaining accurate measurements from a Spectral gamma sensor requires the MD 104 to move at a lower speed compared to the maximum speed achievable to record measurements with a Total Gamma sensor. Similarly, the accuracy of measurements produced by a temperature sensor may decrease when the sensor, and therefore the MD 104, moves above a predetermined speed.

[88] The controller 120 may also be configured to determine the state of one or more of the sensors 132 based on the speed and/or direction of travel of the MD 104 within the borehole 101. For example, the controller 120 may operate the MD 104 with a caliper instrument activated only during an out-run movement of the MD 104 through the borehole 101 (i.e., to avoid damage to the instrument due to the physical configuration of the caliper fingers relative to the interior surface of the borehole 101).

[89] The controller 120 therefore operates advantageously in that, by selectively controlling the speed of the MD 104, together with the state(s) of the sensor(s) and/or the state of the deployment component(s), additional data is collected leading to improvements in the resolution and/or quality of the geological survey obtained (i.e., by the second set of measurement data).

[90] In the some embodiments, the evaluation of the first measurement data involves determining one or more regions of interest (ROI) of the borehole. The ROIs are specified by one or more of the measurement positions of the first measurement data, such as corresponding depth values of the device 104. The ROIs are representative of physical volumes of the borehole strata projected onto the 1- dimensional movement of the device 104 along the path within the borehole 101. The controller 120 determines regions of interest by processing the geological measurements generated by the MD 104 in an initial or first operational mode, for example as occurring during the in-run (first) movement. The geological measurements are compared to geological model data values, such as by the use of thresholding, to determine an abnormality in the borehole strata (i.e., corresponding to a ROI).

[91] In some embodiments, the controller 120 is configured to generate a classification of the formation and/or strata of the borehole 101 within or at each ROI, which may include, for example, an indication of a type of geological material(s) at or near the determined ROI. This may advantageously assist the controller 120 in setting the second operational mode to perform a second measurement of the ROI such as to optimize or improve the utility of the measurement data obtained. For example, the controller 120 may set the speed of the MD 104, control the state of particular sensors, and/or control the state of particular deployment components, based at least in part on the determined, or estimated, geological material(s) at the ROI. In this way, the controller 120 is adapted to capture geological data with increased clarity, and/or granularity, dynamically via the use of the second operational mode to perform measurement of the borehole, such as for example during the out-run (second) movement.

[92] In various embodiments, such as but not limited to those directed to borehole logging on a mine site, the controller 120 is configured to retrieve, process, and/or update external data maintained by a remote computing system. The controller 120 is configured as an edge-processor that accesses the remote computing system to retrieve the external data to improve the effectiveness of logging the borehole 101. The external data may be utilized by the controller 120 to determine an operational mode of the MD 104 to generate measurement data, to evaluate or refine measurement data generated by the MD 104 (e.g., to determine one or more ROIs), and/or to otherwise facilitate improved logging of the borehole 101.

[93] In some embodiments, the external data consists of profile data representing one or more characteristics of one or more other boreholes associated with the borehole 101. The associated boreholes may be identified based on their adjacency to the borehole 101 on the common bench (as determined by a collar-to-collar distance), based on one or more geophysical properties of each associated borehole relative to the borehole 101, and/or based on any other arbitrary criteria.

[94] The profile data may be maintained by the remote computing system configured as a bench management system 160, and as part of a bench profile, where the bench profile may include a collection of borehole data sets, and/or other data describing the mine site.

[95] In some embodiments, the controller 120 retrieves and processes profile data for one or more boreholes of a bench or site including: borehole logging data; borehole pattern data; MWD or LWD data obtained from a drill rig; in-field Geoanalysis data such as assay including chemistry, structural geology data, as obtained from spectroscopic means such as LIDAR, NIR etc; imaging data such as that obtained from a camera, acoustic etc; elastic rock property data of the formation; and other data associated in the construction of the geological profde.

[96] In some embodiments, the external data includes geological reference data such as, for example, measurements, and/or analytically derived values, of geological properties of one or more materials relevant to the borehole 101. The geological reference data may be obtained from a reference database, either as part of the bench management system 160 or another remote computing system. Values of the geological reference data may be updated or set based on previous blasting and/or surveying activities conducted on the borehole 101, on one or more boreholes in the site of the borehole 101, or on other data such as MWD, LWD data.

[97] The external data may be pre-loaded, as obtained from the bench management system 160 or other system prior, to logging the borehole 101 with the MD 104 in accordance with the techniques described herein. Alternatively, the external data may be obtained as a live data stream provided in real-time or substantially real-time to the controller 120. For example, the controller 120 may be configured to receive geological reference data from a materials database in response to detecting particular geological features within the borehole 101.

[98] In some embodiments, the controller 120 is configured to process the external data to adjust or set an operational mode of the MD (i.e., to determine how the MD 104 operates in the first or second movements within the borehole 101). For example, the controller 120 may utilize profile data comprising geological or measurement data such as MWD, LWD data obtained from other boreholes, such as for example boreholes adjacent to the borehole 101, to determine one or more expected ROIs, and/or other properties, of the borehole 101 (e.g., its size, and/or any particular geological materials contained within). The operational mode of the MD 104 may be set based on the expected ROIs, one or more ROIs determined for the other boreholes, and/or any other arbitrary data collected in relation to the other boreholes, thereby resulting in a more accurate logging of borehole 101. [99] In further examples, the controller 120 is configured to utilize the external data to enhance the analysis of measurement data generated by the MD 104 for the borehole 101. The controller 120 may process the retrieved external data to generate evaluation criteria data that is used to evaluate the first measurement data. For example, the controller 120 may determine one or more ROIs for borehole 101 based on one or more geological model data values of profile data obtained from the logging of the adjacent boreholes 101a, and 101b.

[100] Alternatively, or additionally, the controller 120 may determine one or more ROIs for borehole 101 using geological reference data that is retrieved based on prior knowledge about the formations that are likely to be encountered during measurement with the MD 104. For example, if the borehole 101 is within a coal mine then the controller 120 may retrieve reference data specific to types of coal and use the reference data to assist with determining ROIs from the measurement data (i.e., by improving the ability to distinguish between one or more layers of coal and any material that is adjacent to, or interspersed between, the coal layers).

[101] In some embodiments, the controller 120 is configured to utilize the external data in response to evaluating measurement data generated by the MD 104 for the borehole 101. This may include, for example, the controller 120 retrieving and/or processing the external data in response to determining one or more ROIs from an evaluation of first measurement data generated by the MD 104 within the borehole 101. For example, the controller 120 may set or adjust the second operational mode of the MD 104 to generate second measurement data, and/or refine the second measurement data as generated, based on the external data thereby improving the logging of the borehole 101.

[102] The controller 120 is configured to perform various operational control mechanisms, data QC/QA verification and/or device calibration operations in response to processing measurement data generated by a logging activity conducted with the MD 104 and/or external data, including: determining that a re-log of measurement data, or a re -calibration of device 104, is required because for example, the controller 120 identifies an anomaly in the data relating to the geology of the formation or to the tool itself; determining ROIs along the axis of the borehole 101 where additional data may be needed/requested, or is used to supplement data already collected where there is a ROI, or it is identified that there are certain characteristics that requires further investigation; and to selectively perform a second measurement, and therefore logging of borehole 101, based on similarity, or lack thereof, of the first measurement data of borehole 101 to the expected profile data.

[103] In the described embodiments, the controller 120 is configured to perform an assessment of the generated measurement data to identify abnormalities and/or errors, and to subsequently characterize the abnormalities or errors of the measurement data to dynamically improve the operation of the platform. The controller 120 is configured to compare the measurement data, as generated during either the in or out run, against other sources of data including profile data of other boreholes. This enables the controller 120 to make real-time decisions about the nature of the anomalous measurements, such as for example to decide that the errors may be the result of a mechanical malfunction of one or more components of the MD. In response, the controller 120 is configured to conduct an operational check on the MD, in real time or at a predetermined time during the measurement activity (e.g., when the MD is at the top of the hole, at the bottom of the hole, or at another pre-determined position). In response to determining a malfunction, the controller 120 may transmit instructions or signals to cause a recalibration or readjustment of the one or more components, or otherwise take other corrective action with respect to the MD.

[104] In some embodiments, the logging platform is configured to collect multiple sets of logging data for the borehole 101. The sets of logging data may be generated using a single MD 104, or a plurality of measurement devices (MDs) that are identical or similar to the devices described herein. A single set of logging data may include one or more corresponding sets of measurement data generated by a MD during a survey of the borehole 101 (e.g., sets of first and second measurement data generated during in- run and out-run movements respectively). In some embodiments, operations to receive, store, and process logging data of one or more boreholes including borehole 101 for the purpose of re-logging the borehole 101 are performed by a remote computing system (e.g., the bench management system 160). However, in other embodiments a controller 120 of a MD used to conduct a prior logging of the borehole, 101 or one or more other boreholes, or another computing system may be configured to perform the operations.

[105] The proposed systems, devices, and methods provide an improved approach to borehole measurement and analysis, and thereby advantageously facilitate the automatic profiling of a bench in a mine site, by: 1) enabling the collection of improved geological data efficiently by adjusting an operational mode of the MD by evaluating a first set of measurements produced by the device in the operational mode; 2) enabling the measurement and/or logging to be performed in accordance with a workflow involving a pair of in-run and out-run movements of the MD; 3) performing dynamic control over the MD, based on an evaluation of received measurement data, in realtime, or substantially real-time with the receipt of the measurement data (e.g., as a live data stream); and 4) automatically detecting ROIs corresponding to particular geological features of the borehole, and generating improved measurements of each ROI by selectively determining or adjusting the operation of the MD.

[106] The aforementioned advantages enable an improved overall profile of the borehole to be constructed when utilizing the systems, apparatus, devices, and methods described herein, compared to performing borehole logging according to single-pass uniform approaches where the operation of the MD is not dynamically varied. Further, the use of bench profile data, including logging data of previously profiled and/or logged boreholes on a common bench, enables increased efficiency in the logging of Individual boreholes as only those areas of interest are measured. Additionally, as a result of the increase in the quality and granularity of the logging data obtained, profile of the bench is improved enabling better decisions to be made in relation to operations across the mine site (e.g., drilling, logging, and blasting) and consequently resulting in time and cost savings. Borehole logging platform

[107] Figs, la and 1c respectively illustrate the drilling of boreholes and subsequently performing logging for boreholes drilled into surface 109 at a site, such as a mine site. Fig. la shows the desired configuration of a borehole 101 to be drilled in surface 109, as depicted in a pre-drilling state (i.e., prior to creation of the borehole 101). Although Fig. la illustrates the application of the proposed techniques to an above ground mining site, this can be extended to other situations where there is a need to log a borehole after it has been drilled.

[108] As shown in Fig. la, the borehole 101 is created by a drilling apparatus 140 comprising a drill rig 141 configured to position and control the operation of a drilling device 142 including a drill string 146 with a drill bit 148 attached at the end of the drill string 146 that carries out the drilling. In some embodiments, one or more systems are coupled to the drill rig 141, or device 142, such as a drill guidance system 143 and a drill operation system 145 implemented as one or more computing devices. The drill guidance system 143 includes a navigation component for the drill rig 141, enabling the rig 141 to move to particular positions on the surface 109 to drill the one or more holes (including borehole 101) in accordance with a drilling plan that includes predetermined locations to drill the boreholes.

[109] In some embodiments, the drill guidance system 143 also includes a location component configured to determine location co-ordinate values of: the drill rig 141 and the drill device 142. For example, the location component is configured to determine the location for the as drilled position (A) of the collar of borehole 101. These location co-ordinate values determined by the drill guidance system 143 may be the same or different to the drilling plan, due to unforeseen differences on the drilling site, such as for example wear of the drill bit 148, slight misalignment of the drill rig 141, uneven terrain or other circumstances that arise during drilling vs the optimal circumstances on which the drilling plan was based upon. [110] The drill operation system 145 controls the operation of the drill rig 141 enabling the drilling of one or more holes, including borehole 101. In some embodiments, the drill operation system 145 is configured to process data (i.e., hole pattern data, as described below) to determine particular drilling operations required for hole creation. For example, the drill operation system 145 may dynamically configure the drilling apparatus 140 by selecting a particular drill bit 148, and mode of drilling with the selected bit, based on the desired characteristics of the borehole 101 (e.g., hole length and width).

[111] Fig. lb shows the borehole 101 as formed following the completion of the drilling operation (but prior to drill string extraction). The hole 101 extends into the ground at the first position A and terminates at a second position A’ forming the as drilled borehole 101. At the completion of the excavation of the borehole 101 (i.e., once the end of the hole 101 is formed at the second position A’), the drill string 146 is disposed within the borehole 101, such that drill bit 148 resides at the hole end position A’. The drill string will then be extracted to then drill the next hole in the pattern. For mining applications, boreholes 101, lOla-b are created over a bench formed beneath the surface 109. The boreholes of the bench may be created according to a hole drilling plan, such that surface 109 includes a plurality of other boreholes 101a, 101b that are associated with the borehole 101 (e.g., by way of a close relative physical proximity of the boreholes).

[112] In some embodiments, the drill rig 141 is configured to receive hole pattern data representing a desired configuration of the one or more holes, including borehole 101, to be drilled by drill rig 141 on the surface 109. The hole pattern data may include location data representing desired hole locations for each hole, including borehole 101, and navigation data to navigate the drill rig 141 to the vicinity of the desired hole locations. The drill guidance system 143 is configured to process the location data and the navigation data to navigate the drill rig 141 to the desired location where each particular borehole is to be drilled on surface 109. [113] The hole patern data may also include operation data enabling the drilling of borehole 101, in response to the rig 141 arriving at position B on surface 109 enabling the hole to be formed at, or close to, the desired hole location for borehole 101 (i.e., location A). In some embodiments, the operation data includes drilling information, such as drilling device configuration and operational parameters, to create the holes specified by the hole patern data. The drill operation system 145 processes the operation data to control the creation of the borehole 101 by the drill rig 141.

[114] The hole patern data is provided to the drill rig 141 prior to the commencement of drilling of borehole 101, such as by a data exchange with a bench management system (BMS) 160. The BMS 160 may be configured to generate, using the one or more computing devices 161, the hole profile data, such as for example to represent a set of desired boreholes for drilling based on bench profile or model data describing the geology of the bench over surface 109. The hole patern data is stored in the data storage system 162 and transmited to the drill rig 141 via a communications network, such as network 150.

[115] Figs. Ic-e illustrates the operation of one embodiment of an automated borehole logging platform 100 configured to collect measurements from, and generate corresponding logging data for, a borehole (or “hole”) 101. As shown in Fig. 1c, platform 100 includes a control vehicle 106 configured to control the operation of a MD (MD) 104 to enable the MD 104 to collect measurements of a geological formation 107 associated with the borehole 101. The control vehicle 106 may be part of a drilling rig 141 used to form the borehole 101 (as shown in Fig. la), or another special purpose vehicle, such as an automated, autonomous, or semi-autonomous ground vehicle (AGV) configured to position itself at a logging position B on the surface 109 prior to deploying the MD 104 into the borehole 101.

[116] In the described embodiments, the platform 100 includes controller 120, in the form of a computing device including one or more processors configured to execute computer readable instructions enabling the controller 120 to receive, process, generate, and transmit data for logging the borehole 101. The controller 120 is configured as an edge-processing device that controls the operation of the MD 104 for measuring the borehole 101. The controller 120 is in electronic communication with the MD 104 and a logging apparatus 110. Logging apparatus 110 is configured to facilitate the operation of the MD 104 with respect to the borehole 101, including the movement of the MD 104 and the measurements performed by the MD 104, in response to corresponding control signals from the controller 120.

[117] In the described embodiments, the controller 120 is implemented on, or integrated with, the control vehicle 106, for example, as a standalone computing device that performs computational operations for the vehicle 106 (i.e., as a “computing box” or “communications box” of the vehicle 106). In some embodiments, the controller 120 is detachable from the control vehicle 106 for implementation on, or coupling to, another like vehicle. The controller 120 communicates with one or more remote computing systems 160, 165 configured to receive, store and process external data. . Remote computing system 160 includes one or more processing devices 161 and data stores 162 and is configured as a bench management system (BMS) to receive, store and process data associated with boreholes 101, 101a and 101b of the bench within the surface 109. Remote computing system 165 is configured as a data server providing geological, geophysical, and related reference data to the controller 120. The controller 120 communicates with the BMS 160 and remote computing system 165 via an intermediate communications network 150, such as the Internet, or another wide area network such as a cellular network such as a Global System for Mobile Communications (GSM) network enabling the BMS 160 to be physically separated from the controller 120.

[118] In some embodiments of the platform 100, particular components and/or functionality of the controller 120 may be incorporated into the MD 104 to facilitate performing the borehole logging operations described herein in an “on device” mode. In some embodiments, the controller 120 may be physically decoupled from the vehicle 106, for example where the controller 120 is deployed at a fixed position on the surface 109 (e.g., as part of a base station or outpost within the mine site). In such embodiments, control vehicle 106 may include a transceiver device and corresponding communication modules enabling the vehicle 106 to relay measurement data obtained from the MD 104 to the controller 120, and control signals obtained from the controller 120 to the logging apparatus 110 and/or MD 104.

[119] As shown in Fig. 1c, the as drilled borehole 101 extends into the ground at the first (“collar”) position A and terminates at a second (“end” or “toe”) position A’. The logging of the borehole 101 involves moving the MD 104 into and out of the borehole 101 during a pair of complementary in-run (first) and out-run (second) movements of the MD 104. In each of the first and second movements, the MD 104 operates according to a corresponding operational mode. The operational mode may be enabled by the logging apparatus 110 in response to control signals from controller 120. That is, the logging apparatus 110 indirectly controls the operation of the MD 104 in accordance with instructions from controller 120.

[120] In the described embodiments, logging apparatus 110 includes a deployment mechanism 114 and corresponding connection means 119 physically connecting the MD 104 to the control vehicle 106 (via the deployment mechanism 114). The deployment mechanism 114 and connection means 119 are collectively configured to: control the physical movement of the MD 104; and enable the exchange of control signals and data between the logging apparatus 110 or directly with the controller 120 and the components of the MD 104 (e.g., one or more sensors) to facilitate the measurement of the borehole 101.

[121] Figs. Id and le illustrate the respective first and second movements of the MD 104 through the borehole 101, in the example of an in-run and out-run movement pair, as in the logging processes described herein. With reference to Fig. lb, prior to the first movement the MD 104 is positioned at the collar A of the borehole 101. The logging apparatus 110 operates the MD 104 according to a first operational mode involving causing the MD 104 to traverse the borehole 101 along a path X within the borehole 101, and in a direction heading into the borehole (i.e., from A towards A’). The first operational mode specifies operational characteristics of the MD 104 during the first movement (e.g., one or more speeds of the MD 104 measured relatively along the axial path X, and/or the state of one or more deployment components that influence how the MD 104 travels along the axial path X at the one or more speeds).

[122] As the MD 104 is passed into the borehole 101, measurement components of the MD 104 are operated to generate first measurement data of the borehole (as described below). The first movement of the MD 104 ceases in response to the distal end of the MD 104 reaching the end position A’ (or a position as close as possible thereto). With reference to Fig. 1c, the second movement of the MD 104 through the borehole 101 commences when the MD 104 begins to traverse the path taken during the first movement, but in an opposing direction (i.e., heading from A’ towards A). To achieve the second movement the logging apparatus 110 operates the MD 104 according to a second operational mode which, in some embodiments, is defined as a modification to the first operational mode of the device 104. The second movement of the MD 104 ceases in response to the distal end of the MD 104 reaching the collar position A corresponding to extraction of the MD 104 from the borehole 101 is then recommenced.

[123] Figs, lb and 1c illustrate the exemplary in-run and out-run movements of the MD 104 over a path within the borehole 101 extending substantially between the collar A and end A’. In other examples, the logging process may be performed without the MD 104 reaching the end A’ position of the borehole 101, and/or with a single direction of travel of the MD 104 in the borehole 101 being maintained during the first and second movements.

[124] For example, the logging apparatus 110 may insert the MD 104 only to an intermediate position between the collar A and end A’ during the first movement. A second movement may be performed either during a corresponding out-run (i.e., where the MD 104 moves from the intermediate position back to the collar A), or a movement corresponding to further insertion from the intermediate position to another intermediate position closer to the end A’. This enables the platform 100 to efficiently generate logging data for selected sections of the borehole since the full length of the borehole need not be traversed. [125] Figs. Ic-e illustrate the application of the proposed techniques to an above ground mining site in which borehole 101 is logged in a post-drilling state. In some embodiments, the drilling device 142 is configured to obtain hole drilling data during the drilling of borehole 101. In some embodiments, the hole drilling data indicates a configuration of the borehole 101 as a set of as drilled hole parameters including, at least, a collar position indicating a location of the collar of the borehole 101.

[126] In some embodiments, the drill rig 141 uploads the hole drilling data to the BMS 160 for storage and/or processing, to one or more external systems, and/or to a controller 120 of the platform 100 to facilitate the post-drilling logging of the borehole 101. The upload may be performed via the communications network 150, or another similar network, or via a direct connection (e.g., via the connection of a local storage device, or via an Ethernet based local area network transfer).

[127] Fig. 2 illustrates the controller 120, MD 104, logging apparatus 110 and BMS 160 of the borehole logging platform 100 depicted by Fig. 1c, according to the described embodiments. In the described embodiments, the controller 120 is coupled to the deployment vehicle 106 and is configured to control operations associated with performing autonomous logging of the borehole 101, including for example: moving/positioning the vehicle 106 in accordance with the hole drilling data, hole pattern data, or other data; configuring and controlling an operational mode of the MD 104 to measure the borehole 101; instructing the logging apparatus to enable the operation of the MD 104 according to a determined operational mode; receiving first measurement data of borehole 101 collected by the device 104 operating in a first operational mode; evaluating the first measurement data in accordance with the methods described below, and determining a second operational mode for the MD 104 to enable the generation of second measurement data of the borehole 101; receiving the second operational mode and logging the borehole 101 using at least the same.

[128] In some embodiments, the controller 120 is configured to determine additional operational modes subsequent to the second mode, such as a third mode, a fourth mode, etc. The controller 120 is configured to selectively control the operation of at least the MD 104 to operate according to any one or more of the additional operational modes to generate further corresponding logging data simultaneously with, or consecutively to, the second operational mode.

[129] In the described embodiments, controller 120 is implemented as a standalone computing device, and comprises a central system bus (not shown), a memory system 203, a central processing unit (CPU) 202, communications module 206, and I/O device interfaces 204. The CPU 202 may be any microprocessor which performs the execution of sequences of machine instructions, and may have architectures consisting of a single or multiple processing cores such as, for example, a system having a 32- or 64-bit Advanced RISC Machine (ARM) architecture (e.g., ARMvx). The CPU 202 issues control signals to other device components via the system bus, and has direct access to at least some form of the memory system 203.

[130] The memory system 203 provides internal media for the electrical storage of the machine instructions required to execute the user application. The memory system 203 may include random access memory (RAM), non-volatile memory (such as ROM or EPROM), cache memory and registers for fast access by the CPU 202, and high volume storage subsystems such as hard disk drives (HDDs), or solid state drives (SSDs).

[131] The processes executed by the controller 120 are implemented as programming instructions of one or more software modules stored on non-volatile storage of the memory system 203. In some other embodiments, the processes may be executed by one or more dedicated hardware components, such as field programmable gate arrays (FPGAs) and/or application-specific integrated circuits (ASICs). The one or more software modules include: an evaluation module 213 which is configured to process measurement data generated by the MD 104 and generate evaluation data to evaluate the measurements therein; a mode generator module 214 configured to set operational parameter values of a mode of the MD 104; a MD calibration module 215 configured to calibrate the functionality of one or more of the sensors 132 of the MD 104; and a data QA/QC module 216. The data QA/QC module 216 and MD calibration module 215 are respectively configured to assess the measurements of the MD 104, and to perform operations that facilitate the characterization of any determined abnormalities or errors (e.g., instructing the diagnostic tests to be executed on the MD 104). Memory 203 may also include one or more general application programs providing methods, data structures or other software services that define data or perform functions as required by the controller 120 (e.g., an operating system). The data and instructions may reside in multiple parts of the memory system 203, including registers, cache, main memory, and high volume storage.

[132] The I/O device interface 204 provides functionality enabling the user to interact with the device 120 via one or more I/O devices. In some embodiments, the device 120 includes one or more onboard input devices such as a touchpad or touch screen enabling a user to interact with the device 120. The I/O device interface 204 also provides functionality for the device 120 to instruct output peripherals, which may include displays, and audio devices.

[133] In the described embodiments, the MD 104 is connected to the controller 120 via a specialized I/O connector port of interface 204 enabling the transfer of measurement data, including geological and depth and/or position values, to the controller 120 in real-time, or substantially real-time. In some embodiments, the controller 120 is configured to store the measurement data values as a function of time in order to enable post-processing of the data. In other embodiments, the received measurement data values are only processed dynamically in real-time, for example by the invocation of the evaluation module 213 with the received data to and the subsequent invocation of the mode, calibration, and/or QA/QC modules in response to the generated evaluation data.

[134] Communications component 206 is a modem or transceiver device configured to enable the establishment of a logical connection between the controller 120 and other computing devices through a wireless or wired transmission media. For example, in the described embodiments the device 120 is configured to receive borehole data representing the logging data of other boreholes lOla-b from the BMS 160 via intermediate WAN 150.

[135] The controller 120 implements one or more service modules including a structured query language (SQL) support module (e.g., MySQL) enabling data to be stored in, and retrieved from, a data store 208 (such as an SQL database). In the described embodiments, the data store 208 is formed within the memory system 203 and includes data tables, or other structures, configured to store, for the borehole 101: measurement data 210 generated by the MD 104; logging data 211 created by the controller 120 from the measurement data 210; and profile data 212 of the bench. In some embodiments, the data store 208 is configured to store other data associated with borehole 101, such as hole pattern data. In some embodiments, the logging data 211 includes one or more types of correction data associated with the measurement data 210, such as for example corrected depth values that account for the determined error, as generated by the controller 120.

[136] The skilled person in the art will appreciate that many other embodiments may exist including variations in the hardware configuration of device 120, and the distribution of program data and instructions to execute the borehole logging methods described herein.

[137] Measurement device 104 comprises one or more sensors 132 and a MD controller 130 configured to control, at least, the operation of the sensors 132. In some embodiments, the MD 104 is configured as an embedded system where the MD controller 130 is configured with a processor 131 and memory (not shown) implemented as an integrated microcontroller with a RISC architecture, and the sensors 132 configured as peripheral devices providing data to, and receiving control data from, the microcontroller.

[138] In some embodiments, the MD controller 130 includes one or more operational modules configured to store data including, at least, the measurement values generated by the sensors 132. In some embodiments, the MD controller 130 is configured to store additional data including: position data; and optionally depth offset, correction and/or adjustment data generated by, or provided to, the MD 104. For example, depth offset data may include a depth offset value enabling the MD 104 to correct the depth values recorded during measurement of the borehole 101 (i.e., where the depth values may be provided to the MD 104 by the logging apparatus 110, and/or generated on-device by the MD 104).

[139] The MD 104 is configured to communicate with the controller 120 via the I/O interface 204 and a corresponding interface within the MD 104. In one embodiment a wired connection is established between the MD controller 130 of the MD 104 and the controller 120, such as via an Ethernet cable passed through, or integrated with, the wireline 119. For example, where the controller 120 is located on the deployment vehicle 106, the cable may be housed within a cable enclosure of the wireline 119 and passed through the deployment mechanism 114 (not shown). The logging apparatus

110 is configured to relay signals and data from the MD controller 130 to the controller 120 via the I/O 204 connection means. In other embodiments, the MD controller 130 may include a wireless network interface implementing the IEEE 8O2.xx family of networking protocols 128 enabling the exchange of information wirelessly with the controller 120 (e.g., over technologies such as WiFi). Communication between the logging apparatus 110 and the controller 120 may also occur over a wireless channel, for example in embodiments where the controller 120 is deployed remotely to vehicle 106.

[140] In the described embodiments, the MD 104 is a geological logging tool configured to measure one or more geological parameters of the borehole 101 and/or the formation/strata 107 surrounding the borehole 101. The one or more sensors 132 are collectively configured to generate geological data representing one or more geological parameter measurements of the borehole 101 and/or the formation/strata 107 surrounding the borehole 101 in response to the deployment of the device 104 into the borehole 101 by the logging apparatus 110. [141] In some embodiments, the MD 104 can include local positioning components configured to determine positional and/or orientation information of the MD 104 itself and/or the sensors 132. The local positioning components may include, for example, gyroscopes, magnetometers, and/or accelerometers configured to generate data indicating a position, slew, and/or angle of the depth sensors with respect to a local reference point defined at another position on the MD 104. In the described embodiments, the local reference point is the point of connection of the deployment mechanism 109 to the MD housing or this point could be any other known or identified reference point on the MD.

[142] The logging apparatus 110 includes one or more electrical and/or mechanical components configured to collectively: exchange control signals and data with the controller 120; and operate the device 104 to measure the borehole 101 in accordance with control signals received from the controller 120. In some embodiments, the logging apparatus 110 is mechanically and/or physically integrated with the deployment vehicle 106 to collectively form an autonomous, automated or semi- autonomous robot configured to perform automated logging of the borehole 101 that is dynamically controlled by the controller 120 (which may be separated from the vehicle 106).

[143] The control apparatus 110 includes a signal processing device 112, such as a computing device with at least one processor, configured to receive control signals and data from the controller 120. The signal processing device 112 operates the MD 104 in response to the control signals and data received from the controller 120 via the generation and transmission of corresponding signals and data to a deployment mechanism 114. In some embodiments, the logging apparatus 110 is also configured to transmit and receive data from the MD controller 130 of the MD 104, such as for example to set, modify or adjust operational characteristics of the MD 104 in response to instructions from the controller 120.

[144] The deployment mechanism 114 is configured to enable the movement of the MD 104 in the borehole 101, where the characteristics of the movement experienced by the MD 104 within the borehole 101 are controlled according to an operational mode of the device 104. The deployment mechanism 114 may include, for example: a wireline 119 provided with an overshot that can engage with the MD 104; and a winch (not shown) connected to the wireline 119 to lower the MD 104 downwards through the borehole 101 and raise the MD 104 upwards through the borehole 101.

[145] In some embodiments, the logging apparatus 110 includes a depth counter device 116 configured to quantify the depth of the MD 104 within the borehole 101. For example, in some embodiments the depth counter 116 is an encoder configured to measure an amount of wireline 119 currently deployed in response to the movement of the MD 104 within the borehole 101. In some aspects, the counter 116 includes digital or analogue circuitry adapted to count successive length values corresponding to indications of the wireline 119 presently implemented. In other aspects, the counter 116 is a mechanical device configured to read one or more physical properties of the wireline 119 to determine the length. In some aspects, the mechanical device operates by processing one or more measurement units included along the wireline 119 length.

[146] In the described embodiments, the depth values generated by the depth counter 116 represent the length of the borehole 101 (i.e., the “wireline” or “measured” depth). While in conventional vertical boreholes or wells this coincides with the true vertical depth, in directional or horizontal wells, especially those using extended reach drilling, the two measurement types produce different values.

[147] Fig. 3 illustrates an exemplary configuration of the MD 104 according to the described embodiments. The components of the MD 104 may be arranged in interconnected sections enabling the modular attachment and detachment of the components in accordance with a desired function of the MD 104. Exemplary components of the MD 104 include: a deployment connector 133 enabling the device 104 to be lowered into the borehole 101 via the wireline 119; one or more geological sensors 132a, 132b (e.g., electrical or electronic sensors) and one or more instruments 132c collectively configured for geological or geophysical parameter surveying; and a housing 139 that encapsulates the components. The housing 139 is composed of a resilient material, such as a metal or hard plastic, to provide protection to the internal modules during movements of the MD 104 within the borehole 101 (e.g., as the MD 104 is lowered into the hole 101 during the first movement and as it is extracted during the second movement of the logging process).

[148] As depicted in Fig. 3, the geological sensors may be grouped into one or more sensor sets 132a, 132b positioned at physically distinct locations along the axial length of the MD. For example, geological sensing modules 132a, 132b form sets of electromagnetic sensors that are collectively configured to generate data representing one or more geological data measurements of the borehole 101, and/or the formation/strata 107 surrounding the borehole 101, during the measurement process. Exemplary embodiments may include, for example, a total gamma system 132a configured to detect gamma radiation through the scintillation of light produced by the interaction of the gamma rays with a scintillator crystal material.

[149] The MD 104 may also include a magnetic susceptibility and conductivity system 132b, including at least one receiver coil responsive to at least one transmitter coil to obtain both magnetic susceptibility and conductivity measurements from a region surrounding the MD 104.

[150] In some embodiments, either or both of geological sensor sets 132a 132b may include a temperature sensor, a water sensor, a deviation sensor that can sense pitch, roll and heading, or any other number of varying sensors or modules. For example, the geological sensors 132a may include an infrared (IR) sensor. In some embodiments, the IR sensor measures temperature associated with the borehole which is then processed to produce an electrical signal providing an indication of a temperature associated with the borehole 101 (e.g., a temperature at the collar or an interior surface).

[151] Additionally, in some embodiments the MD 104 is provided with centralizers 134 at, or adjacent to, proximate and distal ends of the MD 104 that are positioned about the housing 139. The centralizers 134 can assist with stabilizing the MD 104 within the borehole 101 which can further assist the functionality of sensors 132, for example a magnetic susceptibility sensor.

[152] In some embodiments, the instruments 132c of the sensors 132 include one or more mechanical or electromechanical devices configured to make physical contact with the formation/strata of a borehole during the measurement process. For example, the instruments 132c may include a caliper set having a number of caliper fingers that, when activated, extend outwards from a body of the MD 104 to collectively provide a measurement of the diameter of the borehole 101.

[153] Under normal operation of the caliper set, each caliper finger deploys to an extended length until the finger is stopped by an abutment (e.g., the side of the borehole wall). The amount of extension of each finger the may be detected by the MD controller 130 thereby providing a measurement of the distance between the outer housing 139 of the MD 104 and the borehole 101. This enables the generation of distance measurements representing a diameter of the borehole 101.

[154] lin some embodiments, the instruments 132c includes a modified caliper (either as an addition to, or replacement of, the standard caliper described above). The modified caliper may provide extended and/or additional functionality to that of a standard caliper. In some embodiments, the modifed caliper includes one or more components that enable the modified caliper to generate measurement data without physical contact between the caliper and the borehole 101 during movements of the logging process. The components of the modified caliper may include, for example, one or more imaging devices (such as cameras), one or more emitter/detector devices (such as IR or acoustic subsensors), or any other device configured to take measurements of a surface or region of the borehole 101.

[155] In one example, the modified caliper is configured with components attached to an end of corresponding fingers, and operates by extending each finger towards the side of the borehole wall, but stopping the extension prior to physical contact with the wall. This is advantageous in that the component is placed close to the borehole interior wall thereby improving the accuracy of the measurements obtained by the component, without requiring physical contact of the same with the borehole (and thereby avoiding potential damage or wear).

[156] In some embodiments, the sensors 132 include one or more imaging devices or sensors configured to generate data representing images of the interior of the borehole 101. For example, sensors 132 may include one or more cameras, such as monographic cameras, stereographic cameras, or laser scanning devices, and/or radar, or light-based radar (LiDAR) devices that generate imaging data related to shape, color, depth, and/or other features of object(s) of the borehole that are in the line of the sensors. The imaging data may be processed, by the MD 104, the controller 120 or another computing device to assist with understanding the nature of the borehole 101 according to the techniques described herein. In some examples, some or all of the imaging devices or sensors are configured as components of the modified caliper. In other examples, the imaging devices are configured as one or more sensor groups, which may be located, for example, on the body or at the base of the MD 104.

[157] In some embodiments, the MD 104 is provided with one or more one or more deployment components 132d. Each of the one or more deployment components 132d is a physical instrument, device, or apparatus that is configured to control the deployment characteristics of the MD 104 in the borehole 101. The deployment characteristics generally refer to the properties of the MD 104, other than its speed and sensor states, that characterize its motion during first, second, and/or other movements within the borehole 101.

[158] For example, the deployment components 132d may include bracing and/or stabilizing devices that, when activated, cause the MD 104 to be oriented or positioned in a certain way relative to one or more interior surfaces of the borehole 101. One or more deployment components 132d may be activated in combination with one or more of the sensors 132a, 132b and/or instruments 132c. For example, a bracing device may be activated to position a sensor 132a against the interior wall of the borehole 101. In some embodiments, one or more of the instruments 132c are also deployment components 132d. For example, a caliper set of the MD 104 may act as a sensor to provide a measurement of the diameter of the borehole 101. The caliper set of the MD 104, when activated, may also force the MD 104 into a particular alignment relative to the axial path X of the borehole 101, or provide a degree of stability to the movement of the MD 104 (thereby influencing the deployment characteristics).

Dynamic borehole logging

[159] Fig. 4a illustrates a flow diagram of a method 400 for logging a borehole 101 using the logging platform 100 described herein. At step 401, a device configuration process is performed to initialize the MD 104 in preparation for conducting measurements of the borehole 101. In the described embodiments, device configuration involves: i) component set-up; and ii) initial operational mode determination.

Configuration

[160] During component set-up, the measurement functionality of the MD 104 is configured via the selective modification of one or more physical elements of the MD 104, such as for example to replace, add or remove particular sensors 132 in accordance with any geological surveying operations that may be desired to be performed.

[161] Component setup may be performed on the basis of data obtained from one or more computing devices, such as the controller 120 and/or devices of the BMS 160. A user may operate the interactive user interface elements of the controller 120 to obtain and present bench data indicating one or more properties of the formation 107, and may select the components of the MD 104 based on the geological surveying activities to be performed in view of the properties of the formation 107.

[162] For example, it may be desired to measure borehole 101 with a geological survey that detects coal deposits in formation 107, in which case the MD 104 may be configured with at least total Gamma and Spectral Gamma sensors in geological sensor sets 132a 132b to enable the detection of the quality of the coal deposits. In another example, the borehole measurements may be directed to determining the water levels within the borehole. In another example, the MD 104 is configured to measure the borehole diameter with a caliper sensor that is included as part of the instruments 132c.

[163] Other components that may be added to, removed from, or otherwise adjusted during set-up include one or more components that determine the position of the MD 104 within the borehole, such as for example depth estimation and/or correction components.

[164] In some examples, MD 104 includes one or more sensors that are designated to generate specialized measurements for specific types of rock, or other geological matter, within the formation 107. The process of generating specialized measurements of a region or zone of the formation, in response to identifying the presence of particular geological material within, is referred to as “characterizing” the formation.

[165] The one or more sensors designated for characterizing a particular rock type may be determined during the configuration step 401. For example, the user may assign or otherwise specify the sensors 132a 132b or instruments 132c that will be activated to generate the specialized measurements for one or more predetermined types of rock (e.g., coal, limestone, sandstone, etc.) in response to the identification of these rock types in the formation 107, according to the techniques described herein.

[166] Device configuration involves the determination of an initial operational mode of the MD 104. In the described embodiments, an operational mode O is specified by: a set of one or more speeds (i.e., absolute velocity values) of the MD 104 within the borehole 101; and an activation state {x , x ₂ , ... , xjj} of each sensor s of the one or more sensors 132 of the MD 104, for generating corresponding measurement data m. In other embodiments, the operational mode may specify measurement properties or configurations of each of the one or more sensors 132 of the MD 104 (e.g., a period, frequency, or signal strength associated with the measurement capability of the sensor when activated). Each speed value v is a real number representing the desired speed by which the MD 104 is to move through the borehole 101 while in mode O. Each activation state value x ^s represents a binary state of sensor s as: “enabled” (i.e., value ‘ 1’) in which the sensor s generates a geological measurement g ^s at a corresponding depth d; and “disabled” (i.e., value ‘0’) in which the sensor does not generate any measurements.

[167] The controller 120 is configured to create the operational parameter values such that the MD 104 has constant (static) operation over time and distance travelled through the borehole 101. In this configuration, the operational parameters include a single speed v and activation state x ^s for sensor s such that the operation of the MD 104 does not vary during a corresponding movement through the borehole 101. That is, when operating according to a static type operational mode 0 the MD 104 will move with constant speed v and with each sensor s being fixed in an enabled or disabled state.

[168] The controller 120 is also configured to create dynamic operational modes in which the operational parameter values vary over time and/or distance travelled by the MD 104 through the borehole 101. A set of N speed v and sensor activation states x ^s of sensor s are each defined for corresponding depth intervals [d _L , dj ... [d _L , dj _N , each representing a section of the borehole 101 through which the MD 104 passes while operating in the mode 0. That is, when operating according to a dynamic type operational mode 0 the MD 104 moves through the borehole 101 with a speed and/or enabled or disabled sensor states that vary based on the position of the MD 104 (i.e., its depth) within the borehole 101.

[169] At configuration step 401, the controller 120 generates operational parameters defining an initial operational mode 0 ₁ of the MD 104. In some configurations, the initial (first) operational mode O is a static mode defining a constant speed v and activation states x ¹ , ... , x ^s for each of the S geological sensors 132. Initial operational mode O may be set by the user according to a default set of values. In some embodiments, the default set of values may be chosen based on parameters of a desired geological survey (e.g., to detect particular mineral deposits, such as coal deposits). In other embodiments, the initial operational mode 0 of the MD 104 may be a dynamic mode in which variable speeds and sensor activations are determined for particular depth intervals based on, for example, logging data of other boreholes lOla-b as obtained by the controller 120 via the BMS 160.

[170] In some embodiments, the initial operational mode 0 ₁ of MD 104 is determined by performing one or more sensing or measurement activities before the MD 104 is deployed into the borehole 101. For example, the controller 120 may be configured to perform a pre-deployment scan of the collar of the borehole 101 with one or more of the sensors 132 of the MD 104. Thermal scanning may be performed by an IR temperature sensor 132a 132b to provide an indication of the collar surface temperature of the borehole 101. The controller 120 determines the initial operational mode O by processing the measurements generated by the pre-deployment scan. For example, if the collar surface temperature is elevated compared to a threshold value then a relatively lower speed may be selected for the MD 104 in anticipation of thermal issues, as compared to a standard or default speed. In some embodiments, the controller 120 retrieves external data, such as profde data or other data, to process measurements generated by the pre-deployment scan.

[171] Controller 120 transmits the configuration data, including data describing the operational mode O ₁₅ as part of a control signal to the logging apparatus 110 via I/O interface 204. Operational parameter data representing the speed values is received by the logging apparatus 110 for processing and storage in the signal processing device 112. The logging apparatus 110 is configured to relay the operational parameter data representing the sensor activation states {x , x ₂ , ... , x^} to the MD controller 130 of the MD 104 via the transmission of a corresponding MD control signal through the wireline 119.

[172] In other embodiments, the initial operational mode of MD 104 may be predetermined (e.g., based on factory or vendor settings) such that configuration of the MD 104 is not required prior to the deployment of the MD 104 into the borehole 101 to commence the logging process.

[173] In some of the examples described herein, each operational mode of the MD 104 determines both a set of speeds of the MD 104 and a set of states of the sensors 132 of the MD 104. However, in other examples a given operational mode of the MD 104 determines either a set of speeds, or a set of states of the sensors 132, of the MD 104 exclusively.

[174] For example, the MD 104 may include sensors 132 that are capable of operating only in a single ‘default’ state for the purpose of generating measurement data (e.g., an always on state). Accordingly, in this case an operational mode of MD 104 may specify sets of speeds of the MD 104, but not any corresponding state of any of the sensors 132, during a respective movement. In another example, the speed at which the MD 104 can be deployed into the borehole 101 may be a fixed predetermined ‘default’ speed (e.g., due to constraints of components external to the MD 104, such as the wireline, the winch, or similar). Accordingly, in this case an operational mode of MD 104 may specify states of the sensors 132 of the MD 104, but not any speeds of the MD 104, during a respective movement.

[175] In other examples, the operational modes of the MD 104 that determine at least one of: a set of speeds of the MD 104; and a set of states of the sensors 132 of the MD 104, may additionally determine a set of states of deployment components of the MD 104, as described herein.

Generating first measurement data

[176] Following the component set-up, the MD 104 is deployed into the borehole 101. On deployment the first movement of the MD 104 into the borehole is along the axial path within the borehole (i.e., in the direction heading into the borehole), where the movement and operation of the sensors 132 are in accordance with O ₁ . [177] MD 104 is configured to generate one or more measurements of the borehole 101 according to the operational mode of the device 104. In the described embodiments, the MD 104 generates measurement data m comprising, at least, a series of geological data samples g ^s from each sensor s. Also included in the measurement data is a series of depth values d indicating a corresponding depth of the MD 104 within the borehole 101 for each geological measurement.

[178] At step 402, the controller 120 receives the first measurement data of the borehole 101. The first measurement data m ₁ includes the set of geological data series values {g ^s } from each of sensors 132 and the corresponding depth values d. In some configurations, the MD 104 generates depth values, and/or corrected depth values based on initial depths provided by the logging apparatus 110, and provides the same to the controller 120 as part of the first measurement data . In other embodiments, the depth values d are received from the logging apparatus 110 separately to the geological data values received from the MD 104, where the depth values d and geological values {g ^s } collectively form the measurement data as received by the controller 120.

[179] Fig. 4b illustrates an example of the generation of first measurement data m ₁ resulting from the measurement of the borehole 101 by the MD 104 during the in-run (i.e., first movement). In this example, the operational mode O is a static mode such that the device 104 moves at constant speed v during the in-run and where a sensor si is fixed in the enabled state to constantly generate measurement values forming the geological data series g ^s ■■■ }• The geological data series g ^sl is related to a corresponding series of depth values d = [d ₁ , d ₂ ... } representing the depth of the device 104 in borehole 101 for the geological measurement value.

[180] The movement of the device 104 is enabled by the deployment mechanism 114 of the logging apparatus 110. For example, the mechanism 114 may include a winch which utilizes gravity to lower the MD 104 into the borehole 101, via wireline

119, at constant speed v. In the described example, the first movement ceases once the MD 104 reaches the bottom of the borehole 101 at position A’, or is otherwise prevented from travelling any further towards position A’ (e.g., by a blockage or other obstruction). In other examples, the path of the MD 104 may not necessarily extend fully between the collar position A and the end of the borehole A’.

[181] The measurement data includes the set of geological data values g ^sl generated by the activated sensor si of the sensor set 132. The geological data values g ^sl of the formation 107 surrounding the borehole 101 over consecutive corresponding depth measurements provided by the depth data values d together generate a geological profile. At step 404, the controller 120 evaluates the first measurement data by processing the data to generate corresponding borehole evaluation data. The type and form of the generated borehole evaluation data is dependent on the steps performed by the controller 120 as part of the evaluation process. In some embodiments, the controller 120 is configured to store at least part of the first measurement data as a measurement data set 210 in data store 208. Values of may be stored in set 210 using an array, list, table or other data structure in an implementation enabling the values of m ₁ to be indexed in association with borehole 101.

[182] As shown in Fig. 5, an exemplary evaluation process may include one or more of the following: performing a region of interest (ROI) analysis on the measurement data m ₁ (i.e., at step 502); performing a quality assurance/quality control (QA/QC) analysis on the measurement data m ₁ (i.e., at step 504); and performing an equipment calibration analysis on the measurement data (i.e., at step 506). In some embodiments, the outcome of an equipment calibration analysis is used to perform further QA/QC analysis on the measurement data (i.e., as part of an iterative decision making process for characterizing the abnormalities or errors in the measurement data).

Region of interest based borehole data evaluation

[183] Fig. 6 illustrates an exemplary process for performing a ROI analysis of the measurement data m ₁ . With reference to Figs. 1c and 4b, formation 107 may contain a plurality of distinct geological materials 107a, 107b that occur with a degree of spatial continuity with respect to the depth dimension of the formation 107, and therefore the borehole 101 that passes through the same. A ROI analysis seeks to identify the spatial regions of interest (ROIs), also referred to as “lithological units” or “rock zones”, in which distinct geological material 107a, 107b exist relative to the material of the bulk of the formation 107 or differences in quality exist within each of the lithological zones in the ROI, for example, in coal there could be variations in coal quality in the same zone or seam of coal.

[184] In the described embodiments, the ROI analysis process 502 evaluates the first measurement data m ₁ collected from the borehole 101 to determine one or more regions or zones of formation 107 where there exists a deposit of geological material, minerals, or an otherwise significant geological or geophysical feature. At step 602, the evaluation module 213 optionally pre-processes the values of the measurement data m ₁ including the values of the geological data g ^sl and the corresponding depth values d. Pre-processing may involve transformation, scaling, windowing and/or any other operations to enable or facilitate the comparison of the measurement data to the data of one or more ROI models A.

[185] At steps 604 and 606, the evaluation module 213 of the controller 120 performs a comparison between the geological data series values generated by the one or more sensors 132 of the MD 104 and the ROI models, and identifies any existence of ROIs from the comparison result. ROI models may be extracted from external data, such as data retrieved from the data store 208, for example, as part of the profile data 212 associated with the bench. Each ROI model includes one or more model parameters with values that correspond to the geological measurements generated by each activated sensor in the first measurement data m-t. Various different approaches to ROI modeling may be implemented by the controller 120, including, but not limited to, (i) thresholding and (ii) pattern recognition or iii) any other type of classification. The geological data series values generated by any one or more sensors 132 can be used, independently or in any combination, to identify a ROI according to the modelling techniques described.

[186] In the thresholding approach, each ROI model A defines a threshold value T, or a threshold interval [T _min , T _max ], that are compared with the geological data values of a particular sensor of the sensors 132 (e.g., values of the geological data series g ^sl ) to determine the one or more ROIs. One or more geological data values generated by each activated sensor s are compared to the threshold value, or interval values, of ROI model A to determine whether a ROI starts, continues, or ends at the corresponding depth value of the geological data value(s).

[187] In the pattern recognition approach, the evaluation module 213 determines a set of geological data values generated by each activated sensor, at a given depth d. to be used as a feature vector for scoring against the one or more ROI models. For example, the evaluation module 213 may be configured to perform probabilistic classification where the ROI models are Hidden Markov Models (HMMs) or Gaussian Mixture Models (GMMs) trained using supervised or unsupervised machine learning. Geological data feature vector G comprising the geological sensor values measured at the corresponding depth is scored against each model to produce a likelihood score p(G|A). The evaluation module 213 may be configured to determine whether a ROI starts, continues, or ends at the corresponding depth by a relative comparison of the likelihood scores generated across the ROIs models.

[188] In some embodiments, the ROI models include an abnormality model enabling ROIs to be determined by the detection of abnormal, atypical, or otherwise unexpected geological data values. For example, the abnormality model A _norm may define a threshold value above which geological measurements of the formation 107, for a given sensor s, are interpreted as indicating the presence of a deposit of geological materials, minerals, or an otherwise significant geological or geophysical feature at the given depth in the formation 107. The abnormality model A _norm may be selected by the evaluation module 213 based on the contents of the data store 208, such as for example the profile data 212 of the bench. The abnormality model A _norm may be specific to the known or expected geological structure, profile, or other physical property of the formation 107, as it exists at the time of the logging of the borehole 101. In some embodiments, the ROI models include a plurality of abnormality models. For example, abnormality models may be defined for different lithological types of rock units to identify corresponding ROIs. [189] In some embodiments, the ROI models include geological material specific models (e.g., _coai ) that, for example, define threshold intervals (e.g.,

[Tmincoab T _m axCoai]) within which a geological measurements of the formation 107, for a given sensor s, are interpreted as indicating the presence of the geological material (e.g., of a coal deposit). The evaluation module 213 may be configured to perform a plurality of comparisons between measured geological data values {g ^s } and the parameters of a corresponding plurality of ROI models. For example, the evaluation module 213 may determine, for depth d, the difference between the geological data value g^ and: an abnormality threshold value T _norm of abnormality model 2„ _orm (which may be specific to formation 107), and respective minimum and maximum interval bounds T _minCoab T _maxCoai for the ‘coal’ ROI model A _coai .

[190] At step 606, one or more ROIs . . . , R _N are identified based on the comparison results of step 604. Evaluation module 213 determines for each ROI I?!,..., R _N , an indication of the location of the ROI within the borehole 101. In the described embodiments, the location of each ROI is a depth interval indicating the occurrence of the ROI as a spatially continuous segment along the path taken by the MD 104 to collect measurement data m ₁ within the borehole 101.

[191] In some embodiments, the evaluation module 213 is configured to determine a classification of the formation 107 and/or strata of the borehole within or at one or more of the determined ROIs. For example, a ROI determined between two depths d _T and d ₂ (i.e., R = [d , d ₂ ]) may be classified in association with a geological material ‘M’ on the basis of the corresponding geological data values g ^s of sensor s being substantially within a threshold interval defined by _M during the comparison step 604. In other examples, the ROI may be determined between the two depths d _T and d ₂ by a classification operation performed with any arbitrary model and number of features.

Example: determining ROIs from first measurement data

[192] Fig. 4b illustrates an example of performing a ROI analysis on first measurement data m ₁ collected over an in-run of MD 104 through borehole 101. Measurement data includes geological data measurements g ^sl at corresponding depths d produced by the activated sensor si. For example, the MD 104 may be configured to conduct a geological survey to identify coal deposits in the formation 107.

[193] In one implementation, sensor si is a total Gamma sensor configured to measure at a predetermined frequency, where the speed of MD 104 is set to v = 10m/ minute. A geological profile of the borehole 101 is obtained by plotting the measurements of sensor si g ^s against the corresponding depth values d as shown. The path through the borehole 101 taken by the MD 104 extends 40 metres from the collar position A (i.e., d ₀ = 0m) to the end position A’ (i.e., d ₅ = 40m).

[194] The geological profile of the borehole 101, as generated by enabled total Gamma sensor si, is characterized by sets of sensor measurements with significantly higher magnitude measurements at depths corresponding to sub-formations 107a, 107b of the borehole 101 (i.e., where coal deposits occur). To detect ROIs corresponding to the sub-formations, the evaluation module 213 is configured to compare geological measurements g ^s with at least one ROI model. The ROI model in this example is an abnormality model _norm with depicted threshold value T _norm such that the geological measurement sample generated at depth d is flagged as abnormal if > T _norm (e.g., sample g^ _e generated at depth d _e shown in Fig. 4b).

[195] Evaluation module 213 determines two ROIs, RO^ = [d d ₂ ] and ROI ₂ = [d ₃ , d ₄ ], in which the geological measurements generated are considered abnormal according to model _norm . Determination of the ROIs may involve windowing, smoothing, averaging or similar operations such that a minimum number of geological measurements within a depth interval must meet the ROI model criteria (e.g., exceed threshold T _norm for model A _norm ) in order for the corresponding depths to be considered as forming a ROI.

[196] Other implementations may involve the use of different sensors to generate the first measurement data m ₁ , for example over the in-run of MD 104. For example, sensor si may be a temperature sensor configured to provide geological measurements g ^sl as estimates of a temperature of the formation 107 at corresponding depth values d. The evaluation module 213 is configured to determine ROIs based on the temperature values of the geological measurements, and one or more ROI models (e.g., an abnormality model _norm ). For example, the threshold value T _norm may be a predetermined temperature above which the formation 107 is considered to be “hot” at the measured depth. The evaluation module 213 may be configured to determine one or more ROIs corresponding to the hot regions within the borehole 101.

[197] In some examples, the MD 104 may include a series of sensors that are configured to generate data associated with producing or verifying a geological classification of the ROI (i.e., to indicate the presence of a geological material or rock type in the region). The classification may be used either to verify a prior geological classification made for the ROI based on other data (e.g., by comparing the measurements g ^sl to an ROI model) or to correct the prior geological classification.

[198] In another example, sensor si may be an instrument 132c configured to provide geological measurements g ^sl related to one or more physical characteristics (referred to as “borehole physicals") of the borehole 101 at corresponding depth values d. For example, sensor si may be a caliper configured to generate measurements of a diameter of the borehole 101 at a corresponding depth. In embodiments using a standard caliper, and where the generation of the first measurement data m ₁ occurs during an in-run movement of the MD 104 within the borehole 101, the controller 120 may selectively set the activation state of the caliper to disabled to prevent damage to the caliper from the downward direction of movement of the instrument relative to the inner surfaces of the borehole 101.

[199] In some implementations, the geological measurements g ^sl of the borehole physicals are processed to determine ROIs that correspond to areas of physical significance of the formation 107. For example, the evaluation module 213 may be configured to process diameter values of the geological measurements g ^sl generated by a caliper to detect changes in the structural integrity of the formation 107 as a function of depth. An ROI may be determined in response to the detection of an increase in the measured diameter of the borehole 101, relative to a specified threshold T _norm , within a corresponding depth interval (i.e., indicating that the borehole 101 may be prone to collapse or fragility in this region).

[200] The evaluation module 213 may also be configured to process imaging data generated by the sensors 132 to identify depth intervals, and to determine corresponding ROIs, where physical degradation of the formation 107 has occurred, or is likely to occur. For example, edge detection and/or other image processing algorithms may be executed by the controller 120 to extract features that identify relevant artifacts in the images of the formation 107 (i.e., cracks or fissures).

[201] In some embodiments, geological data, as generated by a caliper or other sensor or instrument, is processed by the evaluation module 213 to determine ROIs that represent void regions for which all corresponding measurement data obtained by the MD 104 is untrustworthy or at least subject to uncertainty. For example, the controller 120 may be configured to process generated borehole biophysicals using the ROI model A _norm that specifies a void threshold T _void or interval to identify regions of the formation 107 with particularly significant physical degradation. Measurement data generated in void ROIs may be processed differently to other data when determining a second operational mode, generating the second measurement data, and/or logging the borehole 101. For example, on the determination of any void ROIs the controller 120 may flag the borehole 101 for re-logging irrespective of the generation of the second measurement data or other data.

[202] It will be appreciated that the MD 104 is not limited to the use of one sensor and/or instrument to generate measurement data m ₁ or m ₂ . Any number of sensors, components, or instruments 132 may be utilized to generate measurement data m ₁ or m ₂ including, for example, combinations of electromagnetic sensors, such as gamma sensors or electrical and/or magnetic conductivity sensors, thermal sensors, imaging devices, and physical instruments. The data generated by individual sensors mentioned in the examples above may be combined with one or more other measurements of borehole 101, and/or other data, to generate the measurement data m ₁ or m ₂ .

Determining a second operational mode

[203] With reference to Fig. 4a, at step 406 the controller 120 processes the borehole evaluation data, as generated in step 404, to modify the initial operational mode of the MD 104 (i.e., mode 0 ₁ used to produce first measurement data m ₁ during the in-run).

In the examples described herein, the borehole evaluation data is ROI data generated by evaluation module 213. The ROI data includes an indication of the position of each determined region (e.g., for ROI ₁ . depth interval [d _1; d ₂ J) and optionally a geological classification of the formation in the region (e.g., ‘coal’).

[204] Fig. 7 illustrates an exemplary process by which initial operational mode O is modified by the controller 120, in response to the ROI data, to generate a new operational mode (a second mode) O ₂ . The modified (second) operational mode enables the MD 104 to generate second measurement data m ₂ during an out-run (or other second) movement of the MD 104.

[205] At step 702, the mode generator module 214 receives the borehole ROI data, from the evaluation module 213 and/or data store 208, and selects a determined ROI R. The mode generator module 214 determines one or more operational parameter values defining the new operational mode to enable the MD 104 to measure the borehole 101 at the selected ROI R. At step 704, the mode generator module 214determines a desired movement speed of the MD 104 through the region R.

[206] At step 706, the mode generator module 214 determines a desired activation state of one or more of the sensors 132. For example, the mode generator module 214 selects the sensors to be used for generating the corresponding geological measurement data m ₂ as the MD 104 passes through the region R during the second movement. The mode generator module 214 then flags only the selected sensors to have an ‘active’ state in the second operational mode. [207] In some examples, the mode generator module 214 determines the selected sensors dynamically based on the characteristics of the region R. For example, the mode generator module 214 may process a geological classification of the formation in the region R (e.g., ‘coal') to select at least one sensorthat is designated to characterize the associated material or rock in the region.

[208] Characterizing a zone or region may include, for example, activating the designated sensor(s) to generate data for determining the Elastic Rock Properties (ERP) of the formation in the zone/region, as part of the second measurement data m ₂ . This enables an ERP classification to be determined for region R based on the prior determination of the geological classification of the region (i.e., by identifying that the region contains rock of a particular type).

[209] At step 708, the mode generator module 214 generates a new operational mode by modifying the initial operational mode O ₁ of the MD 104. The module 214 generates adjustment data representing an adjustment to the set of speeds and/or the activation state of one or more geological sensors, of the initial mode O to achieve the desired movement speed and activation state(s) (as determined in the preceding steps). In the described embodiments, the mode generator module 214 applies the adjustment data to the operational parameter values of initial mode O to generate the second mode O ₂ . This is advantageous in that the operation of the MD 104 may be preserved from the initial mode in the regions of non-interest.

[210] In other embodiments, the mode generation module 214 may be configured to generate one or more operational parameter values for the modified (second) operational mode O ₂ without reference to or knowledge of parameters of the first mode O ₁ . For example, the mode generation module 214 may use a determined geological classification of a ROI R to directly set the speed and sensor state of the MD 104 for obtaining the second measurement data over depths corresponding to R (i.e., irrespective of the corresponding speeds and sensor activations specified by O ₁ ). [211] In some embodiments, the determination of a subset of the operational parameter values of mode O ₂ is dependent on the selection of values for a different subset of parameters. For example, the mode generator module 214 may be configured to determine a speed of the MD 104 in determined region R based, at least in part on, the one or more geological sensors that are to be activated for generating the second measurement data m ₂ over the same region.

[212] At step 710, the mode generator module 214 parses the ROI data to select the next ROI for which to determine the second operational mode parameters. The steps 704-708 are repeated for each determined ROI to define the second operational mode O ₂ for the second movement. In the described examples, the second movement is an out-run movement of the MD 104 in the determined regions. In some embodiments, the mode generator module 214 may be configured to additionally adjust, or otherwise determine, the operation of the MD 104 for the out-run movement outside in the determined regions. For example, a different speed of movement and/or a different combination of activated sensors may be defined by the second operational mode for the regions of non-interest during the out-run.

[213] At steps 712 and 714, the controller 120 transmits data indicating the second operational mode O ₂ to the logging apparatus 110. In the described embodiments, the mode generator module 214 encapsulates the parameter values of mode O ₂ as configuration data, generates a control signal including the configuration data, and transmits the control signal to the logging apparatus 110 via I/O interface 204.

Generating second measurement data

[214] Fig. 4c illustrates an example of the generation of second measurement data m ₂ resulting from the MD 104 operating in operational mode O ₂ during the second movement (e.g., an out-run). In response to receiving the control signal, the signal processing device 112 extracts the set of speed values and corresponding depth intervals. The signal processing device 112 causes the commencement of the second movement of the MD 104 by instructing the deployment mechanism 114 to raise the MD 104 from its position A’ (at depth d ₅ = 40m) toward the collar of the borehole 101 (position A at depth d ₀ = 0m). The speed of the movement of the MD 104 is controlled according to the speed values and corresponding depth intervals of mode O ₂ .

[215] In the depicted example, the MD 104 operates to conduct a geological survey to profde deposits 107a, 107b in determined regions ROI ₄ and ROI ₂ using a sensor s2 moving at a speed of 1 m/minute through the borehole 101. Outside of the determined ROIs the MD 104 moves at the initial mode speed of 10 m/minute.

[216] Mode O ₂ also defines depth-specific sensor activation states in the described example. Depth intervals spanning the length of the borehole 101 are shown as D ₄ = [d ₀ , d D ₂ = [d ₄ , d ₂ ] D ₃ = [d ₂ , d ₃ ] D ₄ = [d ₃ , d ₄ ] and D ₅ = [d ₄ , d ₅ ] . Regions of interest ROI ₄ and ROI ₂ correspond to intervals D ₂ and D ₄ .

[217] In one implementation, MD 104 is configured with a total Gamma sensor si having activation states {0,0, 0,0,0} for respective intervals D ₄ -D ₅ indicating that the total Gamma sensor is deactivated for the entirety of the out-run. Sensor s2 is a spectral gamma sensor with activation states {0,1, 0,1,0} for the same depths, such that sensor s2 is switched on only when the MD 104 is within each ROI.

[218] Other implementations may involve the use of different sensors, or sensor combinations, to generate the second measurement data m ₂ over the out-run of the MD 104. The sensors used to generate the second measurement data m ₂ may be the same or distinct from those used to generate the first measurement data m ₁ .

[219] For example, in implementations where a temperature sensor is used to generate the first measurement data m ₄ , mode O ₂ may include activation states such that sensor si remains activated, and therefore generates geological data, during the out-run for at least the identified ROIs. However, the mode O ₂ may set the speed of the MD 104 to a relatively lower speed in particular determined ROIs (e.g., regions where the borehole 101 was determined to be “hot” from the temperature measurements of the first data m ₁ ) to improve the accuracy of the temperature values obtained in the second measurement data m ₂ .

[220] As shown in the example depicted in Fig. 4c, the geological measurement functionality of the MD 104 is adapted to generate second measurement data m ₂ for example using measurements g ^s2 of sensor s2 taken only at depths within ROI and ROI ₂ . Furthermore, the speed of the device 104 through the borehole 101 is varied from the initial operating speed v in the areas of non-interest to an alternative speed in the determined ROIs. This is advantageous in that the MD 104 is able to move faster through the regions of non-interest, while slowing down in the ROIs. This assists with obtaining, for example, spectral gamma or temperature measurements of increased accuracy while minimizing the time taken by the MD 104 to move through the borehole 101, and thereby improving the efficiency of the logging process by improving or at least maintaining the same logging times.

Logging the borehole

[221] With reference to Fig. 4a, at step 408 the controller 120 logs the borehole 101. Logging the borehole 101 involves using the MD 104 operating in at least the second operational mode to enable the generation, processing, and/or storage of second measurement data m ₂ . For example, in some embodiments, the MD 104 is configured to transmit the second measurement data to the controller 120 as a live data steam, where the MD controller 130 processes and sends the geological measurement values (and optionally corresponding depth values) to the I/O interface 124 via wireline 119 in real-time, or substantially real-time, with the generation of the values during the outrun movement.

[222] In other embodiments, the MD controller 130 is configured to store the measurement data values within the local memory system, such as within a cache, buffer or other dedicated storage element, as the values are generated during the movement, and to subsequently transmit all measurement data (e.g., the entire series of geological measurements and depths) to the controller 120 on completion of the out-run movement.

[223] Logging of the borehole 101 by the controller 120 involves the generation of a complete set of geological measurements of the borehole 101 over the logging path (i.e., from collar position A to end position A’ in the examples discussed). In some embodiments, the controller 120 generates logging data 211 for borehole 101 directly from the second measurement data m ₂ . In some applications the second measurement data is incomplete over the path, such as in the example illustrated by Fig. 4c in which geological values g ^s2 are only generated in ROf and ROI ₂ . In such application, the second measurement data may be supplemented, combined, collated or otherwise processed with additional data (e.g., geological data g ^sl from the first measurement data m ₁ ).

[224] In some embodiments, the logging data 212 for borehole 101 further includes other data, such as part or all of the first measurement data and/or data indicating one or more ROIs or other features determined from the first measurement data . Logging data generated by a measurement device, such as MD 104, may therefore be referred to as a “set” of constituent measurement, geological, and/or other data sets. In some embodiments, the controller 120 is configured to store the logging data 211 in the data store 208. Further processing may be performed on the logging data 211 such as for example to identify and correct errors in the geological and/or depth values, or to perform other data processing operations (e.g., scaling, normalization, smoothing, etc.) to facilitate improved logging of borehole 101.

Using external data for improved logging

[225] In some configurations of the borehole logging platform 100, such as configurations used to perform logging on a mine site, the controller 120 is configured to utilize external data (i.e., data obtained from a source external to the controller 120 and the MD 104) to improve logging of the borehole 101. Fig. 8a illustrates an example process 800 for using external data to enhance the borehole logging process 400. [226] At step 802, the controller 120 retrieves the external data from an external data source. The external data source may include, but is not limited to, a remote or local computing system, storage device, or other computer readable media configured to store external data including: bench profile data, geological reference data, and/or other data (e.g., models, configuration or utility information etc). In some embodiments, the external data is retrieved by the controller 120 prior to the commencement of a logging activity performed by the MD 104 on the borehole 101.

[227] At step 804, the controller 120 processes the external data to generate parameters for enhancing the logging process 400. Processing the external data may include, but is not limited to, applying pre-processing, compensation, transformation or adaptation operations to one or more data values; extracting, separating, reducing or interpolating features and/or individual samples from one or more data values; and otherwise adjusting, transforming or one or more data values. The controller 120, or MD 104, may process the external data according to a form or type of the external data and the intended use of the parameters.

[228] The controller 120 uses the parameters determined from processing the external data to improve the generation and/or evaluation of the first measurement data m ₁ . In some embodiments, the controller 120 configures the MD 104 with an initial (first) operational mode enabling logging of the borehole 101 to commence according to process 400 (i.e. at step 401). Alternatively, or additionally, the controller 120 further uses the parameters to evaluate or perform analysis of the first measurement data (i.e., at step 404).

[229] In some embodiments, the external data is retrieved dynamically by the controller 120 during the logging. This approach is shown in the dashed lines of Fig. 8a. For example, in response to evaluating first measurement data generated by the MD 104 to determine one or more ROIs (i.e., at step 404), the controller 120 may query the external data source to retrieve new or further data associated with the borehole 101. [230] For example, the controller 120 may determine a ROI corresponding to a coal deposit in response to processing the first measurement data m ₁ obtained from moving the MD 104 within the borehole 101 (e.g., during an in-run pass). The controller 120 may then retrieve reference data specific to coal deposits from an external database (e.g. as maintained by reference system 165), and process the reference data in combination with the first measurement data. At step 406, the controller may then determine or adjust the operation of the MD 104 during a subsequent movement (e.g., during an out-run pass) to improve the generation of the second measurement data m ₂ .

[231] Alternatively, or additionally, at step 809 the controller 120 uses the parameters obtained by processing the external data to evaluate and/or refine the second measurement data m ₂ , as generated by the MD 104 at step 406. For example, the controller 120 may use the parameters of the external data to post-process one or more geological data values of the second data m ₂ (e.g., to remove or reduce noise in the data samples based on expected values or distributions indicated by the external data parameters). In some embodiments, the controller 120 performs region-specific processing of samples of the second measurement data m ₂ . For example, the controller 120 may refine the second measurement data m ₂ by replacing or interpolating geological data values generated in void ROIs based on the parameters determined from the external data. The controller 120 then uses the refined second measurement data m ₂ to log the borehole 101 at step 408.

[232] Fig. 8b illustrates an example of the process 800 in which the platform 100 utilizes external data in the form of bench profile data representing a profile of a bench comprised of a plurality ofboreholes including borehole 101.

[233] At step 802, the profile data is retrieved by the controller 120 from the BMS 160. The profile data represents characteristics of a plurality ofboreholes formed over the bench (the “bench set”). The profile data may include, but is not limited to: logged data of one or more of the boreholes of the bench set; hole pattern data of the bench; MWD data obtained by drill rig 141 as a result of drilling one or more holes of the bench set; and geo-analysis, structural geology, or similar data indicating one or more properties of the formation 107 of the bench.

[234] In some embodiments, the controller 120 is configured to receive the profile data and, at step 804, to extract a subset of the profile data representing characteristics of one or more other boreholes that are associated with the borehole 101 over the bench (referred to as “associated boreholes” herein). In other embodiments, the controller 120 retrieves only the profile data of the associated boreholes from the BMS 160, such as, for example, in response to a query submitted by the controller 120 including an identifier of the borehole 101 to be, or presently being, logged.

[235] The associated boreholes are selected by the controller 120, or the BMS 160, relative to the borehole 101. In some embodiments, the associated boreholes are a subset of the bench set including one or more boreholes that are spatially adjacent to the borehole 101 (“adjacent boreholes”). A bench analysis system 161 of the BMS 160 may determine the adjacent boreholes as the one or more boreholes of the bench with a collar distance equal to or less than a pre-determined threshold from borehole 101. The collar distance is measured as the linear distance over surface 109 of the bench between the collar position of borehole 101 (i.e., position A) and the corresponding collar position of the adjacent borehole. Profde data corresponding to the determined adjacent boreholes is transmitted to the controller 120 (e.g., as a set of table entries). The distance value determining the adjacent boreholes may be configured or altered by a user of the BMS 160 enabling variation in the number of other boreholes in the bench that are considered as adj acent to the present borehole 101.

[236] Although the embodiments discussed below identify associated boreholes based on adjacency to borehole 101, other implementations of the controller 120 and/or BMS 160 may utilize any arbitrary criteria to determine the associated boreholes of the bench profile, relative to borehole 101. In other embodiments, the associated boreholes may be selected from the bench set as the boreholes that have one or more equal or similar geophysical properties to the borehole 101, but that are not necessarily adjacent to borehole 101. For example, it may be desirable to select, as an associated borehole, a borehole that is located farther from borehole 101 than the threshold distance value, but that has one or more other common characteristics or properties to borehole 101 (e.g., a similar borehole length).

[237] The controller 120 is configured to process profile data 212, including at least the adjacent borehole data, to generate one or more of: (1) device configuration data determining, at least, an operational mode of the MD (i.e., at step 806); and (2) evaluation criteria data determining the generation of the borehole evaluation data (i.e., at step 808).

Determining an operational mode using profile data

[238] At step 806, the controller 120 generates device configuration data to specify the initial operational mode O used by the MD 104 to generate the first measurement data m ₁ . Controller 120 is configured to invoke the mode generator module 214 prior to the commencement of the logging process to determine one or more operational parameters of the initial operational mode O .

[239] The mode generator module 214 may process the adjacent borehole data and set the state of one or more sensors 132, and/or one or more speeds of the MD 104, for the in-run movement. For example, mode generator module 214 may retrieve the logging data of an adjacent borehole, and in response to the logging data indicating the presence of for example a coal deposit in the formation 107, the mode generator module 214 may set sensor si, e.g., a total Gamma sensor, to the ‘enabled’ activation state. This results in the MD 104 producing the first measurement data including g ^sl (e.g., based on the ability of total Gamma-sensing to detect coal). A speed value may be set for the MD 104 during the in-run movement according to the logging data of an adjacent borehole and/or the state of one or more sensors 132 (e.g., constant speed v set to 10 m/minute to enable accurate total Gamma measurements).

[240] In some embodiments, the device configuration data determines the initial operational mode O based at least in part on the ROIs determined for the adjacent boreholes. For example, mode generator module 214 may extract ROI data from the adjacent borehole data to determine ROIs identified by prior logging processes performed on the adjacent boreholes. The mode generator module 214 processes the depth intervals of the corresponding determined ROIs of the adjacent boreholes and determines one or more expected or predicted ROIs for borehole 101. For example, depth values specifying a ROI in adjacent borehole 101a may be used directly as corresponding depth values for borehole 101. In other cases, a conversion or translation process may be applied to the adjacent borehole depths to produce the corresponding depth values for borehole 101 (e.g., where the ground-level of borehole 101 is offset from that of the adjacent borehole).

[241] Operational parameters of the initial mode O may be set, by mode generator module 214, based on the set of expected or predicted ROIs. For example, the mode generator 214 may set the activation state of sensor si to ‘enabled’ during the in-run movement only at depths where the MD 104 that is within an expected ROI for borehole 101. One or more corresponding variations in the speed of the MD 104 may also be set by generator 214 during mode O ₁₅ such as to increase the speed when MD 104 is not within an expected ROI (and therefore not recording measurements from sensor si). This further improves the efficiency of the logging process for borehole 101 by leveraging pre-determined logging data of the adjacent holes to reduce the time taken to generate the initial in-run data at least by predicting or extrapolating the particular regions within the borehole that need to be logged.

[242] In some embodiments, the generated device configuration data may specify the second operational mode O ₂ used by the MD 104 to generate the second measurement data m ₂ . The mode generator module 214 is configured to generate parameter values of the second operational mode O ₂ by processing the adjacent borehole data in conjunction with, or as an alternative to, the initial in-run measurement data m ₁ collected by MD 104 for borehole 101. The exemplary functionality performed by the mode generator 214 to set sensor activation states and/or speeds of the MD 104 during the in-run movement to generate the first measurement data may be analogously performed to dynamically adapt the generation of the second measurement data m ₂ according to the profde (i.e., adjacent borehole) data.

[243] The device configuration data generated to specify the second operational mode O ₂ may include adjustment data for modifying the initial operational mode OiOf the MD 104. Alternatively, the mode generator module 214 may generate absolute values of the parameters directly such that O ₂ is specified independently and without reference to the initial mode O ₁ .

[244] For example, the mode generator module 214 may compare geological measurement values logged for one or more adjacent boreholes to the corresponding measurements obtained from in-run measurement data of borehole 101. In response to the comparison, the mode generator module 214 may determine adjustment data to modify the operational parameters of the initial mode Oifor example to control the speed of the MD 104 and/or set the state of one or more sensors to generate measurements, either constantly or for a particular depth interval set, during the out-run movement. The adjustment is performed irrespective of the borehole evaluation data generated based on the first measurement data m ₁ collected for borehole 101.

[245] In another example, the data QA/QC module 216 may perform a comparison between the geological values logged for one or more adjacent boreholes and the geological values g of measurement data assess the quality of the in-run data collected for borehole 101. In response to the comparison, the data QA/QC module 216 may flag some or all of the geological measurements g of as erroneous and subsequently perform one or more operations to characterize the nature of the erroneous values of g.

[246] For example, the QA/QC module 216 may invoke the MD calibration module 215 to execute a calibration routine to cause the MD 104 to check the operational state of its components. The MD 104 is configured to provide an indication of the outcome of the calibration to the MD calibration module 215. In some embodiments, the QA/QC module 216 is configured to perform one or more corrective actions based on the calibration outcome (as received from calibration module 215). For example, the QA/QC module 216 may indicate that re-acquisition of the first measurement data is required. The evaluation module 213, mode generator module 214, QA/QC module 216, and MD calibration module 215 thereby collectively operate to perform the dynamic collection and assessment of logging data, and to enable subsequent QA/QC operations to characterize a source of identified errors in the measured data (e.g., via equipment self-diagnosis), as part of an autonomous logging platform.

Evaluating measurement data using profile data

[247] At step 808, the controller 120 invokes the evaluation module 213 to generate the evaluation criteria data based at least in part on the adjacent borehole data. The evaluation criteria data controls the generation of the borehole evaluation data, representing m ₁ (i.e., according to step 404), by, for example, determining the parameters and/or functions invoked by the evaluation 213, data QA/QC 216 and/or MD calibration 215 modules depending on the configuration of the controller 120.

[248] In some embodiments, the controller 120 is configured to perform a ROI analysis of measurement data according to process 502 of Fig. 6. The controller 120 processes the adjacent borehole data and determines evaluation criteria data to be used by the evaluation module 213 in the ROI analysis of m ₁ . For example, the evaluation criteria data may include an indication of one or more ROI models for use in the comparison of the geological data values at step 604. This includes the use, by evaluation module 213, of a particular abnormality model A _norm , and/or geological material specific models (e.g., A _coa( ), to determine ROIs for borehole 101. This is advantageous in that the controller 120 is configured to utilize knowledge of the formation 107 gained from the bench profile data (e.g., logging data for the adjacent boreholes 101a, 101b) to improve the measurements obtained during the out-run measurement, and thereby enhance the accuracy of the logging data subsequently generated for borehole 101. [249] In other embodiments, the controller 120 is configured to generate evaluation criteria data that determines or otherwise influences the generation of the borehole evaluation data for various data QA/QC and/or calibration analysis operations performed on the first measurement data mj. For example, the evaluation criteria data may set tolerances or bounds for an amount of error or deviation in the geological measurement values of the data, relative to the values obtained from one or more adjacent boreholes.

[250] In some embodiments, data QA/QC module 216 identifies one or more anomalous data values according to the specified tolerances, and generates borehole evaluation data including an indication of the quality of the first measurement data from the detected one or more anomalous data values. Data QA/QC module 216 subsequently processes the borehole evaluation data to determine whether a re-logging operation is required (as described below). This enables the controller 120 to control for the quality of measurements obtained during the logging process based on predetermined knowledge of the geology of the formation 107.

[251] In some embodiments, the controller 120 is configured to selectively log the borehole 101 in response to the profile data and/or the adjacent borehole data. For example, the profile data may include a set of expected or predetermined parameters of the geology of the bench, such as to represent a geological profile, or in some instances a 3D block model, or part thereof, for the mine site. The geological profile model data may be stored in data store 208 and pre-loaded by processor 202. Alternatively, the geological profile model may be transmitted to the controller 120 by the BMS 160, either prior to the logging operation, or in real-time, or substantially real-time, in the form of a live data stream delivered to the processor 202 via communications module 206.

[252] In some embodiments, controller 120 performs model validation functions to conduct logging of boreholes across the bench in order to validate the geological profile model of the profile data. In one validation operation, logging of the borehole 101 is performed selectively where the second measurement data m ₂ is obtained only if the borehole evaluation data indicates a deviation between first measurement data m and corresponding measurements and/or properties of the geological profile.

[253] In one example, if data QA/QC module 215 determines that data set m ₁ is consistent with the geological profile then the out-run measurement process can be selectively bypassed, and borehole 101 need not be logged. In this case, the mode generator 214 may then set the second operational mode O ₂ to facilitate efficient extraction of the MD 104 from the borehole 101 (e.g., by setting an out-run speed of the MD 104 to a maximum value, with the sensor activation state of all sensors set to disabled). This process enables the selective generation of logging measurements only where such measurements would result in increased quality and/or granularity over the data that is already known in the context of the geological profile model as the comparison dataset. The selective logging of borehole 101 in this manner thereby improves the efficiency of the logging platform particularly when the controller 120 is autonomously operated.

Performing multiple loggings of a borehole

[254] The logging platform 100 may be configured to perform multiple loggings of a borehole 101 such as to collect a number of different logging data sets, and to thereby enable a more detailed analysis of the respective formation(s) 107. The process of obtaining multiple logging data sets for a single borehole, after an initial logging of the hole is performed, is referred to as re-logging the borehole.

[255] Fig. 9 illustrates an example process 900 for re-logging the borehole 101. The conditions for initiating the re-logging process 900 on the borehole 101 may vary depending on the configuration of the logging platform 100. In one embodiment process 900 is performed by the BMS 160. At step 902, the BMS 160 obtains a set of initial logging data from a controller 120 of a MD 104 used to log the borehole 101. The logging of the borehole 101 using MD 104 may have proceeded, for example, according to processes 400 or 800. [256] At step 904, the BMS 160 processes the set of initial logging data to determine whether the borehole 101 is to be re-logged (i.e., at step 906). For example, the BMS 160 may process the second measurement data m ₂ of the initial logging data to determine any anomalies or deviations in, or other unexpected characteristics of, in the geological values recorded for the borehole 101. In some embodiments, the BMS 160 processes data associated with the ROIs identified for the borehole 101 (e.g., ROI model data, the existence of void ROIs, etc.), or with any calibration or QC/QA operations performed on the corresponding measurement data by the controller 120 (e.g., geological values of second measurement data m ₂ ) to determine whether to re-log the borehole 101. For example, the BMS 160 may positively determine to re-log the borehole 101 if there are at least a predetermined number of void ROIs determined, and/or if the total depth covered by the determined void regions exceeds a threshold length.

[257] In response to determining that borehole 101 is to be re-logged, at step 908 the BMS 160 selects a measurement device to perform the re-logging. The re-logging may be performed by an measurement device that is the same as, or distinct from, the MD 104 that was used to generate the initial set of logging data. In some embodiments, the logging platform 100 comprises a plurality of measurement devices including one or more devices additional to the MD 104 used to generate the initial logging data. The BMS 160 is configured to select the measurement device to perform the re-logging (referred to as the “selected MD”) based on one or more of:

• a set of device characteristics of the one more measurement devices of the platform 100, such as the corresponding physical properties and/or the measurement capabilities of each measurement device (e.g., the dimensions, weight, or other properties of the measurement device, and the types of sensors deployed in the device); and a workflow state of the one or more measurement devices of the platform 100, such as for example whether the measurement device is currently ‘busy’ or ‘idle’ based on the logging tasks currently assigned to the measurement device, the position of each device on the mine site in relation to the collar position of the borehole 101, and/or any other information relevant to conducting logging with the device on the site.

[258] At step 912, BMS 160 instructs a re-logging activity on borehole 101 with the selected MD by providing control signals to the corresponding controller 120 of the selected MD. In some embodiments, the control signals enable the corresponding controller 120 to set an operational mode of the selected MD 104 (i.e., at step 910). The operational mode set by the corresponding controller 120 may be a first operational mode used by the selected MD to perform the re-logging according to the process 400 described herein. The corresponding controller 120 executes the re-logging activity to obtain a second logging data set for the borehole 101. Re-logging process 900 may be repeated using the second logging data set to initiate the (further) re-logging of borehole 101, with any subsequent iterations performed likewise.

[259] In some embodiments, the measurement device utilized for a given re-logging of the borehole 101 is selected from the plurality of MDs of the platform based on the processing of the first or any other prior generated logging data, and/or external data, to enable a more detailed analysis of the borehole 101. Alternatively, or in addition, arbitrary re-logging of the borehole 101 may be performed by the same measurement device performing the first (or other prior) logging activity (i.e., in the case of relogging the same borehole 101 multiple times).

[260] For example, a first MD 104a may be fitted with only gamma and magnetic conductivity sensors, a second MD 104b may be fitted with only a temperature sensor, and a third MD 104c may be fitted only with physical sensing instruments (e.g., a caliper). In some configurations, the BMS 160 performs multiple logging of the borehole 101 with each of the first, second and third MDs 104a, 104b, 104c to achieve measurement of the interior formation 107 by a combination of the different sensors 132 deployed in the same. [261] In some embodiments, one or more steps of the process 900 are conducted by the controller 120 of the MD 104 used to generate the initial logging data. Following step 408 of logging process 400, at steps 902 and 904 the controller 120 receives and processes the initial logging data to determine whether to re-log borehole 101. In some configurations, in response to a decision to perform re-logging (i.e., at step 906) the controller 120 operates the MD 104 to perform a re-logging of the borehole 101. The controller 120 optionally sets or adjusts the operational mode of the MD 104 for the relogging relative to an operational mode used in the initial logging. This allows (re- jlogging data to be collected from the same MD 104 providing the initial logging data but with an operational mode/profile that is customized by the controller 120 based on the post-processing of the initial log.

[262] In some embodiments, the controller 120 selects a different measurement device to perform the re-logging (i.e., at step 908). For example, controller 120 may be configured to exchange data with corresponding controllers of one or more other measurement devices of the platform 100 to: select a measurement device to re-log the borehole, and provide re-logging information to the selected measurement device. The re-logging information may include one or more of: previous logging data generated for the borehole 101 (e.g., the initial logging data); and data indicating one or more operational parameters specifying a suggested operational mode of the selected measurement device. Re-logging of the borehole 101 is subsequently performed by a corresponding controller of the selected measurement device.

[263] In some embodiments, the sets of logging data, as generated by multiple logging activities conducted on the borehole 101, are processed by a controller 120, bench management system 160, or other system, to perform logging validation, correction, collation or other operations. For example, individual sets of logging data generated by corresponding MDs may be cross-correlated with one or more other sets of logging data as determined based on the one or more MDs that performed logging on the borehole 101, the operational mode(s) of the MD(s), and/or any other relevant criteria. [264] Validation may involve determining one or more accuracy values of the measurement data comprising the set of logging data, either based on cross-correlation or any other heuristic or analytical technique. For example, an accuracy value in the form of a confidence score may be calculated based on the degree to which measurement data of a particular logging data set deviates from corresponding data in other logging data sets. Alternatively, or additionally, a confidence score may be calculated by comparing measurement data of the logging data set(s) to one or more expected values (e.g., as determined by an existing data model).

[265] In some embodiments, the controller 120, bench management system 160, or other system, is configured to process the accuracy measures to classify or label the borehole 101. For example, in response to determining a relatively low confidence score from one or more sets of logging data, the controller 120 may flag borehole 101 for further investigation (e.g., additional re-logging and/or other evaluation).

[266] It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.