BACKGROUND

Embodiments of the present disclosure relate to a method of predicting microseismic behavior, the method being of the type that, for example, employs physical analysis techniques. Embodiments of the present disclosure also relate to a multi-stage stimulation process microseismicity predication apparatus of the type that, for example, comprises a rules engine and a knowledge base in order to predict microseismic behavior. In some embodiments of the present disclosure microseismic analysis is in hydrocarbon exploration, hydrocarbon drilling, hydrocarbon production, hydrocarbon management and/or the like.

In the field of hydraulic fracturing, it has become increasingly common to develop an oil and/or gas reservoir, particularly one classed as an unconventional oil and gas reservoir, by drilling several wells in close proximity. Typically, each well has a section located within hydrocarbon bearing formation(s), which is termed the lateral section, which is stimulated along a significant proportion of its length using multiple hydraulic fractures. The operation of stimulation of the geological formation(s) surrounding a single predetermined length of the wellbore results in the creation of a "fracture stage". Often, the wells are stimulated using a "zipper frac" methodology in which fracture stages are interdigitated between two or more of the wellbores. As intimated above, hydraulic fracturing involves the injection of a fluid, or a mixture of fluid and solid particles, the latter being known as proppant, into the surrounding geological formation via a wellbore with the intention of initiating fracture(s). During such fracturing, elastic waves are produced as the material in the path of fracture propagation, and surrounding the fracture, fails. Such seismic events are detected and are referred to as microseismic events due to the low magnitude of sound typically emitted.

Hydraulic fracture monitoring is employed in underground oil and gas wellbores in order to provide an understanding of the geometry of the hydraulic fractures so as to enable: optimize well spacing, better completion and stimulation design, reliable production predictions, real-time operational decisions during the treatment or stimulation of the wellbore itself and/or the like. For each microseismic event, seismic traces are recorded, which include both the longitudinal and transverse waves travelling through the formation. These traces are recorded at a number of locations, typically using one or more lines of receivers that may be disposed in one or more monitoring wells or at the surface. From these traces, it is possible to locate at least the origin of the microseismic event in space and time, and possibly also, depending on the geometry of the receivers, to determine the source characteristics of the microseismic event.

In relation to monitoring the microseismic events generated, a number of techniques are known that use the determined locations of microseismic events in order to make operational decisions. Such decisions include the cessation of pumping in order to avoid excessive fracture height growth or avoidance of induced seismicity, amendment to the number of planned fracture stages, fracture placement, reduced/increased pumping and/or the like.

Additionally, it is known to assess sometimes the likely locations and types of microseismic events prior to stimulation in order to obtain a first fracture stage using formation and fault properties and the stress state of the formation, as well as regular seismic data. Similarly, in respect of analysis after creation of the first fracture stage, it is known to interpret microseismic events in order to derive spatio-temporal evolution of source characteristics, derive geometrical features from microseismic event "clouds", such as pre-existing faults and newly created fracture conduits, characterize the nature of microseismic event clusters, refining or calibrating properties of some or all of a known set of geometrical features, and quantifying the perturbations to the stress state due to the appearance of, and/or change of state of, some or all of the set of geometrical features that are independently identified using microseismic data. However, despite a comprehensive suite of analysis methodologies being available to analysts, the prediction and control of the fracturing process is not always well understood as a result of a lack of appreciation at a high level of how fractures form and/or develop in the presence of the formation of other fractures. Additionally, inherent uncertainties exist in relation to material, geometrical properties and stress state of the formation and pre-existing faults, pore pressure distribution, flow properties such as porosity and permeability, fluid properties, such as viscosity and density, and the proportions of the different fluid and gasses that are present, as measured by the gas, water and oil saturations.

SUMMARY

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

According to a first aspect of embodiments of the present disclosure, there is provided a method of predicting microseismic behavior in a multi-stage stimulation process in respect of a geological formation. The method comprises identifying a primary factor contributing to a type and pattern of observed microseismicity that is associated with a stimulation stage. To identify the primary factor a candidate factor contributing to the type and pattern of the observed microseismicity associated with the stimulation stage is postulated. A physical analysis technique is used to test the candidacy of the postulated candidate factor by analyzing the contribution of the postulated candidate factor to the type and pattern of the observed microseismicity.

Results of the prediction analysis may be displayed or sent to a processor controlling the stimulation process. Processing may be iterative with respect to a number of different postulated candidate factors and a most likely postulated candidate factor may be detremined from the iterative process.

The analysis technique may be a geophysical analysis technique.

The method may further comprise: repeating the identification of the primary factor in respect of another candidate factor; and generating a set of primary factors determined to contribute to the type and pattern of the observed microseismicity. The method may further comprise: determining a sequence of the primary factors of the set of primary factors.

The method may further comprise: identifying a spatio-temporal pattern change in the observed microseismicity associated with the stimulation stage. The method may further comprise: accessing pre-execution data associated with the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the pre-execution data.

The method may further comprise: accessing post-execution data and/or execution parameter data associated with execution of the stimulation stage of the multistage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the post-execution data and/or the execution parameter data.

The post-execution data may comprise microseismic event data corresponding to observed microseismicity responsive to the stimulation stage.

The method may further comprise: accessing execution parameter data associated with execution of the stimulation stage of the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the execution parameter data. The execution parameter data may comprises at least one of: a pump pressure; a pumping rate; and/or a pumping duration.

The method may further comprise: accessing formation type identification data associated with execution of the stimulation stage of the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the formation type identification data.

The postulation of the candidate factor contributing to the type and pattern of the observed microseismicity may comprise: determining the nature of a change to the observed microseismicity.

The determining whether the candidate factor contributes to the type and pattern of the observed microseismicity comprises: determining a position of the candidate factor in a sequence of influence of the primary factors.

The method may further comprise: determining in respect of the fracture stage a variation in at least one of: an event pattern; a rate; and/or a source characteristic. The method may further comprise: determining a variation between fracture stages of at least one of: an event pattern; a rate of microseismic events; and/or a source characteristic.

Testing the candidacy of the candidate factor may comprise: using the physical analysis technique to quantify the effect of the candidate factor, the effect having a correlation with the observed microseismicity.

The method may further comprise: storing the primary factors identified.

The multi-stage stimulation process may be a hydraulic fracturing process.

According to a first aspect of some embodiments of the present disclosure, there is provided a method of treating a geological formation in which a wellbore extends, the method comprises the method of predicting microseismicity in a multi-stage stimulation process as set forth above in relation to the first aspect.

The method may further comprise: adjusting an engineering parameter in response to identification of the primary factor. According to a third aspect of some embodiments of the present disclosure, there is provided a multi-stage stimulation process microseismicity prediction apparatus comprising: a rules engine arranged to identify a primary factor contributing to a type and pattern of observed microseismicity associated with a stimulation stage of a geological formation, the rules engine having access to a knowledge base in order to: postulate a candidate factor contributing to the type and pattern of the observed microseismicity associated with the stimulation stage; and an analysis algorithm engine arranged to cooperate with the rules engine in order to determine whether the candidate factor contributes to the type and pattern of the expected microseismicity using a physical analysis technique. . It is thus possible to provide a method and apparatus that identifies primary factors that influence observed microseismicity, thereby enabling improved accuracy in the prediction of microseismic events and therefore improved control over a treatment of a wellbore, for example during a hydraulic fracturing treatment, such as by improved setting and modification of engineering parameters during treatment. BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings, in which:

Figure 1 is a schematic diagram of a hydrocarbon field, for example a gas field, having a formation being treated in accordance with some embodiments of the present disclosure;

Figure 2 is a schematic diagram of a fracture analysis apparatus in accordance with some embodiments of the present disclosure;

Figure 3 is a schematic diagram of an architectural stack supported by the apparatus of Figure 2;

Figure 4 is a schematic diagram of application software in accordance with some embodiments of the present disclosure;

Figure 5 is a flow diagram of a method of predicting microseismic behavior in accordance with some embodiments of the present disclosure; Figure 6 is a flow diagram of a first part of a method of determining a change in nature of microseismicity as used in the method of Figure 5 in accordance with some embodiments of the present disclosure;

Figure 7 is a flow diagram of a second part of the method of Figure 6;

Figure 8 is a chart of a distribution of microseismic events in respect of a first stage and a second stage of a treatment of a first wellbore and a second wellbore;

Figure 9 is a chart of distribution of microseismic events in respect of a third stage and a fourth stage of treatment of the first and second wellbores;

Figure 10 is a chart of distribution of microseismic events in respect of a fifth stage and a sixth stage of treatment of the first and second wellbores; Figure 11 is a chart of distribution of microseismic events in respect of a seventh, eighth stage and ninth stage of treatment of the first wellbore and a seventh stage of treatment of the second wellbore; Figure 12 is a screen shot of plots of z-component microseismic data generated by an array of geophones recording microseismic events, where the z-component is parallel to the trajectory of the first borehole;

Figures 13(a)-(c) are graphs of minimum moment magnitude versus b-value, distance versus moment magnitude, and moment magnitude versus probability;

Figure 14 is a flow diagram of a method of testing candidacy of a candidate factor employed in the method of Figure 5;

Figure 15 is a spatial plot of effective normal stress corresponding to the charts of Figures 8 to 1 1 and used with the method of Figures 5 and 14; Figures 16 to 19 are a set of screen shots generated by a candidacy testing application with the method of Figures 5 and 14; and

Figure 20 is a schematic diagram of a multi-stage stimulation process microseismicity prediction apparatus in accordance with some embodiments of the present disclosure. In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

DESCRIPTION

The ensuing description provides preferred exemplary embodiment(s) only, and is not intended to limit the scope, applicability or configuration of the invention. Rather, the ensuing description of the preferred exemplary embodiment(s) will provide those skilled in the art with an enabling description for implementing a preferred exemplary embodiment of the invention. It being understood that various changes may be made in the function and arrangement of elements without departing from the scope of the invention as set forth in the appended claims. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

Moreover, as disclosed herein, the term "storage medium" may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term "computer-readable medium" includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and various other mediums capable of storing, containing or carrying instruction(s) and/or data.

Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as storage medium. A processor(s) may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

It is to be understood that the following disclosure provides many different embodiments, or examples, for implementing different features of various embodiments. Specific examples of components and arrangements are described below to simplify the present disclosure. These are, of course, merely examples and are not intended to be limiting. In addition, the present disclosure may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. Moreover, the formation of a first feature over or on a second feature in the description that follows may include embodiments in which the first and second features are formed in direct contact, and may also include embodiments in which additional features may be formed interposing the first and second features, such that the first and second features may not be in direct contact.

Referring to Figure 1 , a hydrocarbon field 100 comprises a wellbore 102 having a vertical portion 104 and, in the example of so-called unconventional resource access, a plurality of lateral portions, for example a first lateral portion 106 and a second lateral portion 108 extending in a geological formation 1 10 known to contain hydrocarbon resources, for example gas, liquid or a combination thereof. The first and second lateral portions are disposed in spaced relation to each other.

Although reference is made herein to the lateral borehole portions leading off a lateral borehole portion, the skilled person will appreciate that the methods and apparatus described herein are borehole trajectory agnostic. In order to release the hydrocarbon resources contained in the formation 1 10, the formation 1 10 is treated by means of hydraulic stimulation in order to create hydraulic fractures. In the example of Figure 1 , a first hydraulic fracture stage 1 12 has been formed about the first lateral portion 106 and a second hydraulic fracture stage 1 14 has been formed about the second lateral portion 108. Similarly, a third hydraulic fracture stage 1 16 has been formed about the first lateral portion 106 and a fourth hydraulic fracture stage 1 18 has been formed about the second lateral portion 108. Additionally, a fifth hydraulic fracture stage 120 has been formed about the first lateral portion 106, but further stimulation is yet to occur, for example in respect of the second lateral portion 108, which would be to the left of the fourth fracture stage 1 18.

In any event, the treatment of the wellbore 102 is designed to create a so-called zipper fracture pattern between the first and second lateral portions 106, 108 by virtue of the first, third and fifth fracture stages 1 12, 1 16, 120 being longitudinally spaced along the first lateral portion 106 at predetermined locations and the second and fourth fracture stages 1 14, 1 18 being longitudinally spaced along the second longitudinal portion 108 at predetermined locations such that the first, third and fifth fracture stages 1 12, 1 16, 120 are interdigitated with the second and fourth fracture stages 1 14, 1 18. In this example, a further vertical wellbore 122 is provided in which an array of geophones are disposed in order to detect microseismic events. In this respect, each hydraulic fracture created in the formation 1 10 results in microseismicity, i.e. the generation of microseismic events. However, although the wellbore 122 has been described as vertical, the skilled person will appreciate that alternative, non-vertical, wellbore trajectories are contemplated.

Moreover, it should be appreciated that other monitoring techniques can be employed in addition to or as an alternative to the array of geophones described above. For example, geophysical measurement devices can be employed at the surface 124, in shallow boreholes and/or in the borehole 102 to be treated. The geophysical monitoring devices can be employed with a controlled, active, source located either in a shallow borehole, or in the borehole 102 and/or the further borehole 122 or on the surface 124. In these cases, the interpretation of the acquired data to characterize one or more fracture stages can use alternate data analysis techniques such as, but not limited to, the analysis of cross-well geophysical data, optionally combined with the analysis of the microseismic data. Non-geophysical monitoring techniques can also be employed using, for example, tiltmeters, which measure deformation at the surface 124, in shallow boreholes and/or in the borehole 102 and/or the further 122.

Microseismic data generated by the array of geophones is provided to a data analysis center (not shown) that serves as a data processing environment, including providing for example a processing workstation constituting a hydraulic fracture analysis apparatus 124 (Figure 2). The fracture analysis apparatus 124 is used, as described later below, to interpret the various data, including microseismic data, collected and provided to the fracture analysis apparatus 124 in order to identify primary factors attributable to the microseismicity observed and/or expected in respect of the treatment of the formation 1 10 by stimulation of the formation 1 10. Such factors may be used to understand the activities of a stimulation engineer as well as predict the nature of the fractures produced. The nature of the fractures refers, for example, to the fracture geometry and/or whether the fractures are hydraulically connected, for example where a set of fractures contain fluid that is able to move between the fractures, a set of fractures that are not hydraulically connected, and/or the location of proppant within the fractures. The nature of the fractures is derived using the pattern of microseismic events, which refers to the distribution of microseismic events in space and time, possibly in conjunction with the style of the microseismic events, which refers to the deformation associated with a microseismic event, for example a deformation that is shearing and/or opening or closing. Referring to Figure 2, it should be noted that the block diagram of the fracture analysis apparatus 124 is not inclusive of all components of the processing workstation, but is only representative of many example components. The workstation is located within a housing (not shown). The processing workstation can be, for example, a general- purpose computing apparatus, for example a Personal Computer (PC), or any other suitable computing device. The fracture analysis apparatus 124 includes, in this example, a processing resource, for example a processor 200, coupled to an input device 202 via an input device interface (not shown) and a display device, for example a display screen 204 via a display driver (also not shown). Although reference is made here to the input device 202 in the singular, the skilled person should appreciate that the input device 202 represents any number of input devices, including a keyboard device, mouse, trackball, voice input device, touch panel and/or any other known input device utilized to input information. Likewise, the display screen 204 can include any type of display screen, for example a Liquid Crystal Display (LCD). As is common with such computing apparatus, the processor 200 supports a Graphical User Interface (GUI) that operates in conjunction with the input device 202 and the display screen 204.

The processor 200 is operably coupled to and capable of receiving input data from input device 202 via a connection 206, and operatively connected to the display screen 204 and optionally to an output device 208, via respective output connections 212, to output information thereto. The output device 208 is, for example, an audible output device, such as a loudspeaker. The processor 200 is operably coupled to a memory resource 214 via internal connections 216, for example address and data buses, and is further adapted to receive/send information from/to input/output (I/O) ports 218 via connection 220. The memory resource 214 comprises, for example, a volatile memory, such as a Random Access Memory (RAM) and a non-volatile memory, for example a digital memory, such as a flash memory. A storage device, for example a hard disc drive 222, or a solid state drive is also operably coupled to the processor 200 to provide high- capacity data storage capabilities.

Turning to Figure 3, the processor 200 of the fracture analysis apparatus 124 loads an operating system 230 from the memory resource 214 and/or the hard drive 222 for execution by functional hardware components 232, which provides an environment in which application software 234 can run. The operating system 230 serves to control the functional hardware components 232 and resides between the application software 234 and the functional hardware components 232. The application software 234 provides an operational environment including the GUI 236 that supports core functions of the fracture analysis apparatus 124.

As mentioned above, the operational environment supports application software. In one example set forth herein, the performance of primary factor identification is described in the context of a desktop analysis where a human operator, for example an analyst, uses the application software 234 in combination with knowledge and experience to postulate candidates for the primary factors mentioned above and then to test these postulations in order to determine whether the candidate factors are, in fact, primary factors. For example, identification of a primary factor comprises postulating a candidate factor that contribute to the type and pattern of observed microseismicity for a given stimulation stage and then it is determined whether the candidate factor contributes to the type and pattern of observed microseismicity using one or more physical analysis techniques, for example a geophysical analysis technique. The candidacy of the candidate primary factor is thereby tested.

However, the skilled person will appreciate that the methods set forth herein need not be implemented in software and other hardware-based techniques can be employed, for example programmable hardware, such as Field Programmable Gate Arrays (FPGAs) or customizable integrated circuits, such as Application-Specific Integrated Circuits (ASICs). It will, nevertheless, be apparent to the skilled person that a software-oriented approach is more elegant than a pure hardware approach. In any event, the skilled person should appreciate that the determination of primary factors and associated processing can be performed in a more automated manner than a desktop analysis requiring decision-making from the human operator. Consequently, rules and knowledge-based approach will be described later herein.

Referring to Figure 4, the application software 234 supported by the fracture analysis apparatus 124 comprises a plurality of candidate selection analysis applications 250. The application software 234 also comprises a plurality of candidate testing applications 252. Each of the candidate selection analysis applications 250 and the candidate testing applications 252 are capable of accessing with a database of microseismic data 254 stored by the hard disc drive 222 and a database of other treatment-related data 256 stored by the hard disc drive 222. The application software 234 further comprises a factor sequence recordal application 260 that is capable of accessing a factor sequence store 258, which is also stored by the hard disc drive 222. The factor sequence recordal application 260 can be any suitable application for recording the primary factors and a sequence thereof, for example a spreadsheet application, such as Excel ^® available from Microsoft Corporation.

The candidate selection analysis applications 250 can be any suitable analysis application that implements one or more analysis techniques, which enables an analyst, using the fracture analysis apparatus 124, to identify one or more candidate factors. For example, any one more of the techniques set out in the disclosures referenced below can be employed, the contents of which are hereby incorporated by reference in their entirety.

International Patent Publication No. WO 201 1 /077223, entitled "System and Method for Microseismic Analysis";

International Patent Publication No. WO 201 1 /077227, entitled Identification of Reservoir Geometry from Microseismic Event Clouds"; "Quantitative Interpretation of Major Planes from Microseismic Event Locations with Application to Production Prediction" (Williams, M., Khadhraoui, B. and Bradford, I., presented at the 80TH SOCIETY OF EXPLORATION GEOPHYSICISTS CONFERENCE, Denver, US, 17-22 October, 2010);

"Recovering Frequency-Magnitude Statistics from Detection Limit Microseismic Data", (Williams, M.J., Extended abstract of presentation given at the 73 ^rd EUROPEAN ASSOCIATION OF GEOSCIENTISTS AND ENGINEERS CONFERENCE & EXHIBITION incorporating SPE EUROPEC 201 1 , Vienna, Austria, 23-26 May, 201 1 );

"Recovering Frequency-Magnitude Statistics from Detection Limited Microseismic Data", (Williams, M.J. and Le Calvez, J. EUROPEAN ASSOCIATION OF GEOSCIENTISTS AND ENGINEERS, GEOPHYSICAL PROSPECTING, 61 (Suppl. 1 ), 20-38, DOI: 10.1 1 1 l /j.l 365- 2478.2012.01097.x, 2012);

International Patent Publication No. WO 2014/1 10542, entitled "Method of Analyzing Seismic Data";

"Towards Self-consistent Microseismic-based Interpretation of Hydraulic Stimulation", (Williams, M.J., J.H. Le Calvez, J. Stokes, presented at the 75th EAGE CONFERENCE AND EXHIBITION incorporating SPE EUROPEC 2013, 10-13 June 2013, London, UK); and

US Patent Publication No. 2010/0307755, entitled "Method and Apparatus for Efficient Real-Time Characterization of Hydraulic Fractures and Fracturing Optimization Based Thereon". Similarly, the candidate testing applications 252 can be any suitable analysis application that implements one or more analysis techniques that can be used to confirm whether a candidate factor postulated using the candidate selection analysis applications 250 is indeed a primary factor and also the location in a sequence of primary factors associated with the treatment of the formation 1 10. The database of microseismic data 254 stores microseismic measurement data corresponding to observed microseismic events. The database of other treatment-related data 256 stores, for example data relating to the formation and/or engineering parameters, such as pump pressure data, pump rate data and/or pump duration data, fluid and proppant properties, fluid and proppant injection volume rates versus time, total fluid pumped versus time, total proppant pumped versus time, measurements from inside the treatment well, such as downhole pressure versus time and temperature, measurements made using fibre-optic sensors installed in and/or around the treatment well.

In operation (Figure 5), at least one fracture stage has been completed and one or more fracture stages remain to be implemented. Any pre-execution data associated with the multi-stage stimulation process that is also available can be used to assist with the identification of candidate factors and testing of their respective candidacies.

An analyst selects (Step 300) an analysis technique and selects (Step 302) a candidate selection analysis application 250 that can perform the analysis technique selected. For example, the analyst selects, in this example, an interpretation software application, such as the Petrel application (available from Schlumberger Limited) or the Matlab® application (available from The MathWorks, Inc.), as the application in order to plot and visually analyze a distribution of microseismic events. In this regard, the interpretation software application accesses the database of microseismic data 254, but it should be understood that other applications may require additional or alternative access to the database of other treatment-related data 256.

The interpretation software is therefore used to identify a number of candidate factors attributable to the observed microseismicity. Depending upon the application used, one or more candidate factors may be identified. In this respect, it is desirable to monitor the style and pattern of the microseismicity, and to this end it is necessary to identify the factor or factors which are likely to contribute to and hence influence the style and pattern of the microseismicity in fracture stages yet to be implemented. In order to achieve this goal, an analyst, using the analysis technique(s) selected, identifies (Step 304) changes to the nature of observed microseismicity and, where possible, the times at which the changes occur.

Referring to Figure 6, for example, in order to identify any changes in nature in the observed microseismicity, the interpretation software 400 accesses (Step 400) a first data set relating to a first given fracture stage, which may not necessarily be the first fracture stage performed, and an analyst, using the interpretation software 400, analyses the pattern of points defined by the microseismic data accessed, constituting an example of post (stimulation stage)-execution data, in order to determine (Step 402) whether, within the stage being analyzed, a change has taken place, for example a change in a spatio- temporal pattern of the observed microseismicity associated with the stimulation stage. In the event that a change has been detected, the change is logged (Step 404) by an analyst. Thereafter, the interpretation application is used to analyze rate data in order to determine (Step 406) whether, within the stage being analyzed, the rate of occurrence of microseismic events has changed. In the event that a change is detected, the change is logged (Step 408) by the analyst. Thereafter, the interpretation software application is used to analyze the source characteristic data in order to determine (Step 410) whether, within the stage being analyzed, the source characteristic has changed. In this example, the source characteristic data comprises information defining the motion during failure of the formation, for example shear, tensile opening or closure, and the extent of the motion.

The source characteristic data can be any suitable quantifiable descriptor that can assist in determining the type (style) of the microseismic event. In the event that a change in the source characteristic is detected, the change is logged (Step 412) by the analyst. Although the determination steps described above (Steps 402, 406, 410) have been described above in a specific order, it should be appreciated that the steps can be performed in any order deemed appropriate to the specific application. An analyst then determines (Step 414) whether the database of microseismic data 250 contains data relating to subsequent fracture stages. In the present example, the database of microseismic data contains data relating to further fracture stages and so the data relating to the next fracture stage is accessed (Step 416) and, using the newly accessed microseismic data and the previously accessed microseismic data mentioned above, the interpretation application is used to analyze the respective patterns respectively defined by each of the current and preceding microseismic data and to determine (Step 418) whether, between the fracture stages, the patterns of microseismicity have changed. In the event that the patterns have changed, the change is logged (Step 420) by the analyst. Similarly, the interpretation application is then used to analyze the rate data for each of the preceding and current fracture stages being analyzed and to determine (Step 422) whether, between the stages, the rates have changed. In the event that the rates have changed, the change is logged (Step 424) by the analyst. Thereafter, the interpretation software application is used to analyze the source characteristic data for each of the preceding and current fracture stages being analyzed and to determine (Step 426) whether, between the stages, the source characteristics have changed. In the event that the source characteristics have changed, the change is logged (Step 428) by the analyst.

Although the determination steps described above (Steps 418, 422, 426) have been described above in a specific order, it should be appreciated that the steps can be performed in any order deemed appropriate to the specific application. Additionally, the analysis relating to detecting changes within the fracture stage is also performed (Steps 402 to 412) before the analyst determines (Step 414) whether data relating to further microseismic stages is stored. In the event that further data is stored, the above-described analysis can be repeated. In this respect, the above-described example employed a data window bounded by a preceding and immediately succeeding fracture stage. In other examples, the data window can be widened. However, the window should not be expanded so as to be too wide, for example greater than two fracture stages in width in a given temporal direction (it is conceivable to analyze data relating to a fracture stage temporally disposed between a number of other fracture stages; this is to provide context, so that changes can be seen but are not obscured by too much data).

If microseismic data relating to further fracture stages is determined (Step 414) not to be available, an analyst determines (Step 430) whether any changes in the nature of the observed microseismic data have been logged. Referring back to Figure 5, in the event that changes have been logged, an analyst uses knowledge of the changes logged to infer (Step 306) one or more candidate factors using knowledge and experience available to the analyst. An analyst then proceeds (Step 432, Figure 6) to test (Step 308) the candidacy of the candidate factors postulated. However, if no changes have been logged, an analyst proceeds to analyze (Step 434, Figure 6) engineering parameters. However, in another embodiment, the analyst can analyze the engineering parameters prior to testing the candidacy of the candidate and provide a prediction of distribution of the microseismic events that may be encountered upon execution of future fracture stage. The analyst can also optionally provide, where circumstances permit, a prediction of the style of the microseismic events.

Referring to Figure 7, in the event that changes have not been logged as a result of the above-described analysis, the analyst attempts to detect (Step 450) changes in engineering parameters, constituting execution parameter data. For example, the interpretation application can be used to analyze pumping pressure, pumping rate and/or pumping duration. In addition, the interpretation application can be used to analyze time sequences of these parameters. In the event that a change in one or more of the engineering parameters is detected, the change is logged (Step 452) along with any relevant time sequence information. An analyst also attempts to determine (Step 454) any changes relating to the formation 1 10 being stimulated. In this regard, such changes relate to the formation 1 10 including changes to pre-existing fault properties of faults in the formation 1 10, for example changes to: the stress state of the formation, for example pore pressure, mechanical properties of the formation 1 10 (for example density and/or failure properties), flow properties (for example porosity and permeability); and changes to the properties of in-situ fluids. In the event that changes relating to the formation are detected, the analyst logs (Step 456) the changes identified.

As can be seen from the above-described methodology, the changes in nature to the observed microseismicity are either diagnosed directly using microseismic data alone or microseismic data in combination with other types for data, for example data indicative of changes in engineering parameters and/or the nature of the formation 1 10.

Assuming that a change in nature of some description has occurred and is identified, the analyst proceeds (Step 308, Figure 5) to test the candidacy of the candidate factors in order to quantify the effect of the candidate factor where the effect has a correlation with the observed microseismicity. The above described method will now be further exemplified by an analysis being performed in the context of the zipper fracture of Figure 1 . Referring to Figures 8 to 1 1 , plots of microseismic events generated by the interpretation application used by the analyst can be used to analyze the evolution of observed microseismicity during stimulation treatments in a first representation 508 of the first lateral portion 106 and a second representation 510 of the second lateral portion 108 in accordance with the method of Figures 5 and 6 described above. In this respect, although not shown in Figure 1 , three pre-existing faults are known: a first fault 500, a second fault 502 and a third fault 504. Information relating to the faults is stored in the database of other treatment-related data 256. Microseismic data used is obtained from data collected in a monitoring well 506, which constitutes the further borehole 122 by, in this example, the 20 deepest levels of a so-called 71 x3C seismic array located in the monitoring well 506. Following the methodology of Figure 6, and referring to Figure 8, the earliest stage of the first lateral portion 508, which is nearest to the distal ends (sometimes referred to as "toes") of the wellbore 102, comprises substantially linear fractures of expected length. However, referring to Figure 9, fracture stages further from the toe of the first lateral portion 508 and located north of the first and third faults 500, 504 comprise shorter fractures that are geometrically less linear in nature than the fractures near to the toes of the wells.

Analysis of Figure 9 by the analyst, in respect of the third and fourth stages of treatment, further reveals a significant concentration of microseismicity in a zone lying to the east of the first and second faults 500, 502 and to the west of the third fault 504. Additionally, it can be seen from Figure 8 that a fracture stage on the second lateral portion 510 occurs directly between the first and second faults 500, 502, but the lateral extent of the fracture stage is much shorter than the lateral extend of preceding fracture stages on the second lateral portion 510. Referring to Figures 8, 9 and 10, the analyst notices that little microseismicity exists south of the second and third faults 502, 504, respectively. Moreover, south of the third fault 504, microseismic activity decreases from west to east. Lastly, south of the first, second and third faults 500, 502, 504, microseismic activity decreases to the south, although such a decrease may be attributable to suggested declines in the number of located microseismic events with increasing distance between the sources of microseismic events and the seismic array located in the monitoring well 122. Although qualitative changes to the observed microseismicity can be analyzed, for example as described above, it should be appreciated that such analysis can be accompanied by or replaced by quantifiable measures of changes in microseismicity, for example as described in the disclosures referenced below, the contents of which are incorporated herein by reference in their entirety:

"A Comparison of the Modeled Behaviour of a Couple System Comprising Of A Hydraulic Fracture And Natural Fractures With Microseismic Observations", (Bradford, I., presentation at the EAGE THIRD PASSIVE SEISMICS WORKSHOP - ACTIVELY PASSIVE !, Athens, Greece, March 27-30, 201 1 ); "Correlating The Distribution of Microseismic Events with the Stress State for Multistage Stimulations" (Bradford, I. presentation at the EAGE Fourth PASSIVE SEISMICS WORKSHOP, Amsterdam, Netherlands, March 17-20, 2013); "Geomechanical Interpretation of a "Zipper Frac" in Barnett Shale Wells" (Le Calvez, J. Williams, M.J., Stokes, J., AMERICAN ASSOCIATION OF PETROLEUM GEOLOGISTS ANNUAL CONVENTION & EXHIBITION, Long Beach, CA, 22-25 April, 2012);

"Evaluating the Impact of Mineralogy, Natural Fractures and In Situ Stresses on Hydraulically Induced Fracture System Geometry in Horizontal Shale Wells" (Miller, C, Hamilton, D., Sturm, S., Waters, G., Taylor, T., Le Calvez, J.H. and Singh, M., SOCIETY OF PETROLEUM ENGINEERS, Paper 163878, SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 4-8 February 2013);

"Inversion and Attribute-Assisted Hydraulically-lnduced Microseismic Fracture Prediction: A North Texas Barnett Shale Case Study (Refunjol, X.E., Keranen, K. M., and Marfurt, K.J. (2010), SOCIETY OF EXPLORATION GEOPHYSICISTS TECHNICAL PROGRAM, Expanded Abstracts, Pages 2616-2165, Society of Exploration Geophysicists 2010 Annual Meeting, 18-20 October);

"Inversion and Attribute-Assisted Hydraulically Induced Microseismic Fracture Characterization in the North Texas Barnett Shale" (Refunjol, X.E., Marfurt, K.J. and Le Calvez, J.E. (201 1 ), THE LEADING EDGE, March 201 1 , Vol. 30, No. 3, Pages 292-299);

"Integration of Hydraulically-lnduced Microseismic Event Locations with Active Seismic Attributes: A North Texas Barnett Shale Case Study (Refunjol, X.E., Keranen, K., Le Calvez, J.H., and Marfurt, K.J., (2012), GEOPHYSICS, Vol. 77, Pages 1 -12); "Unconventional Geophysics for Unconventional Plays" (Rich, J. P. and

Ammerman, M., Society of Petroleum Engineers, Paper 131779, SPE UNCONVENTIONAL GAS CONFERENCE, Pittsburgh, Pennsylvania, USA, 23-25 February 2010);

"Faulting Induced by Forced Fluid Injection and Fluid Flow Forced by Faulting: An Interpretation of the Hydraulic Fracture Microseismicity, Carthage Cotton Valley Gas Field, Texas" (Rutledge, J.T., Phillips, W.S. and Mayerhofer, M.J., BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, Vol. 94, No. 5, Pages 1817-1830, October 2004);

International patent publication no. 2014/105659 entitled "Method of Calibrating Fracture Geometry to Microseismic Events"; Towards Self-consistent Microseismic-based Interpretation of Hydraulic Stimulation (Williams, M.J., J.H. Le Calvez, J. Stokes, presented at the 75th EAGE Conference and Exhibition incorporating SPE EUROPEC 2013, 10-13 June 2013, London, UK); "Temporal Evolution of Stress State from Hydraulic Fracturing Source

Mechanisms in the Marcellus Shale" (Williams-Stroud, S., Neuhaus, C.W., Telker, C, Remmington, C, Barker, W., Neshyba, G. and Blair, K., SOCIETY OF PETROLEUM ENGINEERS (SPE), Paper 162786, SPE Canadian Unconventional Resources Conference, Calgary, Alberta, 30 October - 1 November 2012); International Patent Publication No. WO 2013/067363, entitled "Modeling of

Interaction of Hydraulic Fractures in Complex Fracture Networks"; and

"Modelling of Interaction of Hydraulic Fractures in Complex Fracture Networks" (Wu, R., Kresse O., Weng, X. , Cohen, C. and Gu. H. (2012), SOCIETY OF PETROLEUM ENGINEERS (SPE), Paper 152052, SPE Hydraulic Fracturing Conference, The Woodlands, Texas, 6-8 February).

In the above-described example in the context of the zipper fracture, the candidate factor identified by the analyst using the interpretation application is the nature of the formation 1 10, namely the first, second and third faults 500, 502, 504.

In another example (Figures 12 and 13), a different candidate selection analysis application 250, for example a Seisview application (available from Lynx Information Systems, UK), is used in conjunction with the interpretation software application in order to observe changes attributable to the influence of the fracturing process on the surrounding formation, for example, in-well processes. Referring to Figure 12, the z- component observations from a Versatile Seismic Imager (VSI) tool monitoring in the first lateral portion 106 is presented by the Seisview application mentioned above. As can be seen from Figure 12, the left hand side 550 of the observations are random in nature, for example having a Poisson-type type distribution, and an exponential-type distribution of amplitudes. However, the right hand side of the observations are substantially regular in frequency and substantially constant in amplitude. This change is caused by particulates in proppant blocking casing perforations, also known as near-well "screen outs", which in this example constitutes the candidate factor identifiable by the analyst. The change can be identified using b-value analysis (Figure 13), namely analysis of the exponent, b, associated with a Gutenberg-Richter distribution, for example as described in "Reconstructing Frequency-Magnitude Statistics from Detection Limited Microseismic Data" (Williams, M. J. and Calvez, J. L, 2013, GEOPHYSICAL PROSPECTING, 61 : 20-38., doi :10.1 1 1 1 /j.1365-2478.2012.01097.x), and can be further supported by pressure measurements and analysis, for example as described in "Towards Self-consistent Microseismic-based Interpretation of Hydraulic Stimulation" (Williams, M.J., J.H. Le Calvez, J. Stokes, EAGE, London, UK, 2013) mentioned above.

The above two examples do not constitute a recitation of all possible candidate factors and the skilled person will appreciate that a considerable number of other factors exist including, but not limited to formation structure, stress state, textural effects, such as the distribution of pre-existing faults, formation properties, fault properties, and pore fluid properties, such as whether the fluid is water rather than hydrocarbon, or gas is present. Similarly, the factors mentioned above relating to the nature of the stimulation operation employed are not exhaustively listed and other factors would be readily apparent to the skilled person including, but not limited to, spacing between fracture stages (because individual fracture stages may create different effects on the surrounding geology). In this respect, each fracture stage can have a respective zone of influence around itself, for example a zone within the formation that is perturbed due, for example, to perturbations to the stress state and pore pressure of the formation. Individual fracture stages can therefore be placed sufficiently far apart so that their respective zones of influence do not interact each other. Despite such spacing, such zones of influence can nevertheless interact with pre-existing features in the geological environment, for example fluid and/or solids invasion into the formation matrix or into pre-existing faults, and/or remote activation of pre-existing faults by stress or pore pressure change. In contrast, if fracture stages are sufficiently closely spaced, coupling effects can occur, for example pre-existing or newly created features can interact either directly or via zones of influence and, of course, with the geological environment. The changes in the observed microseismicity identified by the analyst using the candidate selection analysis application(s) 250 now allow the candidate factors to be tested (Step 308) in order to determine which of the candidate factors are primary factors attributable to the microseismicity observed.

In this respect, and referring to Figure 14, an analyst selects (Step 470) a first candidate from the candidate factors identified and then an analyst selects (Step 472) a candidate test analysis technique appropriate for testing the candidacy of the candidate factor selected. The analyst then selects (Step 474) a candidate testing application 252 capable of implementing the technique selected. Thereafter, the software application is executed in order to test the candidacy of the candidate factor selected.

For example, in order to test the candidacy of the candidate factor postulated in relation to Figures 8 to 1 1 , for example the first, second and third faults 500, 502, 504, the analyst selects analysis of stress as the technique (Step 470). The analyst then selects (Step 472) the Petrel application available from Schlumberger Limited to generate a map view (Figure 15) of effective normal stress in the z (north) direction. The analyst uses the candidate testing application 252 to overlay the locations of the first, second and third faults 500, 502, 504 and the first and second lateral portions 508, 510. Referring to Figure 14, "Correlating The Distribution Of Microseismic Events With The Stress State For Multi-Stage Stimulations" (Bradford, I. presentation at the EAGE Fourth Passive Seismics Workshop, Amsterdam, Netherlands, March 17-20, 2013) mentioned above describes how the slippage of the first, second and third faults 500, 502, 504, which occurs during the early stages of a zipper fracture, creates zones of higher compression, shown as darker shading. The zones of higher compression influence the pattern of observed microseismicity during the later stages of the creation of the zipper fracture. In this respect, the first and third faults 500, 504 do not significantly influence the in-situ stress state at locations more than 1500ft (458m) to their north. Thus, the earliest stages in the first lateral portion 508, which are nearest to the toe of the wellbore 102, propagate fractures at an azimuth of 63 °east of north, which is the regional direction of the maximum in-situ horizontal stress; the fractures are also of the expected length.

Stages further from the toe of the wellbore and north of the first and third faults 500, 502 generate shorter fractures and microseismicity does not occur in the higher compression zones north of the first and third faults 500, 504. There is a significant concentration of microseismicity in a zone of lower compression lying east of the first and second faults 500, 502 and west of the third fault 504. A fracture stage occurs directly between the first and second faults 500, 502, but its lateral extent seems to be suppressed by the stronger compression occurring in this area. South of the second and third faults 502, 504, where there is some enhanced compression, there is little microseismicity. However, compression increases south of the third fault 504 from west to east and microseismic activity decreases over this spatial trend. Further to the south of the faults, there is increased compression and decreased microseismic activity, although this may be attributable to the distance from the seismic array mentioned above. Hence, using the candidate testing application and the analysis technique that it employs, the analyst confirms that the selected candidate factor, namely the first, second and third faults 500, 502, 504 are, in fact, primary factors. Moreover, this analysis enables the analyst to determine when, in time, i.e. when in a sequence of factors during treatment of the formation 1 10, the primary factor influences the fracturing of the formation 1 10.

Once the first candidate factor has been tested, the analyst determines (Step 476) whether more candidate factors were identified during the previous stage of analysis, and if more candidate factors remain for testing, the analyst selects (Step 478) the next candidate factor and repeats the above-described procedure (Steps 472 to 476) using an appropriate analysis technique and candidate testing application 252. As can be seen, the process of identification of candidate factors, which includes postulation and testing candidacy, is repeated in order to generate a set of primary factors determined to contribute to the type and pattern of observed microseismicity.

Referring to Figures 16 to 19, in another example of the method of Figure 14, the analyst selects another analysis technique and a candidate testing application 252 in order to perform a geomechanical modelling exercise in order to determine whether a candidate factor previously identified is, in fact, a primary factor. In this example, the candidate factor is the evolution of failure zones in the formation 1 10 on neighboring fracture stages. This is a different factor to those described above, but would be apparent to the skilled person analyzing a given formation. As a result of selecting a geomechanical modelling technique (Step 472), the analyst selects (Step 474) the Petrel Reservoir Geomechanics finite-element geomechanics simulator application available from Schlumberger Limited. This candidate testing application 252 is used by the analyst to review the microseismicity and development of failure zones in the formation 1 10. In this respect, the analyst observes that in respect of a first stage of the first lateral portion 106 there is no microseismic activity in the circled regions 600, 602. Referring to the geomechanical model (Figure 17), failure can be seen to be developing as a result of execution of the first stage of fracturing in respect of the first lateral portion 106. Referring to Figure 18, the microseismic response associated with the first fracturing stage of the second lateral portion 108, suggests reactivation of a failure zone by this treatment, which is an example of the analysis revealing that a pre-existing fault and/or fracture can modify the stress state of the formation 1 10, bringing it nearer to failure or taking it further away from failure, which can impact upon the development of fractures in relation to the first lateral portion 106. Turning to the geomechanical model for this stage (Figure 19), the geomechanical model suggesting that this stage perturbs the failure zone created previously in respect of the first stage of the first lateral portion 106 towards additional failure even in the absence of fluid communication between these stages. Hence, in this example, the analyst concludes that the candidate factor of the evolution of failure zones on neighboring fracture zones influences the pattern of microseismicity, is in fact a primary factor. It should be appreciated, however, that a number of techniques and types of information, for example formation type identification data, are at the analyst's disposal for testing the candidacy of the candidate factor, or indeed selection of the candidate factor. Moreover, the testing analysis allows the analyst to determine when in a sequence of primary factors this particular primary factor is of significance in the treatment of the formation 1 10, which is noted by the analyst.

Although the above examples have been described in the context of a desktop analysis conducted by an analyst, the skilled person will appreciate that the methodology set forth above can be implemented in a rules-based system, thereby avoiding substantial interaction with the analyst. In this respect, referring to Figure 20, a multi-stage stimulation process microseismicity prediction apparatus 700 comprises a rules engine 702 capable of accessing a knowledge base 704. The rules engine 702 is arranged to execute rules in order to implement a decision-making process emulating the knowledge and experience of the analyst, thereby automating the above-described methodology. The rules engine 702 is operably coupled to an analysis algorithm engine 706 and both the rules engine 702 and the analysis algorithm engine are capable of accessing a data store 708, which can include the database of microseismic data 254 and/or the database of other treatment-related data 256 mentioned above. The analysis algorithm engine 706 is also capable of accessing a resource of algorithms 710 for execution. The analysis algorithm engine 706 also comprises a pattern recognition engine 712.

In this example, the analysis algorithm engine 706, based upon instructions from the rules engine 702, executes appropriate analysis algorithms in order to contribute to the implementation of the methodology described above in relation to the previous desktop analysis based examples. The rules engine 702 uses the knowledge base 704 in order to make decisions concerning postulation of candidate factors contributing to a type and pattern of observed microseismicity associated with a stimulation stage of a geological formation and testing of the candidacy of the candidate factors. In this regard, the rules engine 702 uses results generated by the analysis algorithm engine 706, including where appropriate analysis results generated by the pattern recognition engine 712, in order to make appropriate decisions concerning the postulation of the candidate factors and/or the testing of the candidacy of the candidate factors. The above-described methodology can be employed as part of a process of treating a geological formation by stimulation where a wellbore extends into the formation. The primary factors identified can thus be used in order adjust engineering parameters that contribute to the stimulation of the formation. In this respect, the identification of the primary factors allows potential observed microseismicity to be better predicted in respect of subsequent, unexecuted/proposed, fracture stages.

Although the drilling system is shown in Figure 1 as being on land, those of skill in the art will recognize that the examples set forth herein are equally applicable to marine environments.

In accordance with the present disclosure, a well site with associated wellbore and apparatus is described in order to describe an embodiment of the application. To that end, an apparatus at the well site can be altered due to field considerations encountered.

It is to be understood that the various embodiments and examples described herein, although different, are not necessarily mutually exclusive. For example, a particular feature, structure, or characteristic described herein in connection with one embodiment may be implemented within other embodiments without departing from the scope of the invention. In addition, it is to be understood that the location or arrangement of individual elements within each disclosed embodiment may be modified without departing from the spirit and scope of the invention. The detailed description set forth herein is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims, appropriately interpreted, along with the full range of equivalents to which the claims are entitled.

In this regard, although the examples set forth herein make reference to geophysical techniques and data, the skilled person should appreciate that the techniques employed in relation to the methodology exemplified herein is not limited to the field of geophysics and any suitable physical analysis technical can be employed. Although in the above examples, execution parameter data, for example engineering parameters, has not been used to test or contribute to testing of candidate primary factors, the skilled person should understand that use of such data is contemplated.

It should also be noted that in the development of any such actual embodiment, numerous decisions specific to circumstance must be made to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

In this disclosure, the term "storage unit" may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term "computer-readable medium" includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and various other mediums capable of storing, containing or carrying instruction(s) and/or data.