CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application No. 63/266,424, filed on January 5, 2022, which is incorporated by reference herein.

FIELD

[0002] The present disclosure generally relates to process and systems for automating conveyance operations using an elongated conveyance member, e.g., a wireline, a slickline, or a coiled tubing.

BACKGROUND

[0003] In many well applications, wireline, slickline, and coiled tubing is employed to facilitate performance of many types of downhole operations. Coiled tubing offers versatile technology due in part to its ability to pass through completion tubulars while conveying a wide array of tools downhole. A wireline system can include many systems and components, including a wireline reel, an injector head, a gooseneck, lifting equipment (e.g., a mast or a crane), and other supporting equipment such as pumps, treating irons, or other components. Wirelines have been utilized for performing well logging and/or treatment in existing wellbores such as hydraulic fracturing operations, matrix acidizing operations, milling operations, perforating operations, wireline drilling operations, and various other types of operations.

[0004] These operations, however, can face operational issues such as a downhole tool becoming stuck due to an increase in friction between the downhole tool and a wellbore, human errors, and system reaction times. Accordingly, processes and systems that can automate the operations and avoid and/or react to the operational issues are desired.

SUMMARY

[0005] 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.

[0006] Processes and systems for automating a conveyance operation using an elongated conveyance member are provided. In some embodiments, the process can include modeling one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models. The process can also include calibrating the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models. The process can also include using the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states. The process can also include using one of (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, wherein the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or the one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, wherein the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or more undetectable system states.

[0007] In some embodiments, the system can include a network adapter configured to acquire sensor data from sensors during a conveyance operation within a wellbore using an elongated conveyance member; and at least one processor configured to execute an executable code to implement at least the functionality of modeling one or more system state profiles of a downhole tool string during the conveyance operation to define one or more profile models. The at least one processor can also be configured to calibrate the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models. The at least one processor can also be configured to use the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states. The at least one processor can also be configured to use one of (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, where the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, where the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

[0008] In some embodiments, the system can include at least one processor configured to execute an executable code to implement at least the functionality of modeling one or more system state profiles of a downhole tool string during a conveyance operation via an elongated conveyance member within a wellbore to define one or more profile models. The at least one processor can also be configured to calibrate the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models. The at least one processor can also be configured to use the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states. The at least one processor can also be configured to use one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, where the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, where the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The subject disclosure is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of the subject disclosure, in which like reference numerals represent similar parts throughout the several views of the drawings.

[0010] FIG. 1 depicts an illustrative process for automating conveyance operations using an elongated conveyance member, according to one or more embodiments described.

[0011] FIG. 2 depicts a schematic illustration of an example well system, according to one or more embodiments described.

[0012] FIG. 3 depicts a schematic of an illustrative computing system for automating conveyance operations using an elongated conveyance member, according to one or more embodiments described.

[0013] FIG. 4 depicts an illustrative computing device that includes a conveyance operations events controller and a non-transitory computer-readable medium that includes computer executable instructions stored thereon within the computing system shown in FIG. 3, according to one or more embodiments described.

DETAILED DESCRIPTION

[0014] The particulars shown herein are by way of example and for purposes of illustrative discussion of the examples of the subject disclosure only and can provide what might be the most useful and readily understood description of the principles and conceptual aspects of the subject disclosure. In this regard, no attempt is made to show every structural detail, the description taken with the drawings making apparent to those skilled in the art how the several forms of the subject disclosure can be embodied in practice.

[0015] The embodiments described herein generally include processes and systems that facilitate operation of well-related tools. In one or more embodiments, a variety of data (e.g., downhole data and/or surface data) can be collected to enable optimization of operations related to the well-related tools. In certain embodiments, the collected data can be provided as advisory data (e.g., presented to human operators of the well to inform control actions performed by the human operators) and/or used to facilitate automation of downhole processes and/or surface processes (e.g., which can be automatically performed by a computer implemented surface processing system (e.g., a well control system), without intervention from human operators). In certain embodiments, the processes and systems described herein can enhance downhole operations by improving the efficiency and utilization of data to enable performance optimization and improved resource controls of the downhole operations. In certain embodiments, a downhole tool string can be deployed downhole into a wellbore via an elongated conveyance member. In certain embodiments, the processes and systems described herein can be used for displaying or otherwise outputting desired (e.g., optimal) actions to human operators so as to enable improved decision-making regarding operation of the downhole tool string (e.g., operation of a downhole or surface system/device).

[0016] In certain embodiments, downhole system states are obtained via, for example, one or more downhole sensors while the downhole tool string is disposed in the wellbore. In certain embodiments, the downhole system states can be obtained by the downhole sensors in substantially real time (e.g., as the downhole data is detected while the downhole tool string is being operated), and sent to a processing system via wired or wireless telemetry. The downhole system states can be combined with surface system states. In certain embodiments, the downhole and/or surface system states can be processed during operation of the downhole tool string within the wellbore to enable automatic optimization (e.g., by the surface processing system, without human intervention) with respect to the operation of the downhole tool string during subsequent stages of downhole tool string operation.

[0017] FIG. 1 depicts an illustrative process 100 for automating conveyance operations using an elongated conveyance member, according to one or more embodiments. The process 100 can include modeling one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models (Block 103). In some embodiments, the system state profile can be or can include, but is not limited to, a speed profile, a tension profile, a depth profile, and/or a force profile of the downhole tool string during the conveyance operation within the wellbore. The process 100 can include calibrating the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models (Block 104). The process 100 can further include using the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states (Block 105). The process 100 can further include using one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, where the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or the one or more undetectable system states (Block 106); or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, where the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states (Block 107). The surface tension and the surface weight arise from both the downhole tool string and the elongated conveyance member.

[0018] In some embodiments, the process 100 can further include using Bayes filtering to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models (Block 108). In some embodiments, the process 100 can further include using optimization-based analyses to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models (Block 109). In some embodiments, the process 100 can further include using a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on one of the calculated elongated conveyance member tension profile or the calculated elongated conveyance member force profile (Block 110). In some embodiments, the process 100 can further include using an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string based, at least in part, on at least one of the calculated surface tension and the uncertainty bounds around the calculated surface tension, and the calculated surface weight and uncertainty bounds around the calculated surface weight (Block 111).

[0019] FIG. 2 depicts a schematic illustration of an example well system 200, according to one or more embodiments. As illustrated, in certain embodiments, an elongated conveyance member string 210 can be run into a wellbore 235 that traverses a hydrocarbon-bearing reservoir 230. The well system 200 can include an interconnection of pipes or casings 225 and elongated conveyance member 204 that can connect to a surface facility 215 at the surface 217 of the well system 200. In certain embodiments, the wellbore 235 can be an open wellbore or a cased wellbore defined by the casing 225.

[0020] A downhole tool string 220 can be run inside the casing 225 via the elongated conveyance member 204. The downhole tool string 220 can include various downhole tools such as a downhole motor that can operate to rotate a drill bit (e.g., during drilling operations), a variety of drilling/cutting/logging tools, or other downhole tools. The downhole tools can be moved along the wellbore 235 via the elongated conveyance member 204. In some embodiments, the downhole tool string 220 can be coupled with the elongated conveyance member 204 to provide the elongated conveyance member string 210. In other embodiments, the downhole tool string 220 can be coupled with the elongated conveyance member 204 and, optionally, other surface equipment to provide the elongated conveyance member string 210. It should be noted the downhole tool string 220 can be include one or more of various types of downhole tools.

[0021] The downhole tool string 220 can be connected to the elongated conveyance member 204, which can be used to run the downhole tool string 220 to a desired location within the wellbore 235. The downhole tool string 220 can be supplemented by isolation devices such as, for example, an inflatable packer that can be activated to isolate the zone below or above it and enable local pressure tests.

[0022] The downhole tool string 220 can include a downhole sensor package 221 that can include one or more downhole sensors. The sensor package 221 can be mounted within and/or on the downhole tool string 220 and/or along the elongated conveyance member 204. The sensor package 221 or components thereof can be positioned at other downhole locations. The sensor package 221 can include sensors such as gamma ray sensors, casing collar locator sensors, temperature sensors, pressure sensors, relative bearing and inclination sensors, and/or other sensors.

[0023] Data from one or more of the downhole sensors from the downhole sensor package 221 can be relayed up hole to the surface facility 215 to indicate system states of the downhole tool string 220. The processing can be accomplished via a computer-based processing system, one or more embodiments of which are described below. The processing can be accomplished in the surface facility 215, elsewhere on the surface 217, at one or more other suitable locations of the well system 200, and/or offsite or via cloud computing systems. In certain embodiments, the data can be relayed up hole in substantially real time (e.g., relayed while it is detected by the downhole sensors from the downhole sensor package 221 during operation of the downhole tool string 220) via the elongated conveyance member 204, and this real-time data can be referred to as edge data for assessments at the surface 217.

[0024] In addition, as described in greater detail herein, additional data (e.g., surface data) can be supplied by one or more surface sensors 211 and/or stored in computer memory locations in the surface facility 215 or elsewhere to indicate system states of a conveyance unit 212 or other surface 217 side equipment or systems. By way of example, historical data and other useful data can be stored in a cloud storage facility (not shown). The surface sensors 211 can include, but are not limited to, one or more of: elongated conveyance member rate detection sensors, elongated conveyance member tension detection sensors, elongated conveyance member force detection sensors, temperature sensors, pressure sensors, and/or fluid rheology sensors, among other types of sensors. [0025] The elongated conveyance member 204 can be a wireline, a slickline, or a coiled tubing. The elongated conveyance member 204 can be or can include one or more electrical lines, fiber-optic lines, or other suitable lines for transmitting data signals. The elongated conveyance member 204 can be deployed by the conveyance unit 212 and delivered downhole, with or without the downhole tool string 220.

[0026] The conveyance unit 212 can include the one or more surface sensors 211. The conveyance unit 212 can include actuators and local controllers, such as programmable logic controllers (PLCs), which can cooperate together to provide sensor data to, receive control signals from, and generate local control signals based on communications with, respectively, the surface facility 215.

[0027] The conveyance unit 212 can be controlled to slow or speed up the motion of the elongated conveyance member 204 so as to control the elongated conveyance member 204 tension and/or force and, thus, the motion of the elongated conveyance member string 210 within the wellbore 235. The downhole tool string 220 can be moved along the wellbore 235 via the elongated conveyance member 204 under control of the conveyance unit 212 so as to apply a desired elongated conveyance member tension or force, thus, to achieve a desired rate of penetration (ROP) within the wellbore 235 and/or a desired rate of extraction (ROE) from the wellbore 235. Depending on the specifics of a given application, various types of data can be collected downhole, and transmitted to the surface facility 215 in substantially real time to facilitate improved operation of the downhole tool string 220 while downhole. For example, the data can be used to fully or partially automate the downhole operations, to optimize the downhole operation, and/or to provide more accurate predictions regarding components or aspects of the downhole operation. In some embodiments, surface analysis based on the data can determine interactions between components of the elongated conveyance member 204 and/or the downhole tool string 220 and the wellbore 235 to infer normal force and friction coefficients.

[0028] Referring now to FIGS. 1 and 2, in one or more embodiments, the one or more profile models can predict the tension and/or or the force along the elongated conveyance member 204. The one or more profile models can also predict the tension and/or the force of the elongated conveyance member 204 at the surface sensors 211. The one or more profile models can model the elongated conveyance member 204 speed and tension and/or force and can model depth profile predictions for the downhole tool string 220. In some embodiments, a steady-state model can be used for predictions. The steady-state model can assume the elongated conveyance member 204 to be in tension or force equilibrium from the surface 217 to the downhole tool string 220. In some embodiments, a dynamic model can be used for predictions. The dynamic model can include inertia effects and wave propagation along the elongated conveyance member 204 such that the dynamic model’s tension and/or force predictions can accommodate conditions when the elongated conveyance member string 210 is not in mechanical equilibrium (e.g., when it starts/stops/switches direction/becomes stuck). In some embodiments, a hybrid model can be used for predictions to include features from the dynamic model and the steady-state model. Various profile models exist for creating or can be created to create mathematical models to predict the forces involved in conveying tools into and out of wellbores with the elongated conveyance member string 210. To make these predictions, the models can utilize input states or parameters such as the elongated conveyance member 204 geometric/mechanical parameters (outer diameter/inner diameter/density/elasticity), elongated conveyance member string 210 specifications such as length/weight/outer diameter, and/or wellbore 235 fluid properties such as viscosity/density, to name a few.

[0029] The one or more profile models can be deterministic, in other words, without uncertainties in the input states (e.g., friction coefficient between the downhole tool string 220 and the wellbore 235, fluid viscosity, etc.). Uncertainty in the one or more profile models can be quantified using probability distribution. The uncertainty quantification can output a distribution of the elongated conveyance member 204 surface tension or force with a mean value and a standard deviation, an uncertainty quantified prediction. The uncertainty quantification can include curvature analysis for uncertainty in parameter estimation using optimization algorithms, where a local gradient of an estimated quantity, such as tension or force, can be related to the uncertainty of a system state. By comparing detectable system states, and the uncertainty quantified predictions, anomalies can be detected downhole such as elongated conveyance member 204 tension or force at the surface that is too low or too high, as compared to an expected tension and/or force value. Accordingly, in some embodiments, actions, such as slowing or even stopping the elongated conveyance member 204 motion during operations, can be taken to avoid damage to the elongated conveyance member string 210. In other embodiments, actions, such as accelerating the elongated conveyance member 204 motion during operations, can be taken to accelerate completion of the operation if tension or force at the surface indicates the elongated member 204 can be deployed or retrieved at a faster rate.

[0030] The process 100 can infer one or more detectable and/or undetectable system states, FIG. 1, (Block 104). The one or more detectable and/or undetectable system states can be inferred in real or near real-time and updated during elongated conveyance member string 210 operations. Using the updated system states, re-planning can be performed to react to the updated system states information, for certain future time periods. Surface measurements (e.g., depth from length encoders and tension or force on the elongated conveyance member 204) can be used to calibrate the one or more profile models by inferring the system states and quantifying their uncertainties.

[0031] Detectable system states can include observable variables that can be directly measured and can include, but are not limited to, downhole tool string 220 depth, elongated conveyance member 204 surface weight, elongated conveyance member 204 surface tension, elongated conveyance member 204 head tension, elongated conveyance member string 210 surface weight, elongated conveyance member string 210 surface tension, elongated conveyance member string 210 head tension, gamma ray data, casing collar locator data, temperature, pressure, downhole tool string 220 relative bearing, downhole tool string 220 inclination, fluid level, wellbore fluid density, and/or speed of the downhole tool string 220 within the wellbore. Undetectable system states can include uncertain, unknown, unmeasured, and/or unmeasurable system states that are desired to be inferred and quantified. In some embodiments, the undetectable system states can be system states that are unmeasurable or can be system states that can be measured but have not been measured. In one or more embodiments, undetectable system states can include, but are not limited to, unknown downhole environments or conditions such as obstructions caused by the deposition of sand/debris/chalks that could lead to the elongated conveyance member 204 and/or the downhole tool string 220 becoming stuck, unknown or unmeasured wellbore 235 tortuosity, uncertain reservoir pressure that could lead to the elongated conveyance member 204 becoming stuck, friction coefficient (e.g., a friction coefficient between the downhole tool string 220 and the wellbore), a stripper friction (e.g., a friction exerted by a stripper on the elongated conveyance member 204), unknown or unmeasured surface friction, unknown or unmeasured fluid level within the wellbore, and/or unknown or unmeasured fluid density within the wellbore.

[0032] Downhole system states from downhole data and/or surface system states from surface data can be used to calibrate the one or more profile models. For example, downhole system states and surface system states can be collected. In certain embodiments, the downhole system states and surface system states can be provided as advisory data for analysis and modeling. In other embodiments, the downhole system states and surface system states can be used to facilitate automation of downhole processes and/or surface processes. For example, the downhole operations can be automated without human intervention by a suitable processing system. The embodiments described herein can enhance downhole operations by improving the efficiency and utilization of downhole system states and surface system states to enable performance optimization and improved resource controls.

[0033] In some embodiments, the downhole tool string 220 can be disposed within the wellbore 235 and the downhole system states can be obtained via the downhole sensor package 221. In certain embodiments, the downhole system states can be obtained in substantially realtime and sent to the surface facility 215 via wired or wireless telemetry. In certain embodiments, the downhole system states can be combined with surface system states at the surface facility 215 or other suitable location, to calibrate the one or more profile models.

[0034] In some embodiments, the downhole system states that can be sensed in real time include, but are not limited to, weight on bit (WOB), torque acting on the downhole tool string 220, downhole pressures, downhole differential pressures, and other desired downhole system states. In certain embodiments, the downhole system states can be used in combination with surface system states, and such surface system states can include, but are not limited to, pump- related parameters (e.g., pump rate and circulating pressures). In certain embodiments, the surface system states also can include parameters related to fluid returns (e.g., wellhead pressure, return fluid flow rate, choke settings, amount of proppant returned, and other desired surface system states). In certain embodiments, the surface system states can include data from the elongated conveyance member 204 (e.g., surface tension or weight of the elongated conveyance member 204, speed of the elongated conveyance member 204, rate of penetration, and other desired parameters). In certain embodiments, the surface data that can be processed to optimize performance and can also include previously recorded data such as fracturing data (e.g., close-in pressures from each fracturing stage, proppant data, friction data, fluid volume data, and other desired data).

[0035] In some embodiments, Bayes filtering can be used to infer the system states and parameters and quantify their uncertainties. In some embodiments, the Bayes filtering can utilize the one or more profile models to assess the uncertainty of the one or more profile models predictions. During operations, sensors send measurements to the surface facility 215 and the measurements can be noisy. The noisy measurement can include the surface tension measurement for the elongated conveyance member 204, and can also include downhole measurements such as head tension, downhole tool string 220 acceleration, gamma ray signals and the like. The Bayes filtering can then use the one or more profile models to back calculate what input parameters could lead to such measurement (e.g., what friction coefficient input into the models would to output the received measured surface tension measured for example by the surface sensors 211). The back calculations can be performed utilizing probabilistic theories providing a probability distribution of the parameters that could explain the received measurements. This can also be called parameter inference. Finally, using the probability distribution of the parameters, the Bayes filtering can propagate those distributions into the one or more profile models to quantify the uncertainty of the output predictions. Another method that can be used to infer the parameters is optimization. The optimization method can use mathematical optimization to find a set of parameters that could match the received measurement when fed into the one or more profile models.

[0036] Bayes filters have been used extensively in different areas for state and parameter estimation. In general, they use a “forward measurement model” (e.g., one that predicts what measurements should occur given a set of states and parameters) to iteratively adjust the states and parameters based on the real measurements that are actually received. In general, Bayes filters infer the input parameters from the measurements.

[0037] There are two ways Bayes filters can be setup to infer the states: (1) a dual setup whereby a first Bayes filter can be constructed to infer the system parameters while treating the system states as deterministic variables, and a second Bayes filter can be constructed to do the opposite. The two filters can work simultaneously to provide inference and uncertainty quantification to the parameters and the states; and (2) a joint setup whereby the system parameters and the states are combined together, and a single Bayes filter infers this augmented state.

[0038] It should be noted that, for depth estimation (e.g., state inference), if downhole CCL/Gamma ray data is available, this data can also be integrated into the Bayes inference framework. In such a situation, the framework can consider the location of a collar casing (or other gamma ray source) as an additional uncertain state and probabilistically infer it together with the depth of the downhole tool string 220. This is similar to the so-called ID Simultaneous Localization and Mapping (SLAM) method commonly used for robot localization in the robotics community.

[0039] There are various realizations of the Bayes filters. For example, in certain embodiments, Gaussian-based Bayes filters can be used whereby the uncertainty of the parameters and the states are assumed to be characterized by Gaussian distributions. The forward measurement model can be assumed to be linear. In embodiments, the filters can include Kalman filters, extended Kalman filters (EKFs), unscented Kalman filters (UKFs), and information filters (Ifs). Alternatively, in other embodiments, non-Gaussian filters can be used to address highly nonlinear and non-Gaussian situations. Example non-Gaussian filters can include Particle filters (PFs) and the transport map method filters.

[0040] Alternatively, an optimization-based approach can be used to infer the system states and parameters from direct measurements (e.g., detected by the sensors 40, 46). In this approach, an objective function (e.g., the difference between the predicted vs. measured values) can be determined and minimized with respect to the system states and parameters. In this approach, characterization of the uncertainty of the inferred param eters/states can be done by checking the quality of the minimization/fitting. For example, a good fitting indicates less uncertainty. More rigorously, the curvature of the objective function around the optimal (i.e., fitted) point characterizes the confidence level of the inference. For example, large curvature means there will generally be a relatively large penalty when moving away from the optimal point, thus, the inferred parameters can be more certain.

[0041] Regardless of which approach (Bayes vs. optimization) is used, the anomaly detection results can be used to decide when to modify conveyance unit 212 operations. In some embodiments, the param eter/state inference process can be performed when the conveyance operation is under normal conditions. For example, before an operation begins, a friction coefficient can be set as 0.2, but during the operation, using for example the Bayesian framework, from the downhole and/or surface system states one of the one or more system state profiles might infer that the friction coefficient is around 0.4 plus or minus 0.1. With this new information, operations re-planning can take place, such as slowing the speed at which the elongated conveyance member 204 enters the wellbore 235 so that the elongated conveyance member 204 does not buckle within the wellbore 235.

[0042] FIG. 3 depicts a schematic of an illustrative computing system 512 for automating conveyance operations using an elongated conveyance member, according to one or more embodiments. The computer system 512 can be located within the surface facility 215 (See FIG. 2) or can be located elsewhere and can be integrated into or can have a command/control interface with the conveyance unit 212. One or more chips, for example chips 505 and/or 521, can be or can include field-programmable gate arrays (“FPGAs”), application specific integrated circuits (“ASICs”), chiplets, Multi-Chip-Modules, central processing units (“CPUs”), and/or system-on-chips (“SOCs”), to name a few. The chip can be used in a wide- range of applications, including but not limited to auto emission control, environmental monitoring, digital voice recorders, or other digital processing systems. ASICs can include entire microprocessors, memory blocks including read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory and other building blocks and can be known as system-on-chip (“SoC”).

[0043] To achieve its desired functionality, the computing system 512 can include various hardware and software components. Among these components can be one or more processors 514 and a conveyance operations events controller 540. These hardware components can be interconnected through the use of a number of electrical connections, busses, and/or network connections. In one embodiment, the processor 514, the chip 505, the chip 521, and the conveyance operations events controller 540 can be communicatively coupled via a bus 522. The bus 522 can be or include any know computing system bus. The conveyance operations events controller 540 can be internal to a data storage device 516.

[0044] The chip 505, the chip 521, and/or the conveyance operations events controller 540 can include, either separately or in some combination, software and hardware, including tangible, non-transitory computer readable medium (not shown), for estimating the location for one or more micro-seismic events within the subterranean formation. The conveyance operations events controller 540 can be integrated into the chip 505, the chip 521, and/or the processor 514. The chip 505 and/or the chip 521 can be integrated into the processor 514. Although conveyance operations events controller 540 is depicted as being internal to the data storage device 516, in other examples, the controller module 534 can be a peripheral device (not shown) coupled to the computing system 512 or included within a peripheral device (not shown) coupled to the computing system 512. In other examples, the conveyance operations events controller 540 can be a peripheral device (not shown) coupled to the computing system 512 or included within a peripheral device (not shown) coupled to the computing system 512. [0045] The conveyance operations events controller 540 can include instructions that when executed by the conveyance operations events controller 540 can cause the conveyance operations events controller 540 to implement at least the functionality of: modeling one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models; calibrating the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models; using the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states; and using one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, where the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, where the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states. In some embodiments, the instructions can, when executed by the conveyance operations events controller 540, cause the conveyance operations events controller 540 to use Bayes filtering to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models. In some embodiments, the instructions can, when executed by the conveyance operations events controller 540, cause the conveyance operations events controller 540 to use optimization-based analyses to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models. In some embodiments, the instructions can, when executed by the conveyance operations events controller 540, cause the conveyance operations events controller 540 to use a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on one of the calculated tension profile or the calculated force profile along the elongated conveyance member. In some embodiments, the instructions can, when executed by the conveyance operations events controller 540, cause the conveyance operations events controller 540 to use an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string based, at least in part, on at least one of the calculated surface tension and the uncertainty bounds around the calculated surface tension and the calculated surface weight and uncertainty bounds around the calculated surface weight.

[0046] In one or more embodiments, the conveyance operations events controller 540 can work in conjunction with the processor 514 to implement the functionality described above. In some embodiments, the conveyance operations events controller 540 can execute firmware code stored on the computing system 512, such as on the chip 505, the chip 521, and/or the processor 514. The functionality of the computing system 512 and/or the conveyance operations events controller 540 can be in accordance with the processes of the present specification described herein. In the course of executing code, the processor 514 and/or the conveyance operations events controller 540 can receive input from and provide output to a number of the remaining hardware units.

[0047] The computing system 512 can be implemented in an electronic device. Examples of electronic devices include servers, desktop computers, laptop computers, cloud based computers, personal digital assistants (“PDAs”), mobile devices, smartphones, gaming systems, and tablets, among other electronic devices. The computing system 512 can be utilized in any data processing scenario including, stand-alone hardware, mobile applications, through a computing network, or combinations thereof. Further, the computing system 512 can be used in a computing network, a public cloud network, a private cloud network, a hybrid cloud network, other forms of networks, or combinations thereof. In one example, the methods provided by the computing system 512 are provided as a service by a third party.

[0048] To achieve its desired functionality, the computing system 512 can include various other hardware components. Among these other hardware components can be a number of data storage devices or tangible, non-transitory computer readable medium 516, a number of peripheral device adapters 518, and a number of network adapters 520. These hardware components can be interconnected through the use of a number of electrical connections, busses, and/or network connections. In one example, the processor 514, data storage device 516, peripheral device adapters 518, and a network adapter 520 can be communicatively coupled via a bus, for example the bus 522 as depicted in FIG. 3 or via a separate bus, not shown.

[0049] The chip 505, the chip 521, and/or the processor 514 can include the hardware and/or firmware/software architecture to retrieve executable code from the data storage device 516 and execute the executable code. The executable code can, when executed by the chip 505, the chip 521, and/or the processor 514, cause the chip 505, the chip 521, and/or the processor 514 to implement at least the functionality of: modeling one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models; calibrating the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models; using the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states; and using one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, where the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, where the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

[0050] The data storage device 516 can store data such as executable program code that can be executed by the processor 514, the conveyance operations events controller 540 , or other processing devices. The processor 514 can be a central processing unit that can be configured to execute an operating system in the computing system 512. As will be discussed, the data storage device 516 can specifically store computer code representing a number of applications that the processor 514 and/or the conveyance operations events controller 540 can execute to implement at least the functionality described herein.

[0051] Referring now to FIGS. 2 and 3, in one or more embodiments, depending on the type of downhole operation, the downhole system states and surface system states can be combined and processed through executable code such that the conveyance operations events controller 540 can prevent stalls and facilitate stall recovery with respect to the downhole tool string 220. In addition, in certain embodiments, processing of the downhole system states and surface system states can also facilitate cooperative operation of the elongated conveyance member string 210, along with other support equipment. This cooperation provides synergy that facilitates output of advisory information and/or automation of the downhole process, as well as appropriate adjustment of the rate of penetration and/or rate of extraction and pump rates for each individual stage of the operation. It should be noted that the downhole system states and the surface system states also can be used to provide advisory information and/or automation of surface processes, such as pumping processes performed by the elongated conveyance member string 210, other surface equipment, and so forth.

[0052] In some embodiments, optimum downhole weight on bit and torque can be provided to the conveyance operations events controller 540. Real-time modeling, based on the downhole system states and surface system states, can enable improved prediction of weight on bit, torque, and/or pressure differentials. Such modeling can also enable the downhole process to be automated and automatically optimized. The downhole system states also can be used to predict wear on components of the downhole tool string 220, and to advise as to timing of the next trip to the surface for replacement of worn components.

[0053] In some embodiments, downhole system states, such as weight on bit, torque data from a load module associated with the downhole tool string 220, and/or bottom hole pressures (internal and/or external) to the downhole tool string 220 can be processed and the conveyance operations events controller 540 can control the downhole tool string 220 to generate a faster and more controlled rate of penetration. Additionally, the downhole system states can be updated as the downhole tool string 220 is moved to different positions along the wellbore 235 to help optimize operations. The downhole system states can also enable automation of the downhole processed through automated controls via control instructions stored with the conveyance operations events controller 540.

[0054] In one or more embodiments, downhole system states can be combined with surface system states and/or other measured or stored surface data to provide an automated control system within the conveyance operations events controller 540 to optimize the rate of penetration of the downhole tool string 220 . In one embodiment, surface system states can include hanging weight of the elongated conveyance member 204, speed of the elongated conveyance member 204, wellhead pressure, choke and flow back pressures, return pump rates, circulating pressures, and pump rates.

[0055] In certain embodiments, the downhole and surface system states can be processed by the computer system 512 during use of the downhole tool string 220 to enable automatic (e.g., without human intervention) optimization with respect to use of the downhole tool string 220 during subsequent stages of operation of the downhole tool string 220.

[0056] In certain embodiments, depending on the type of operation downhole, the conveyance operations events controller 540 can be programmed with a variety of algorithms and/or modeling techniques to achieve desired results. For example, the downhole data and surface data can be combined and at least some of the data can be updated in real time by the computer system 512. This updated data can be processed by the computer system 512 via suitable algorithms in the conveyance operations events controller 540 to enable automation and to improve the performance of, for example, downhole tool string 220. By way of example, the data can be processed and used by the computer system 512 for preventing motor stalls. In certain embodiments, downhole system states such as forces, torque, and/or pressure differentials can be combined by the conveyance operations events controller 540 to enable prediction of a stall of a downhole component and/or to give a warning to a supervisor. In such embodiments, the computer system 512 can be programmed to make self-adjustments (e.g., within the conveyance operations events controller 540) to adjust, for example, a speed of an injector head and/or pump pressures to prevent the stall, and to ensure efficient continuous operation.

[0057] In addition, in certain embodiments, the data and the ongoing collection of data can be used by conveyance operations events controller 540 to monitor various aspects of the performance of downhole components. For example, various algorithms can be used by the conveyance operations events controller 540 to help a supervisor on site to predict, for example, how many more hours one or more downhole components can be run efficiently. This data, and the appropriate processing of the data, can be used by the computer system 512 to make automatic decisions or to provide indications to a supervisor as to when to pull the elongated conveyance member string 210 to the surface 217.

[0058] In certain embodiments, downhole data and surface data also can be processed via the computer system 512 to predict when the elongated conveyance member string 210 may become stuck. The ability to predict when the elongated conveyance member string 210 may become stuck helps avoid unnecessary short trips and, thus, improves longevity of the elongated conveyance member 204. In certain embodiments, downhole system states such as forces, torque, and pressure differentials in combination with surface system states such as weight of the elongated conveyance member 204 or the elongated conveyance member string 210, speed of the elongated conveyance member 204, pump rate, and circulating pressure can be processed via the computer system 512 to provide predictions from the conveyance operations events controller 540 as to when the elongated conveyance member 204 may become stuck.

[0059] In certain embodiments, conveyance operations events controller 540 can be programmed to provide warnings to a supervisor and/or to self-adjust (e.g., automatically, without human intervention) either the speed of an injector head, the pump pressures and rates of a pump unit, or a combination of both, so as to prevent the elongated conveyance member 204 from getting stuck. By way of example, the warnings or other information can be output to a display of the computer system 512 to enable an operator to make better, more informed decisions regarding downhole or surface processes related to operation of the downhole tool string 220. In certain embodiments, the speed of the injector head can be controlled via the conveyance operations events controller 540 by controlling the slack-off force from the surface. In general, the ability to predict and prevent the elongated conveyance member 204 from becoming stuck substantially improves the overall efficiency, and helps avoid unnecessary short trips if the probability of the elongated conveyance member 204 getting stuck is minimal. Accordingly, the downhole data and surface data can be used by the conveyance operations events controller 540 to provide advisory information and/or automation of surface processes, such as pumping processes or other processes.

[0060] In one or more embodiments, the conveyance operations events controller 540 can include a probabilistic tension/force and depth estimation executable code package; an anomaly detection executable code package; and/or a mechanical failure check executable code package. These three packages can be software packages executable by the computer system 512, as described in greater detail herein.

[0061] The probabilistic tension/force and depth estimation executable code package can predict the tensions/forces along the elongated conveyance member 204 and/or the surface weight and the depth of the downhole tool string 220. In order to perform such predictions, the conveyance operations events controller 540 can receive certain parameters as inputs. Among these parameters, some may be relatively difficult to measure directly, for example, the friction coefficient between the elongated conveyance member 204 and/or the downhole tool string 220 and the casing 225. The conveyance operations events controller 540 can use real-time measurements to automatically infer undetectable states and detectable states (e.g., current tool depth in the wellbore 235). In one or more embodiments, either direct measurement of the depth, via the downhole sensor package 221 and/or the surface sensors 211, or the use of a depth estimation model can be used by the conveyance operations events controller 540 to determine the current depth of the downhole tool string 220. In certain embodiments, the depth of the downhole tool string 220 can be measured from surface depth encoders that can be included in the surface sensors 211 and the surface depth encoders can record the length of the elongated conveyance member 204 passing through the surface sensors 211. In some embodiments, deformation and/or elongation of the elongated conveyance member 204 due to external forces after it leaves the surface depth encoders can be ignored. In addition, in certain embodiments, measurement uncertainty due to sensor noise can be ignored. In certain embodiments, the downhole data can be used instead of the surface data when the downhole data are determined to be more trustworthy as compared to the surface data. In certain embodiments, an operator may toggle between the two types of measurements as desired.

[0062] In certain embodiments, such inference may be performed iteratively by the conveyance operations events controller 540 whenever the anomaly detection package indicates that the current operation is under normal conditions or not. In certain embodiments, the outputs from the conveyance operations events controller 540 can include the predicted system states and the uncertainties around those predicted system states. The updated system states can then be used by the conveyance operations events controller 540 to predict the forces along the elongated conveyance member 204 as well as the surface weight with uncertainty bounds.

[0063] The anomaly detection executable code package can use statistical methods to compare the predicted and measured surface weight or tension to detect anomalies during operations (e.g., stuck pipe that leads to an increase in the measured surface weight or decrease in the measured surface tension, or tubing lockup that leads to a sudden weight/tension drop). This anomaly detection package can consider not only the absolute difference between the prediction and measurement, but also the uncertainty around the prediction, the measurement uncertainty due to noise of certain sensors within the downhole sensor package 221 or the surface sensors 211, and trends of the surface weight/tension (e.g., is it increasing, decreasing, stabilizing, etc.). From these factors, the anomaly detection package can automatically classify whether the current operation is under normal conditions or not.

[0064] Instead of relying on an operator to monitor the downhole and surface states, the anomaly detection executable code package can use statistical methods to compare the measured versus predicted surface states to automatically detect anomalies. In one or more embodiments, for the elongated conveyance member, e.g., coiled tubing, uncertainty bounds can be used with surface weight predictions by the conveyance operations events controller 540 utilizing statistical methods to determine the likelihood of anomalies. In certain embodiments, anomaly detection is enabled not only based on single-point prediction versus measurement comparisons but can also be based on trends to track if measured values are going up or down, how fast the values are changing, and if the values are stabilizing, to make automated decisions based on the trends.

[0065] In certain embodiments, the conveyance operations events controller 540 can create an array to record the predicted and measured system states. The records in this array can be based on depth instead of time. As the elongated conveyance member 204 moves, the operations events controller 540 can populate/update the array with the predicted and measured surface weight at the current depth. Then, the operations events controller 540 can calculate the difference in trend (e.g., prediction vs. measurement) around the current depth as well as the absolute difference between the predicted vs. measured weight at the current depth. Then, the operations events controller 540 can plot these two differences onto a two-dimensional plane and classify normal operation versus anomalies by drawing boundaries on this plane. For example, if the absolute difference is relatively significant, most likely there is an anomaly event regardless of the trend. On the other hand, if the trend agrees well, a moderate difference in the absolute weight may be acceptable. Initially, default boundaries may be defined by the operations events controller 540. However, as more data is collected by the sensors 211, 221, the boundaries may be automatically updated by the operations events controller 540 based on the historical data. In certain embodiments, the operations events controller 540 may store the calculated differences as a history record which may be indexed by the current depth and the current running direction.

[0066] Finally, the mechanical failure check executable code package can use the predicted tension/force to monitor different failure modes (e.g., burst, collapse, fatigue, buckling, etc.) to ensure that the elongated conveyance member 204 is operating within its safety envelope. The mechanical failure check executable code package can be used by the conveyance operations events controller 540 to check for potential mechanical failures of the elongated conveyance member 204 by automatically monitoring the system states of the elongated conveyance member 204 to make sure it is working within its safety envelope. Many various potential failure modes may be modeled. For example, in certain embodiments, maximum tubing stress of the elongated conveyance member 204 may be monitored and maintained smaller than a maximum allowed value. In addition, in certain embodiments, tubing burst and collapse of the elongated conveyance member 204 may be guarded against. Under axial load and differential pressures across the walls of the elongated conveyance member 204, the elongated conveyance member 204 could potentially burst or collapse. The mechanical failure check executable code package can monitor the load and the pressures on the elongated conveyance member 204 to ensure that the elongated conveyance member 204, e.g., coiled tubing is safe. In addition, in certain embodiments, tubing fatigue of the elongated conveyance member 204 may be minimized. In some embodiments, fatigue models may be used by the conveyance operations events controller 540 to monitor the fatigue life of the elongated conveyance member 204, and also to optimize the operation (e.g., optimize the locations where a pull test may be performed) to avoid repeatedly putting fatigue on the elongated conveyance member 204.

[0067] In certain embodiments, potential buckling and lockup of the elongated conveyance member 204 may be minimized. Running the elongated conveyance member 204 into a deviated wellbore 235 can cause compression in the elongated conveyance member 204 because of friction. Under relatively large compressive loads, the elongated conveyance member 204 could tangle or buckle. For example, for coiled tubing, the first buckle mode is named sinusoidal buckling, in which the elongated conveyance member 204 snakes along the bottom of the wellbore 235. This is a relatively benign failure mode as the operation can still continue without problem. However, if the elongated conveyance member 204 continues to be pushed into the wellbore 235, a second buckle mode named helical buckling may be initiated, in which the elongated conveyance member 204 may deform into helices pressing against the inner wall of the casing/wellbore trying to expand radially to release the compression. In this second failure mode, tubing-casing/wellbore contact and frictional forces may increase rapidly. If this second failure mode is left unattended, the elongated conveyance member 204 may lock up, which should be avoided. Buckling and lockup may be monitored by the conveyance operations events controller 540 to check the tubing force profile. Using the real-time estimated friction coefficients, the conveyance operations events controller 540 can update the estimated lockup length (because it depends on the friction coefficient). If the current depth is near the updated lockup length, the operation can be slowed by the conveyance operations events controller 540, by sending an appropriate control signal to the conveyance unit 212, while the surface weight continues to be monitored by the conveyance operations events controller 540 to detect any sign of lockup of the elongated conveyance member 204. In certain embodiments, pump friction reducers may be injected to reduce the friction and increase the lockup length.

[0068] Referring again to FIG. 3, in one or more embodiments, the data storage device 516 can include various types of memory modules, including volatile and nonvolatile memory. In one or more embodiments, the data storage device 516 of the present example can include Random Access Memory (“RAM”) 524, Read Only Memory (“ROM”) 526, and Hard Disk Drive (“HDD”) storage 528. Many other types of memory can also be utilized, and the present specification contemplates the use of many varying type(s) of memory in the data storage device 516 as can suit a particular application of the principles described herein. In certain examples, different types of memory in the data storage device 516 can be used for different data storage requirements. In one or more embodiments, in certain examples the processor 514 can boot from Read Only Memory (“ROM”) 526, maintain nonvolatile storage in the Hard Disk Drive (“HDD”) memory 528, and execute program code stored in Random Access Memory (“RAM”) 524. In some examples, the chip 505 and the chip 521 can boot from the Read Only Memory (“ROM”) 526.

[0069] The data storage device 516 can include a computer readable medium, a computer readable storage medium, or a non-transitory computer readable medium, among others. In one or more embodiments, the data storage device 516 can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, for example, the following: an electrical connection having a number of wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM, a Flash memory, a portable compact disc read only memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer readable storage medium can be any tangible medium that can contain or store computer usable program code for use by or in connection with an instruction execution system, apparatus, or device. In another example, a computer readable storage medium can be any non-transitory medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0070] The hardware adapters 518, 520 in the computing system 512 can enable the processor 514 to interface with various other hardware elements, external and internal to the computing system 512. In one or more embodiments, the peripheral device adapters 518 can provide an interface to input/output devices, such as, for example, a display device 530, a mouse, and/or a keyboard. The peripheral device adapters 518 can also provide access to other external devices such as an external storage device, a number of network devices such as, for example, servers, switches, and routers, client devices, other types of computing devices, and combinations thereof.

[0071] The display device 530 can be provided to allow a user of the computing system 512 to interact with and implement the functionality of the computing system 512. Examples of display devices 530 can include a computer screen, a laptop screen, a mobile device screen, a personal digital assistant (“PDA”) screen, and/or a tablet screen, among other display devices 530.

[0072] The peripheral device adapters 518 can also create an interface between the processor 514 and the display device 530, a printer, or other media output devices. The network adapter 520 can provide an interface to other computing devices within, for example, a network, thereby enabling the transmission of data between the computing system 512 and other devices located within the network. The network adapter 520 can provide an interface to an external telecommunications network such as a cellular phone network or other radio frequency enabled network, thereby enabling the transmission of data between the computing system 512 and other external devices such as an external storage device, a number of network devices such as, for example, servers, switches, and routers, client servers, radio frequency enabled devices, other client devices, other types of computing devices, and combinations thereof.

[0073] The computing system 512 can further include a number of modules used in the implementation of the process and systems described herein. The various modules within the computing system 512 can include executable program code that can be executed separately. In this example, the various modules can be stored as separate computer program products. In another example, the various modules within the computing system 512 can be combined within a number of computer program products; each computer program product including a number of the modules.

[0074] FIG. 4 depicts an illustrative computing device that includes the conveyance operations events controller 540 and a non-transitory computer-readable medium 602 that includes computer executable instructions 600 stored thereon within the computing system 512 shown in FIG. 3, according to one or more embodiments. Referring to FIGS. 1, 2, and 4, when the computer executable instructions 600 are executed by the conveyance operations events controller 540, the computer executable instructions 600 can cause the conveyance operations events controller 540 to model one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models (Block 103); calibrate the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models (Block 104); use the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states (Block 105); use one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated elongated conveyance member tension profile, wherein the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states (Block 106); or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, wherein the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states (Block 107). In some embodiments, the process 100 can further include using Bayes filtering to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models (Block 108). When the computer executable instructions 600 are executed by the conveyance operations events controller 540, the computer executable instructions 600 can cause the conveyance operations events controller 540 to use optimization-based analyses to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models (Block 109). When the computer executable instructions 600 are executed by the conveyance operations events controller 540, the computer executable instructions 600 can cause the conveyance operations events controller 540 to use a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on one of the calculated tension profile or the calculated force profile along the elongated conveyance member (Block 110). When the computer executable instructions 600 are executed by the conveyance operations events controller 540, the computer executable instructions 600 can cause the conveyance operations events controller 540 to use an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string 220 based, at least in part, on at least one of the calculated surface tension and the uncertainty bounds around the calculated surface tension and the calculated surface weight and uncertainty bounds around the calculated surface weight (Block 111).

[0075] The present disclosure further relates to any one or more of the following numbered paragraphs:

[0076] 1. A process for automating a conveyance operation using an elongated conveyance member, that can include: modeling one or more system state profiles of a downhole tool string during a conveyance operation within a wellbore to define one or more profile models; calibrating the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models; using the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states; and using one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, wherein the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, wherein the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

[0077] 2. The process of paragraph 1, wherein the elongated conveyance member is a wireline, a slickline, or a coiled tubing.

[0078] 3. The process of paragraph 1 or 2, wherein the elongated conveyance member is a wireline or a slickline and the one or more system state profiles comprise a speed profile, a tension profile, and a depth profile.

[0079] 4. The process of any one of paragraphs 1 to 3, wherein the one or more system state profiles comprise at least one of a tension profile and a force profile.

[0080] 5. The process of any one of paragraphs 1 to 4, wherein the one or more detectable system states comprise a depth of the downhole tool string within the wellbore, a speed of the downhole tool string within the wellbore, or a combination thereof.

[0081] 6. The process of any one of paragraphs 1 to 5, wherein the one or more undetectable system states comprise a friction coefficient between the downhole tool string and the wellbore, a surface friction, a stripper friction, a fluid level, a wellbore fluid density, or a combination thereof.

[0082] 7. The process of any one of paragraphs 1 to 6, wherein using the one or more inference models to infer the one or more detectable system states and/or the one or more undetectable system states comprises using Bayes filtering.

[0083] 8. The process of any one of paragraphs 1 to 7, wherein using the one or more inference models to infer the one or more detectable system states and/or the one or more undetectable system states comprises using optimization-based analyses.

[0084] 9. The process of any one of paragraphs 1 to 8, further including using a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on one of the calculated tension profile or the calculated force profile along the elongated conveyance member.

[0085] 10. The process of any one of paragraphs 1 to 9, further including using an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string based, at least in part, on at least one of the calculated surface tension and the uncertainty bounds around the calculated surface tension, and the calculated surface weight and uncertainty bounds around the calculated surface weight.

[0086] 11. A system including: a network adapter configured to acquire sensor data from sensors during a conveyance operation within a wellbore using an elongated conveyance member; and at least one processor configured to execute an executable code to implement at least the functionality of modeling one or more system state profiles of a downhole tool string during the conveyance operation to define one or more profile models, wherein the at least one processor is further configured to: calibrate the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models; use the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states; and use one of (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated elongated conveyance member tension profile, wherein the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, wherein the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

[0087] 12. The system of paragraph 11, wherein the at least one processor is configured to execute the executable code to use Bayes filtering to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models.

[0088] 13. The system of paragraph 11 or 12, wherein the at least one processor is configured to execute the executable code to use optimization-based analyses to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models.

[0089] 14. The system of any one of paragraphs 11 to 13, further including executable code configured to be executed by the at least one processor to use a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on the calculated tension profile along the elongated conveyance member. [0090] 15. The system of any one of paragraphs 11 to 14, further including executable code configured to be executed by the at least one processor to use an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string based, at least in part, on the calculated surface tension and the uncertainty bounds around the calculated surface tension.

[0091] 16. A system including: at least one processor configured to execute an executable code to implement at least the functionality of modeling one or more system state profiles of a downhole tool string during a conveyance operation via an elongated conveyance member within a wellbore to define one or more profile models, wherein the at least one processor is further configured to: calibrate the one or more profile models using one or more inference models by inferring one or more detectable system states and/or one or more undetectable system states related to running the downhole tool string into the wellbore via the elongated conveyance member to determine one or more calibrated profile models; use the one or more calibrated profile models to calculate at least one of a tension profile and a force profile along the elongated conveyance member based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states; and use one of: (i) a surface tension uncertainty quantification model to calculate a surface tension and uncertainty bounds around the calculated surface tension based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated tension profile, wherein the surface tension uncertainty quantification model accounts for one or more uncertainties related to the one or more detectable system states and/or one or more undetectable system states; or (ii) a surface weight uncertainty quantification model to calculate a surface weight and uncertainty bounds around the calculated surface weight based, at least in part, on the one or more detectable system states and/or the one or more undetectable system states, and the calculated force profile, wherein the surface weight uncertainty quantification model accounts for one or more uncertainties related to the one or undetectable system states.

[0092] 17. The system of paragraph 16, wherein the at least one processor is configured to execute the executable code to use Bayes filtering to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models.

[0093] 18. The system of paragraph 16 or 17, wherein the at least one processor is configured to execute the executable code to use optimization-based analyses to infer the one or more detectable system states and/or the one or more undetectable system states using the one or more inference models. [0094] 19. The system of any one of paragraphs 16 to 18, further including executable code configured to be executed by the at least one processor to use a mechanical failure model to automatically monitor a mechanical safety of the elongated conveyance member based, at least in part, on the calculated tension profile along the elongated conveyance member.

[0095] 20. The system of any one of paragraphs 16 to 19, further including executable code configured to be executed by the at least one processor to use an anomaly detection model to automatically detect downhole anomalies relating to the downhole tool string based, at least in part, on the calculated surface tension and the uncertainty bounds around the calculated surface tension.

[0096] Certain embodiments and features have been described using a set of numerical upper limits and a set of numerical lower limits. It should be appreciated that ranges including the combination of any two values, e.g., the combination of any lower value with any upper value, the combination of any two lower values, and/or the combination of any two upper values are contemplated unless otherwise indicated. Certain lower limits, upper limits and ranges appear in one or more claims below. All numerical values are "about" or "approximately" the indicated value, and take into account experimental error and variations that would be expected by a person having ordinary skill in the art.

[0097] Various terms have been defined above. To the extent a term used in a claim is not defined above, it should be given the broadest definition persons in the pertinent art have given that term as reflected in at least one printed publication or issued patent. Furthermore, all patents, test procedures, and other documents cited in this application are fully incorporated by reference to the extent such disclosure is not inconsistent with this application and for all jurisdictions in which such incorporation is permitted.

[0098] Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications can be possible in the examples without materially departing from this subject disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw cannot be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw can be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. § 112, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function.

[0099] While certain preferred embodiments of the present invention have been illustrated and described in detail above, it can be apparent that modifications and adaptations thereof will occur to those having ordinary skill in the art. It should be, therefore, expressly understood that such modifications and adaptations may be devised without departing from the basic scope thereof, and the scope thereof can be determined by the claims that follow.