Calibrating friction coefficient and rock fracture characteristics

Abstract

A method for calibrating a model of a subterranean formation includes capturing one or more measurements at a surface extending to a wellbore in the subterranean formation. The measurements include a surface torque (STOR) on a drill string extending into the wellbore and a surface bit pressure (SWOB) on the drill string. The method further includes determining a coefficient of friction based on the STOR when the drill bit is off-bottom in the wellbore. The drill bit is coupled to a lower end of the drill string. The method also includes determining a downhole torque (DTOR) on the drill bit and a downhole bit pressure (DWOB) on the drill bit when the drill bit is bottomed in the wellbore. The method further includes identifying a rock type in the subterranean formation based at least in part on the coefficient of friction, the DTOR, and the DWOB.

Description

Cross-reference to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 578,477, filed August 24, 2023, which is incorporated herein by reference. Background Technology

[0002] For years, 4D simulations have been used for product design and cause analysis. There is a growing demand for automated workflows that provide standardized, efficient calibration processes for well site use. More specifically, this would help reduce reliance on manual adjustments, improve the accuracy of simulation inputs, establish strong correlations between simulation results and field measurements, and enhance drilling performance prediction and product development. Summary of the Invention

[0003] A method for calibrating a model of subsurface formations is disclosed. The method includes capturing one or more measurements at the surface of a wellbore extending into the subsurface formation. The measurements include surface torque (STOR) and surface weight on the drill string extending into the wellbore. The method further includes determining a coefficient of friction based on STOR as the drill bit is leaving the bottom of the wellbore. The drill bit is attached to the lower end of the drill string. The method also includes determining downhole torque (DTOR) and downhole weight on the drill bit (DWOB) as the drill bit touches the bottom of the wellbore. The method further includes identifying rock types in the subsurface formation based at least in part on the coefficient of friction, DTOR, and DWOB.

[0004] In another embodiment, the method includes capturing one or more measurements at the surface of the wellbore. The wellbore extends into the subsurface formation. The measurements include the rotational speed per minute (RPM) of the drill string extending into the wellbore, the surface torque (STOR) on the drill string, the surface weight on the drill string (SWOB), and the rate of drilling (ROP) of the drill string. The method also includes determining the coefficient of friction based on STOR as the drill bit is leaving the bottom of the wellbore. The drill bit is attached to the lower end of the drill string. The method further includes determining the downhole torque (DTOR) and downhole weight on the drill bit (DWOB) as the drill bit touches the bottom of the wellbore. DTOR and DWOB are determined based on STOR, SWOB, and the coefficient of friction. The method also includes simulating the drill bit touching the bottom and drilling through various different rock types in a model to generate multiple simulated drill bit torques (BTOR) and multiple simulated drill bit weights (BWOB). The method further includes comparing DTOR with BTOR to generate a first comparison. The method further includes comparing DWOB with BWOB to generate a second comparison. The method further includes identifying one of the rock types based on a first comparison and a second comparison. The identified rock type has minimal difference between DTOR and BTOR, DWOB and BWOB, or both. The method further includes determining the simulated surface torque (STOR') and simulated surface bit weight (SWOB') on the drill string based on the identified rock type. The method further includes comparing STOR with STOR' to generate a third comparison. The method further includes comparing SWOB with SWOB' to generate a fourth comparison. The method further includes calibrating the friction coefficient based on the third and fourth comparisons to generate a calibrated friction coefficient. The method further includes calibrating the rock type based on the third and fourth comparisons to generate a calibrated rock type. The method further includes performing well site actions based on the calibrated friction coefficient and the calibrated rock type. Well site actions include changing the RPM number, changing STOR, changing SWOB, changing ROP, or a combination thereof.

[0005] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include capturing one or more measurements at the surface of a wellbore extending into subsurface formations. The measurements include the rotational speed per minute (RPM) of the drill string extending into the wellbore, the surface torque (STOR) on the drill string, the surface weight on the drill string (SWOB), and the rate of drilling (ROP) of the drill string. The operations also include determining the coefficient of friction based on STOR when the drill bit is leaving the bottom of the wellbore. The drill bit is attached to the lower end of the drill string. The operations also include determining the downhole torque (DTOR) and downhole weight on the drill bit (DWOB) when the drill bit touches the bottom of the wellbore. DTOR and DWOB are determined based on STOR, SWOB, and the coefficient of friction. The operation also includes simulating drill bit bottoming through various rock types in a model to generate multiple simulated drill bit torques (DTOR) and multiple simulated drill bit pressures (DWOB). The operation further includes comparing DTOR with BTOR to generate a first comparison. The operation also includes comparing DWOB with BWOB to generate a second comparison. The operation further includes identifying one of the rock types based on the first and second comparisons. The identified rock type has minimal difference between DTOR and BTOR, DWOB and BWOB, or both. The operation further includes determining simulated surface torque (STOR') and simulated surface pressures (SWOB') on the drill string based on the identified rock type. The operation further includes comparing STOR with STOR' to generate a third comparison. The operation further includes comparing SWOB with SWOB' to generate a fourth comparison. The operation further includes calibrating the friction coefficient based on the third and fourth comparisons to generate a calibrated friction coefficient. The operation further includes calibrating the rock type based on the third and fourth comparisons to generate a calibrated rock type. The operation further includes performing well site actions based on the calibrated friction coefficient and the calibrated rock type. Well site actions include changing the RPM, changing the STOR, changing the SWOB, changing the ROP, or combinations thereof.

[0006] A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include capturing one or more measurements at the surface of a wellbore extending into subsurface formations. The wellbore extends into the wellbore. The measurements include the rotational speed per minute (RPM) of the drill string extending into the wellbore, the surface torque (STOR) on the drill string, the surface bit pressure (SWOB) on the drill string, and the drilling rate (ROP) of the drill string. The operations also include determining the coefficient of friction based on STOR when the drill bit is leaving the bottom of the wellbore. The drill bit is attached to the lower end of the drill string. The operations also include determining the downhole torque (DTOR) and downhole bit pressure (DWOB) on the drill bit when the drill bit touches the bottom of the wellbore. DTOR and DWOB are determined based on STOR, SWOB, and the coefficient of friction. The operations also include simulating the drill bit touching the bottom and drilling through various different rock types in a model to generate multiple simulated drill bit torques (BTOR) and multiple simulated drill bit pressures (BWOB). The operation also includes comparing DTOR with BTOR to generate a first comparison. The operation further includes comparing DWOB with BWOB to generate a second comparison. The operation also includes identifying one of the rock types based on the first and second comparisons. The identified rock type has minimal difference between DTOR and BTOR, DWOB and BWOB, or both. The operation also includes determining the simulated surface torque (STOR') and simulated surface bit weight (SWOB') on the drill string based on the identified rock type. The operation also includes comparing STOR with STOR' to generate a third comparison. The operation also includes comparing SWOB with SWOB' to generate a fourth comparison. The operation also includes calibrating the friction coefficient based on the third and fourth comparisons to generate a calibrated friction coefficient. The operation also includes calibrating the rock type based on the third and fourth comparisons to generate a calibrated rock type. The operation also includes performing well site actions based on the calibrated friction coefficient and the calibrated rock type. Well site actions include changing the RPM number, changing STOR, changing SWOB, changing ROP, or a combination thereof. Attached Figure Description

[0007] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the teachings and, together with the description, serve to explain the principles of the teachings. In the drawings: Figure 1 An example of a system according to one embodiment is shown, which includes various management components for managing various aspects of the geological environment.

[0008] Figure 2 A 4D finite element analysis (FEA) drilling simulation based on one implementation is shown.

[0009] Figure 3A and Figure 3B The bottom hole pattern and drill bit force are shown according to one implementation scheme.

[0010] Figure 4 A flowchart of a simulation calibration according to one implementation scheme is shown.

[0011] Figure 5 A schematic diagram of an automated calibration workflow according to one implementation scheme is shown.

[0012] Figure 6 A diagram illustrating various drilling conditions is shown according to one implementation scheme.

[0013] Figure 7 The log of multiple field measurements according to one implementation scheme is shown.

[0014] Figures 8A to 8M Several graphs illustrating the extraction of calibration point data from field measurements are shown according to one embodiment.

[0015] Figure 9 A diagram showing multiple calibration points is shown according to one embodiment.

[0016] Figure 10A and Figure 10B A graph showing the friction coefficient calibration results obtained using the gradient descent method according to one embodiment is shown.

[0017] Figures 11A to 11C A graph illustrating the rock and multiplier calibration results according to one embodiment is shown.

[0018] Figure 12 The final verification results based on one implementation scheme through multiple iterations are shown.

[0019] Figures 13A to 13C A graph illustrating calibration results from multiple resources (wells) is shown according to one embodiment.

[0020] Figure 14 A flowchart is shown of a method for calibrating a model of underground strata according to one embodiment.

[0021] Figure 15A and Figure 15B A schematic diagram of the method according to one embodiment is shown.

[0022] Figures 16A to 16C A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., determining and / or calibrating the friction coefficient of a downhole tool as it transitions from bottom-out mode) is shown.

[0023] Figures 17A to 17C A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., predicting the pressure on the drill bit (WOB) and the torque on the drill bit based on the coefficient of friction) is shown.

[0024] Figure 18 A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., determining rock type) is shown.

[0025] Figures 19A to 19D A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., determining rock type) is shown.

[0026] Figure 20 A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., verifying the accuracy of the determined friction coefficient and rock type) is shown.

[0027] Figure 21A and Figure 21B A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., improving the accuracy of the selected rock type) is shown.

[0028] Figures 22A to 22D A more detailed schematic diagram of a portion of the method according to one embodiment (e.g., analyzing drilling data from Ligen) is shown.

[0029] Figure 23 A schematic diagram of a computing system according to one implementation scheme is shown. Detailed Implementation

[0030] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0031] It should also be understood that although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this disclosure, a first object or step may be referred to as a second object or step, and similarly, a second object or step may be referred to as a first object or step. The first object or step and the second object or step are each an object or step, but they should not be regarded as the same object or step.

[0032] The terminology used in this specification is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and covers any possible combination of one or more of the associated listed items. It should also be understood that the terms “comprising” and / or “including” as used in this specification specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Furthermore, as used herein, the term “if” can be interpreted as meaning “when” or “after” or “in response to determination” or “in response to detection,” depending on the context.

[0033] This document focuses on the processing procedures, methods, techniques, and workflows according to some implementation schemes. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein can be combined and / or the order of some operations can be changed.

[0034] Figure 1 An example of system 100 is shown, which includes various management components 110 for managing various aspects of a geological environment 150 (e.g., an environment including a sedimentary basin, reservoir 151, one or more faults 153-1, one or more geological bodies 153-2, etc.). For example, management components 110 may allow direct or indirect management of sensing, drilling, injection, extraction, etc., of the geological environment 150. Subsequently, additional information about the geological environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0035] exist Figure 1 In the example, management component 110 includes seismic data component 112, supplementary information component 114 (e.g., well / logging data), processing component 116, simulation component 120, attribute component 130, analysis / visualization component 142, and workflow component 144. During operation, seismic data and other information provided by components 112 and 114 can be input into simulation component 120.

[0036] In an exemplary embodiment, simulation component 120 may depend on entity 122. Entity 122 may include earth entities or geological objects, such as wells, surfaces, bodies, reservoirs, etc. In system 100, entity 122 may include a virtual representation of an actual physical entity reconstructed for simulation purposes. Entity 122 may include entities based on data acquired via sensing, observation, etc. (e.g., seismic data 112 and / or other information 114). Entities may be characterized by one or more properties (e.g., a geometric strut mesh entity of an earth model may be characterized by porosity properties). Such properties may represent one or more measurements (e.g., acquired data), calculation results, etc.

[0037] In an exemplary implementation, simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on predefined classes to facilitate modeling and simulation. A commercial example of an object-based framework is MICROSOFT. ® .NET ® The framework (Redmond, Washington) provides a set of extensible object classes within the .NET framework. ® Within the framework, object classes encapsulate modules of reusable code and associated data structures. Object classes can be used to instantiate object instances for use by programs, scripts, etc. For example, a borehole class can define an object representing a borehole based on well data.

[0038] exist Figure 1 In the example, simulation component 120 can process information to conform to one or more attributes specified by attribute component 130, which may include an attribute library. This processing can occur before the input is given to simulation component 120 (e.g., consider processing component 116). As an example, simulation component 120 can perform operations on input information based on one or more attributes specified by attribute component 130. In an exemplary embodiment, simulation component 120 can construct one or more models of geological environment 150, which can be relied upon to simulate the behavior of geological environment 150 (e.g., in response to one or more behaviors, whether natural or man-made). Figure 1 In the example, the analysis / visualization component 142 may allow interaction with the model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input into one or more other workflows, as indicated by the workflow component 144.

[0039] As an example, simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (Schlumberger Limited, Houston, Texas) or the INTERSECT™ reservoir simulator (Schlumberger Limited, Houston, Texas). As an example, simulation components, simulators, etc., may include features for implementing one or more meshless techniques (e.g., for solving one or more equations). As an example, one or more reservoirs may be simulated with respect to one or more enhanced exploitation techniques (e.g., considering thermal processes such as SAGD).

[0040] In an exemplary embodiment, the management component 110 may include, for example, PETREL. ® Features of a commercially available framework for earthquake simulation software (Schlumberger, Houston, Texas). PETREL ® The framework provides components that allow for optimized exploration and mining operations. PETREL ® The framework includes seismic-to-simulation software components that can output information to improve reservoir performance, for example, by enhancing the productivity of asset teams. Using this framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. This framework can be considered an application and can also be considered a data-driven application (e.g., in cases where data is input for modeling, simulation, etc.).

[0041] In an exemplary embodiment, various aspects of the management component 110 may include add-ons or plug-ins that operate according to the specifications of the framework environment. For example, a product with the trade name OCEAN. ® The commercially available framework environment (Schlumberger, Houston, Texas) allows for the integration of add-ons (or plug-ins) into Petrel. ® In the framework workflow. OCEAN ® Framework environment utilizes .NET ® The tool (Microsoft Corporation, Redmond, Washington) provides a stable, user-friendly interface for efficient development. In exemplary implementations, various components can be implemented as add-ons (or plug-ins) that conform to and operate according to the specifications of the framework environment (e.g., according to the Application Programming Interface (API) specification).

[0042] Figure 1An example of framework 170 is also shown, which includes a model simulation layer 180, a framework service layer 190, a framework core layer 195, and a module layer 175. Framework 170 may include commercially available OCEAN. ® The framework, in which model simulation layer 180 is hosted by OCEAN ® Commercially available petrel for the application ® A model-centric software package. In an example implementation, PETREL... ® Software can be considered a data-driven application. PETREL ® The software may include frameworks for model building and visualization.

[0043] As an example, a framework may include features for implementing one or more mesh generation techniques. For instance, a framework may include an input component for receiving information from seismic data interpretation, at least in part based on one or more attributes of seismic data, well logging data, image data, etc. Such a framework may include a mesh generation component that optionally processes the input information in conjunction with other information to generate a mesh.

[0044] exist Figure 1 In the example, the model simulation layer 180 can provide domain objects 182, act as a data source 184, provide rendering 186, and provide various user interfaces 188. Rendering 186 can provide a graphical environment in which an application can display its data, while user interface 188 can provide a common look and feel for the application's user interface components.

[0045] As an example, domain object 182 may include entity objects, characteristic objects, and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while characteristic objects may be used to provide characteristic values, data versions, and display parameters. For example, an entity object may represent a well, while a characteristic object provides logging information, as well as version information and display information (e.g., displaying the well as part of the model).

[0046] exist Figure 1 In this example, data can be stored in one or more data sources (or data storage areas, typically physical data storage devices), which may be located at the same or different physical sites and are accessible via one or more networks. The model simulation layer 180 can be configured to model projects. Therefore, specific projects can be stored, where the stored project information may include inputs, models, results, and cases. Thus, a user can save a project upon completion of a modeling session. Later, the project can be accessed and retrieved using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0047] exist Figure 1 In the example, geological environment 150 may include layers (e.g., strata) including reservoir 151 and one or more other features, such as fault 153-1, geological body 153-2, etc. As an example, geological environment 150 may be equipped with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and transmit information about one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment used for information acquisition, assisting resource extraction, etc. Other equipment 156 may be located remotely from the well site and include sensing, detection, transmission, or other circuitry systems. Such equipment may include storage and communication circuitry systems to store and transmit data, instructions, etc. As an example, one or more satellites may be provided for communication, data acquisition, and other purposes. For example, Figure 1 A satellite communicating with a network 155 that can be configured for communication is shown. It should be noted that the satellite may additionally or alternatively include circuitry for imaging (e.g., spatial imaging, spectral imaging, temporal imaging, radiometric imaging, etc.).

[0048] Figure 1 The geological environment 150 is also shown as optionally including equipment 157 and 158 associated with a well, the well comprising a basic horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination of natural and artificial fractures. As an example, a laterally extending reservoir may be drilled. In this example, there may be lateral variations in properties, stresses, etc., where assessment of such variations can aid in planning, operations, etc., to develop the laterally extending reservoir (e.g., via fracturing, injection, extraction, etc.). As an example, equipment 157 and / or 158 may include components, one or more systems, etc., for fracturing, seismic sensing, seismic data analysis, assessment of one or more fractures, etc.

[0049] As described above, system 100 can be used to execute one or more workflows. A workflow can be a process that includes several work steps. Work steps can manipulate data, such as creating new data, updating existing data, etc. As an example, a workflow can, for example, operate on one or more inputs based on one or more algorithms and create one or more results. As an example, the system may include a workflow editor for creating, editing, executing, etc., workflows. In this example, the workflow editor can provide selection of one or more predefined work steps, one or more custom work steps, etc. As an example, a workflow can be configurable in PETREL. ®The workflow implemented in the software, for example, operates on seismic data, seismic attributes, etc. As an example, the workflow could be available in OCEAN... ® The process implemented within the framework. As an example, a workflow may include one or more work steps that access modules such as plugins (e.g., external executable code).

[0050] Simulation model calibration to provide 4D FEA dynamic drilling analysis suitable for the basin. The automated calibration process described in this paper integrates various optimization techniques based on the physical characteristics and properties of the simulation engine. For example, grid search can be used to modify (e.g., optimize) discrete formation descriptions using a test rock library, while gradient descent can be used to modify (e.g., optimize) continuous model parameters. Furthermore, utilizing specific well condition data can simplify the modification of certain model parameters, thereby reducing the overall complexity of the optimization process. These methods improve optimization performance even when dealing with computationally intensive simulations.

[0051] 4D FEA Dynamic Drilling Analysis Drill Column Model Figure 2 A 4D finite element analysis (FEA) drilling simulation based on one implementation is presented. The drill string is an elongated structure assembled from various tubing and drilling tools, possessing a complex geometry. It is subject to complex load conditions such as gravity, wellbore contact, and drill bit-rock interaction. Numerical methods are typically used to solve such problems. There are two main discretization methods used to represent drill string structures: the finite rigid body method and the finite element method. The model described in this paper uses the finite element method. The drill string can be discretized using 3D beam elements. Each beam element has two nodes, and each node has six degrees of freedom: three translational and three rotational. Using the finite element method, the behavior of the drilling system can be described by Equation 1.

[0052] Where M, K, and C are the mass matrix, stiffness matrix, and damping matrix, respectively; , and It is a vector of displacement, velocity, and acceleration; and This is the force vector acting on the system. The above equations can be solved using numerical integration techniques (such as the Newmark method).

[0053] The contact force in the wellbore and the frictional force at the contact point can be described by Equation 2.

[0054] in and These are the well contact stiffness and damping coefficient; It is the depth of penetration between the wellbore and the drill string; It is the velocity at the point of contact; and It is the coefficient of friction.

[0055] Drill bit rock interaction mode Figure 3A and Figure 3B The bottom hole model and drill bit forces according to one implementation scheme are shown. The drill bit model can start from a single-tool test (e.g.) Figure 7 (As shown). A series of cutting tests can be performed to record the cutting forces under different tool sizes, orientation angles, depths of cut, formations, and confining pressures. Then, during drilling simulation, when the depth of cut for each tool is determined, the cutting force on each tool can be obtained from the recorded laboratory test results. The load on the entire drill bit can be obtained by summing the forces acting on each tool (e.g., Figures 8A to 8M (As shown). Iterations can be performed during the calculation to balance the total load on the tool with the drilling pressure applied to the drill bit.

[0056] Simulation calibration Due to the complexity of any oil drilling operation and the heterogeneity and inhomogeneity of formations, certain workflows can be developed to systematically optimize the drilling system. One objective of this workflow is to calibrate and validate 4D drilling simulator software using drilling data, mud logging data, and / or wireline logging data, based on formation zoning for the drilling operation. This allows for the creation of virtual carbonate formations to better reproduce actual drilling conditions in pre-salt carbonate rocks. Drilling dynamics simulation can predict vibrations throughout the drill string with a fairly high degree of determinism. With field recording and careful calibration, the accuracy of shock and vibration predictions can be further improved through drilling dynamics simulation.

[0057] Automatic simulation model calibration A data-driven calibration workflow correlates simulation results with field measurements. The calibration workflow may include: 1) extracting calibration points from field measurements; 2) adjusting simulation parameters; 3) running the simulation; and 4) comparing the simulation results with the field measurements. Then, steps 2) through 4) are repeated until the error is within a predetermined error threshold or the maximum number of cycles is reached.

[0058] Figure 4 A flowchart of a simulation calibration according to one implementation scheme is shown. Figure 5A schematic diagram of the overall automated calibration workflow according to one implementation is shown. Due to the timelessness of the simulation, transient phases can be skipped, and averages or patterns can be matched. Furthermore, to overcome the complexity of drilling activities and the large amount of computation in dynamic drilling simulation, the entire workflow can be divided into one or more sub-workflows, each of which can use calibration techniques similar to those discussed previously. Operating parameters (e.g., inputs) and targets come from field data or outputs from previous parts of the workflow. The final output can be or includes calibrated model parameters and / or comparison results. These sub-workflows include a friction coefficient calculator, a surface-to-downhole converter, a rock and multiplier finder, and a final validator. One reason for decoupling the drilling process into separate drill string and drill bit-rock interaction models is to allow the application of lightweight simulators to accelerate the calibration process. Another reason is to reduce the large number of combinations of numerous model parameters and options.

[0059] Friction coefficient calculator This sub-workflow takes the ground rotation speed per minute (RPM) and flow rate as inputs; ground torque as the target; and a drill string model as a simulator to search for the friction coefficients of the open hole and casing sections. A side output can be the rotating ground hook load. The method involves adjusting the friction coefficient within a predetermined range and then feeding back the corresponding ground torque to increase or decrease the friction coefficient, thereby minimizing the torque discrepancy between simulation and measurement.

[0060] Surface to downhole converter This sub-workflow takes surface RPM, flow rate, drilling rate (ROP), open hole and casing friction coefficients, and rotating bottom hook load as inputs; targets surface weight on bit (WOB) and torque; and uses the drill string model as a simulator to search for downhole WOB and torque. A side branch is used for downhole RPM. Here, a static model of the drill string can be used to improve performance, and a single attempt typically takes only a few seconds. Downhole RPM can be captured due to the detailed model of the mud motor.

[0061] Rocks and Multiplier Finder This sub-workflow takes surface ROP and downhole RPM as inputs; downhole WOB and torque as targets; and uses a bit-rock interaction model as a simulator to search for formation characterizations (e.g., rocks and their multipliers). Available candidates from the bit's rock library test results can be input cyclically. Available candidates refer to the cutter type and / or size on the bit. Lithology and / or mud logging results can be used for rock selection, such as sandstone, shale, carbonate rocks, etc. This physical understanding can narrow the search space, thereby reducing time costs, and can also improve the accuracy of results through known formation descriptions (such as homogeneous or heterogeneous rocks, percentage of rock assemblages, etc.). A static model of bit-rock interactions can also be utilized, and a single rock test run takes only a few seconds.

[0062] Validator The drilling surface RPM, flow rate, downhole WOB, friction coefficient, and formation characterization can be used as inputs; surface ROP and torque can be used as targets; and the integrated drill string and drill bit rock interaction model can be used as a simulator to identify gaps between simulation and measurement. The model can be an integrated dynamic model, computationally intensive, and a single simulation typically requires 4 to 6 hours. Using this simulator, overall performance such as ROP and torque, as well as drilling dynamics such as stick-slip, shock and vibration, and high-frequency torsional oscillations (HFTO), can be reproduced.

[0063] The rock and multiplier finder will interact multiple times with the final validator until the gap between the simulation and measurement is within a predetermined range, or until the maximum number of iterations is reached or the results no longer improve.

[0064] Algorithms for automatic simulation model calibration Drilling conditions used to decouple model parameters Figure 6 A diagram illustrating various drilling conditions is shown according to one embodiment. Figure 6 As shown, time intervals or time reference data can be divided into various drilling states. For example, circulation (code 11) indicates that the drill string is stationary while pumping and rotating are taking place. Circulation immediately precedes drilling. For example, drilling (code 0) indicates that the drill string is moving downwards while pumping and rotating are taking place. Circulation is also called bottom-out rotation. This is simplified to the fact that the coefficient of friction is the same during the circulation phase and the drilling phase because the mud conditions and drill string-well contact are similar in both phases. Since the drill string is stationary, the circulation state can be relatively simple, meaning that the drill bit does not interact with the rock. Therefore, circulation state data can be applied to calibrate the coefficient of friction.

[0065] Gradient descent method for continuous model parameter space In mathematics, gradient descent (also known as steepest descent) is a first-order iterative optimization algorithm used to find local minima of differentiable functions. This paper uses this algorithm for calibration. Here, the local minimum is the minimum distance between the simulation result and the field measurement. The differentiable function is the simulated response of the input parameters.

[0066] Using the coefficient of friction as an example, `scipy.optimize.minimize_scalar` can be used when one coefficient of friction is known and another needs to be calibrated. This function calls a simulator to calculate the ground torque and, given the input coefficient of friction, returns the squared error between the simulated and measured ground torques. In the example, a drilling scenario can use a boundary of [0.1, 0.5]. This method can be "bounded," and the tolerance can be 1.0e-3. The optimization algorithm can then iteratively call the function and return the coefficient of friction within the range [0.1, 0.5] that minimizes the difference in ground torque.

[0067] Grid search for discrete stratigraphic description As previously mentioned, the drill bit-rock interaction model can rely on laboratory tests of the tool-rock interaction. The rock samples used for these laboratory tests can be discrete. Furthermore, the confining pressures applied to the rock samples can also be discrete. A (e.g., optimal) match response between the rock samples and the real formation can be an expected outcome of the calibration workflow. The absence of a clear correlation between the rock samples and the mesh search may imply the use of a loop of available rock. The mesh search can be performed in parallel.

[0068] Calibration results Figure 7 The log of multiple field measurements according to one implementation scheme is shown. Figures 8A to 8M Several graphs illustrating the extraction of calibration point data from field measurements are shown according to one embodiment. Figure 9 A graph illustrating multiple calibration points is shown according to one embodiment. Field measurements may include or include surface load factor (SWOB), surface torque (STOR), rotational speed per minute (RPM), and drilling rate (ROP). A "data point" refers to a point at a specific time index containing the measurements at that time index: (1) Since surface RPM is highly controllable and varies little during the drilling process, and is more accurate than other measurements, a histogram of surface rotation speed per minute (SRPM) for the data points at the drill string stand is first determined and / or generated. Then, one or more (e.g., two) groups (sorted by data point count) can be selected for use in (2) below. If the SRPM is nearly constant, data points can be selected as a group from the next section.

[0069] (2) For each SRPM group, determine and / or generate a histogram of the surface drill load (SWOB) for the data points of the group. Then, one or more (e.g., five) of the previous groups (sorted by data point count) can be selected for use in (3) below.

[0070] (3) For one or more (e.g., ten) groups (or five groups if an SRPM group is detected), for any given log from the surface to the well, calculate the average of each measurement of the data points in the group.

[0071] (4) Sort the groups (e.g., ten) based on the number of data points in each group.

[0072] (5) Automatically select the first one or more (e.g., three) groups and use the average value as the bottom drilling input. In one implementation, each of the ten sets can be a candidate; however, testing shows that in most cases, the first few groups account for the majority of the data points.

[0073] Figure 10A and Figure 10B A graph illustrating the friction coefficient calibration results obtained using the gradient descent method according to one embodiment is shown. During the iteration process, the algorithm uses a larger friction coefficient in the second iteration, accompanied by an increase in ground torque, which increases the distance between the simulation and the measurement. Subsequently, the algorithm correspondingly decreases the friction coefficient. And when the distance is smaller, the interval also becomes smaller.

[0074] Figures 11A to 11C A graph illustrating the rock and multiplier calibration results according to one embodiment is shown. Target points are identified by reference numerals 1110A-1110C (blue). Rocks with multipliers (e.g., the best) are identified by reference numerals 1120A-1120C (green). Rocks with multipliers represent points closest to candidate rocks obtained through a grid search of available rocks.

[0075] Figure 12 The final verification results based on one implementation scheme, after multiple iterations, are shown. More specifically, Figure 12 The example demonstrates validation results using an integrated drill string and bit-rock interaction model, combined with calibrated friction coefficients and rock with multipliers. Unfortunately, in this example, the results exceed a pre-set (e.g., acceptable) threshold. Therefore, multiple iterations may be required between the rock and multiplier finder and validation.

[0076] Summary of calibration results Figures 13A to 13CA graph illustrating calibration results from multiple resources (wells) is shown according to one implementation scheme. In these examples, the overall correlation is within a predetermined (e.g., acceptable) range, but there are several outliers.

[0077] Simulation results can provide a baseline for cause analysis and a framework for improvements through calibration. Data extraction methods can be adapted to the simulator and integrated into the entire workflow to reduce human error. Furthermore, simulation parameters can be modified to align field measurements with the calibration model. Additionally, downhole measurements can be matched to reduce the likelihood of downhole failures.

[0078] The automated calibration workflow described in this paper calibrates the model and matches the simulated input to actual field conditions, particularly in formation characterization. Calibrating the rock model overcomes problems caused by limited rock sample availability and rock heterogeneity in laboratory testing. Furthermore, the calibrated rock model can be retained and used as a reference for future adjacent well analyses and next well planning.

[0079] Furthermore, the operator-oriented simulation workflow provided by the automated calibration process enhances confidence in designing suitable bottom hole assembly (BHA) to prevent tool failure. It also helps identify the root causes of drilling malfunctions. This automated calibration workflow offers advantages in improving the accuracy and reliability of simulations, supporting the decision-making process, and enhancing drilling performance.

[0080] Calibration of friction coefficient and rock fracture characteristics based on ground measurements This disclosure can be used to prepare one or more inputs for determining and / or calibrating well friction coefficients and rock fracture characteristics (also known as rock files). Well friction coefficients and rock files can reflect the actual physical conditions of the wellbore both on the surface and downhole.

[0081] The system and method can determine the friction coefficient and rock fracture characteristics (also known as rock profiles) based on ground measurements.

[0082] The system and method can measure or determine one or more surface measurements at the top of the wellbore (e.g., the surface). Surface measurements may be or include weight on bit (SWOB) measured at the surface and torque on the drill bit (STOR) measured at the surface. The system and method can then use torque and friction (T&D) analysis to determine one or more downhole measurements at the downhole tool (e.g., the drill bit) within the wellbore. Downhole measurements may be or include weight on bit (DWOB) measured within the wellbore and at the drill bit, and torque on the drill bit (DTOR) measured within the wellbore and at the drill bit.

[0083] Then, using drill bit-independent analysis, one or more candidate rock files can be used to generate the corresponding behavior of the drill bit when drilling through the rock type in the rock file (e.g., BWOB, DTOR). The system and method can then be modified (e.g., optimized) to identify the rock file from multiple rock files that produces the drill bit stress that most closely approximates the transmitted force (e.g., DWOB, DTOR).

[0084] The discrepancy between surface measurements and (e.g., simulated) downhole measurements can then be determined (e.g., using a full dynamic drilling analysis). This discrepancy can be reduced using feedback adjustment mechanisms. More specifically, the discrepancy can be reduced by reconsidering the transmitted measurements (e.g., DWOB, DTOR).

[0085] Figure 14 A flowchart of a method 1400 for calibrating a model of subsurface formations according to one embodiment is shown. More specifically, method 1400 can be used to calibrate one or more rock types of subsurface formations and / or the coefficient of friction in a wellbore drilled into the subsurface formation. An illustrative order of method 1400 is provided below; however, one or more parts of method 1400 may be performed in a different order, simultaneously, repeatedly, or omitted. Figure 15A and Figure 15B A schematic diagram of at least a portion of method 1400 according to one embodiment is shown.

[0086] Method 1400 includes capturing one or more measurements at the surface of the wellbore, such as at 1405. This is in Figure 15A and Figure 15B As shown in the figure. Measurements may include the rotational speed per minute (RPM) of the drill string extending into the wellbore, the surface torque (STOR) on the drill string, the surface weight on the drill string (SWOB), the drilling rate (ROP) of the drill string, or a combination thereof.

[0087] Method 1400 may also include determining the coefficient of friction based on STOR, as at 1410. The coefficient of friction can be determined as the drill bit moves away from the bottom of the wellbore. The drill bit can be attached to the lower end of the drill string. This is in Figure 15A and Figure 15B as well as Figures 16A to 16C As shown in the image. Figures 16A to 16C A more detailed schematic diagram is shown of a portion of the method according to one embodiment (e.g., determining and / or calibrating the friction coefficient of a downhole tool transitioning from a bottom-out mode). More specifically, Figure 16A A schematic side view showing the friction coefficient distribution between the casing and open hole sections of the wellbore is shown. Figure 16B A schematic diagram showing the STOR when the drill bit is off the bottom of the wellbore is shown. Figure 16CThe diagram shows the relationship between STOR and measurement depth in the wellbore.

[0088] Method 1400 may also include determining the downhole torque (DTOR) and / or downhole bit weight on the drill bit (DWOB), as at 1415. DTOR and DWOB can be determined when the drill bit is at the bottom of the wellbore. DTOR and / or DWOB can be determined at least in part based on STOR, SWOB, and the coefficient of friction. This is in Figure 15A and Figure 15B as well as Figures 17A to 17C As shown in the image. Figures 17A to 17C A more detailed schematic diagram of a portion of the method according to one embodiment is shown (e.g., predicting the pressure on the drill bit (WOB) and torque on the drill bit based on the coefficient of friction). More specifically, Figure 17A The friction distribution along the drill string is shown. Figure 17B A torque distribution diagram along the drill string is shown. Figure 17C A schematic diagram is shown illustrating SWOB, STOR, BWOB, and BTOR during drilling in a wellbore.

[0089] Method 1400 may also include simulating drill bit bottoming through various rock types in the model, such as at 1420. The simulation may generate multiple simulated drill bit torques (BTOR) and / or multiple simulated drill bit pressures (BWOB). This in Figure 15A and Figure 15B as well as Figure 18 As shown in the image.

[0090] Method 1400 may also include comparing DTOR with BTOR to produce a first comparison, as at 1425. This is in Figure 15A and Figure 15B As shown in the image.

[0091] Method 1400 may also include comparing the DWOB with the BWOB to produce a second comparison, as at 1430. This is in Figure 15A and Figure 15B As shown in the image.

[0092] Method 1400 may also include identifying one of the rock types based on a first comparison and / or a second comparison, as at 1435. In one embodiment, the rock type may represent the relationship between rock forces and cutting depth, etc., which can be determined by laboratory testing of rock samples and cutting tools. The rock sample may include lithology, grain / crusher size, mineral composition, etc. However, these physical parameters, other than rock forces, may not be used for (e.g., IDEAS) modeling. For example, in IDEAS calibration, "rock type" (e.g., tool force) can be used to infer drill torque and WOB from the cutting tool within the drill bit. By comparing the derived drill WOB / torque with field measurements, the optimal "rock type" can be selected based on the closest proximity.

[0093] In another embodiment, rock type can be or includes lithology (e.g., igneous, sedimentary, metamorphic), grain / clastic size, mineral composition, texture, grain structure, rock fracture characteristics, or a combination thereof. Illustrative rock fracture characteristics can be or include continuity, cohesion, stress, fracture / fault size and / or location, permeability, rock strength, or a combination thereof.

[0094] As mentioned above, the identified rock types can have minimal differences between DTOR and BTOR, DWOB and BWOB, or both. This is in Figure 15A and Figure 15B as well as Figures 19A to 19D As shown in the image. Figures 19A to 19D A schematic diagram of another method for identifying rock types according to one embodiment is shown. More specifically, Figure 19A A graph showing the cost of each rock file is displayed. Figure 19B One or more (e.g., 3D) diagrams of BTOR and BWOB for each rock file are shown. Figure 19C A diagram showing the relationship between BTOR and drilling operations according to RPM1 and ROP1 is presented. Figure 19D The diagram shows the relationship between BTOR and drilling operations according to RPM2 and ROP2.

[0095] Method 1400 may also include determining the simulated surface torque (STOR') and / or the simulated surface drilling pressure (SWOB') on the drill string, as at 1440. STOR' and / or SWOB' may be determined at least in part based on the identified rock type. This is in Figure 15A and Figure 15B As shown in the image.

[0096] Method 1400 may also include comparing STOR with STOR' to produce a third comparison, as at 1445. This is in Figure 15A and Figure 15B As shown in the image.

[0097] Method 1400 may also include comparing SWOB with SWOB' to produce a fourth comparison, as at 1450. This is in Figure 15A and Figure 15B As shown in the image.

[0098] Method 1400 may also include calibrating the friction coefficient and / or identifying the rock type, as shown at 1455. Calibration may be based on a third and / or fourth comparison. This is in Figure 15A and Figure 15B , Figure 20 , Figure 21A , Figure 21B and Figures 22A to 22D As shown in the image. Figure 20 A schematic diagram of a method for verifying the accuracy of a determined friction coefficient and rock type, according to one embodiment, is shown. Figure 21A and Figure 21B A schematic diagram of a method for improving the accuracy of a selected rock type, according to one embodiment, is shown. Figures 22A to 22D A schematic diagram of a method for analyzing drilling data for standoffs according to one implementation scheme is shown. More specifically, a TDI (Thorough Difference Injection) can be run. Multiple standoffs can then be segmented based on drilling conditions, and the bottom clearance data for each standoff can be determined. Standoffs with target depths can be selected. The drilling data for the selected standoffs can be analyzed to select one or more sets (e.g., two sets) of drilling data. Rock type calibration can include more specifically and / or more reliably identifying rock types. Rock type calibration can also, or alternatively, include determining that the initial rock type is incorrect and identifying a new / correct rock type.

[0099] Method 1400 may also include displaying one or more outputs, as at 1460. The outputs may be or include friction coefficient, DTOR, DWOB, BTORS, BWOBS, rock type, STOR', SWOB', calibrated friction coefficient, calibrated rock type, or a combination thereof.

[0100] Method 1400 may also include performing well site actions, as at 1465. Well site actions may be based on friction coefficient, DTOR, DWOB, BTORS, BWOBS, rock type, STOR', SWOB', calibrated friction coefficient, calibrated rock type, or combinations thereof. Well site actions may be or include generating and / or transmitting commands or signals that cause physical actions to occur at the well site (e.g., using a computing system). Well site actions may also or alternatively include performing physical actions at the well site. Physical actions may include changing the RPM number, changing the STOR, changing the SWOB, changing the ROP, or combinations thereof.

[0101] In some implementations, the methods of this disclosure can be executed by a computing system. Figure 23 An example of such a computing system 2300 according to some embodiments is shown. The computing system 2300 may include a computer or computer system 2301A, which may be a standalone computer system 2301A or an arrangement of distributed computer systems. The computer system 2301A includes one or more analysis modules 2302 configured to perform various tasks according to some embodiments (such as one or more methods disclosed herein). To perform these various tasks, the analysis modules 2302 execute independently or in conjunction with one or more processors 2304 connected to one or more storage media 2306. Processor 2304 is also connected to network interface 2307 to allow computer system 2301A to communicate with one or more additional computer systems and / or computing systems (such as 2301B, 2301C and / or 2301D) via data network 2309. (It should be noted that computer systems 2301B, 2301C and / or 2301D may or may not share the same architecture as computer system 2301A and may be located in different physical locations. For example, computer systems 2301A and 2301B may be located in a processing facility while communicating with one or more computer systems (such as 2301C and / or 2301D located in one or more data centers and / or in different countries on different continents).

[0102] The processor may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0103] Storage medium 2306 can be implemented as one or more computer-readable or machine-readable storage media. It should be noted that, although in Figure 23 In the exemplary embodiments, storage medium 2306 is depicted within computer system 2301A; however, in some embodiments, storage medium 2306 may be distributed within and / or across multiple internal and / or external enclosures of computing system 2301A and / or additional computing systems. Storage medium 2306 may include one or more different forms of memory, including semiconductor memory devices such as dynamic or static random access memory (DRAM or SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory; magnetic disks such as fixed disks, floppy disks, and removable disks; other magnetic media, including magnetic tape; and optical media such as optical discs (CDs) or digital video discs (DVDs), BLURAY, etc. ®Disks or other types of optical storage, or other types of storage devices. It should be noted that the instructions discussed above can be provided on a single computer-readable or machine-readable storage medium, or on multiple computer-readable or machine-readable storage media distributed across a large system that may have multiple nodes. Such one or more computer-readable or machine-readable storage media are considered part of an article (or article of manufacture). An article or article of manufacture can refer to any single or multiple manufactured components. One or more storage media may be located in a machine that executes the machine-readable instructions, or at a remote location from which the machine-readable instructions can be downloaded via a network for execution.

[0104] In some embodiments, computing system 2300 includes one or more calibration modules 2308. In an example of computing system 2300, computer system 2301A includes calibration module 2308. In some embodiments, a single calibration module may be used to perform aspects of one or more embodiments of the methods disclosed herein. In other embodiments, multiple calibration modules may be used to perform aspects of the methods herein.

[0105] It should be understood that computing system 2300 is merely one example of a computing system, and computing system 2300 may have more or fewer components than shown, and may be combined. Figure 23 Additional components not depicted in the exemplary embodiments, and / or the computing system 2300 may have Figure 23 The different configurations or arrangements of the components described. Figure 23 The various components shown can be implemented in hardware, software, or a combination of both, including one or more signal processing circuits and / or application-specific integrated circuits.

[0106] Furthermore, the steps in the processing methods described herein can be implemented by operating one or more functional modules in an information processing device (such as a general-purpose processor or a special-purpose chip (such as an ASIC, FPGA, PLD, or other suitable device)). These modules, combinations of these modules, and / or their combinations with general hardware are included within the scope of this disclosure.

[0107] Computational interpretation, models, and / or other interpretation aids can be optimized iteratively; this concept applies to the methods discussed herein. This can include the use of algorithms (such as in computing devices (e.g., computing system 2300, ...)). Figure 23 The feedback loop executed at the location and / or is manually controlled by the user who can determine whether a given step, action, template, model, or curve set is sufficient to accurately assess the underground 3D geological terrain under consideration.

[0108] For purposes of explanation, the foregoing description has been described with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or limited to the precise form disclosed. In view of the foregoing teachings, many modifications and variations are possible. Furthermore, the order of the elements illustrating and describing the methods described herein may be rearranged, and / or two or more elements may occur simultaneously. The embodiments have been chosen and described in order to best explain the principles of this disclosure and its practical application, thereby enabling others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications suitable for the intended particular use.

Claims

1. A method (1400) for calibrating a model of subsurface strata, the method comprising: Capture (1405) one or more measurements at the surface of the wellbore extending into the underground formation, wherein the one or more measurements include the surface torque (STOR) on the drill string extending into the wellbore; The friction coefficient (1410) is determined based on the STOR when the drill bit is off the bottom of the wellbore, wherein the drill bit is connected to the lower end of the drill string; When the drill bit touches the bottom of the wellbore, determine (1415) the downhole torque (DTOR) on the drill bit and the downhole bit weight (DWOB) on the drill bit; and The rock type in the underground strata is identified (1435) at least in part based on the friction coefficient, the DTOR, and the DWOB.

2. The method (1400) of claim 1, wherein the one or more measurements further include surface drill pressure (SWOB) on the drill string, and wherein the DTOR and the DWOB are determined based on the STOR, the SWOB and the coefficient of friction.

3. The method (1400) according to claim 1 or claim 2, further comprising: In the model, the drill bit is simulated (1420) to bottom out and drill through a variety of different rock types to generate multiple simulated torques (BTOR) on the drill bit, wherein the rock types include the identified types; as well as The DTOR is compared with the BTOR (1425) to generate a comparison, wherein the rock type is identified based on the comparison.

4. The method (1400) according to any one of claims 1 to 3, further comprising: In the model, the drill bit is simulated (1420) to drill through a variety of different rock types to generate multiple simulated pressure on the drill bit (BWOB), wherein the rock types include the identified types; as well as The DWOB is compared with the BWOB (1430) to generate a comparison, wherein the rock type is identified based on the comparison.

5. The method (1400) according to any one of claims 1 to 4, further comprising: The simulated surface torque (STOR') on the drill string is determined based on the identified rock type (1440); The STOR is compared with the STOR' (1445) to generate a comparison; as well as The friction coefficient is calibrated (1455) based on the comparison.

6. The method (1400) according to any one of claims 1 to 5, further comprising: The simulated surface torque (STOR') on the drill string is determined based on the identified rock type (1445); Compare the STOR with the STOR' to generate a comparison; and The rock type is calibrated (1455) based on the comparison.

7. The method (1400) according to any one of claims 1 to 6, further comprising: The simulated surface drilling pressure (SWOB') on the drill string is determined based on the identified rock type (1440); The SWOB is compared with the SWOB' (1450) to generate a comparison; as well as The friction coefficient is calibrated (1455) based on the comparison.

8. The method (1400) according to any one of claims 1 to 7, further comprising: The simulated surface drilling pressure (SWOB') on the drill string is determined based on the identified rock type (1440); The SWOB is compared with the SWOB' (1450) to generate a comparison; as well as The rock type is calibrated (1455) based on the comparison.

9. The method (1400) according to any one of claims 1 to 8, further comprising displaying (1460) the friction coefficient, the DTOR, the DWOB and the rock type.

10. The method (1400) according to any one of claims 1 to 9, further comprising performing (1465) well site actions in response to the rock type, wherein the well site actions include changing the STOR, changing the surface bit weight on the drill string (SWOB), changing the rotational speed per minute (RPM) of the drill string, changing the drilling rate (ROP) of the drill string, or a combination thereof.

11. A computing system (2300) comprising: One or more processors; as well as A memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations including: Capture (1405) one or more measurements at the surface of the wellbore extending into the underground formation, wherein the one or more measurements include surface torque (STOR) and surface drilling pressure (SWOB) on the drill string extending into the wellbore; The friction coefficient (1410) is determined based on the STOR when the drill bit is off the bottom of the wellbore, wherein the drill bit is connected to the lower end of the drill string; When the drill bit touches the bottom of the wellbore, determine (1415) the downhole torque (DTOR) and the downhole bit pressure (DWOB) on the drill bit, wherein the DTOR and the DWOB are determined based on the STOR, the SWOB and the coefficient of friction; as well as The rock type in the underground strata is identified (1435) at least in part based on the friction coefficient, the DTOR, and the DWOB.

12. The computing system (2300) according to claim 11, wherein the operation further includes: In the model, the drill bit is simulated (1420) to bottom through a variety of different rock types to generate multiple simulated torque (BTOR) and multiple simulated bit pressure (BWOB) on the drill bit, wherein the rock types include the identified types; The DTOR is compared with the BTOR (1425) to produce a first comparison; as well as The DWOB is compared with the BWOB (1430) to generate a second comparison, wherein the rock type is identified based on the first comparison and the second comparison.

13. The computing system (2300) according to claim 11 or 12, wherein the operation further comprises: Based on the identified rock type, (1440) the simulated surface torque (STOR') and simulated surface drilling pressure (SWOB') on the drill string are determined; The friction coefficient is calibrated (1455) at least in part based on the STOR' to produce a calibrated friction coefficient; as well as The rock type is calibrated (1455) at least in part based on the SWOB' to produce a calibrated rock type.

14. The computing system (2300) according to claim 13, wherein the operation further comprises: The STOR is compared with the STOR' (1445) to produce a first comparison; The SWOB is compared with the SWOB' (1450) to produce a second comparison; The friction coefficient is calibrated (1455) based on the first comparison and the second comparison to produce a calibrated friction coefficient; as well as The rock type is calibrated (1455) based on the first comparison and the second comparison to produce a calibrated rock type.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: Capture one or more measurements at the surface of the wellbore (1405), wherein the wellbore extends into the underground formation, wherein the measurements include the rotational speed per minute (RPM) of the drill string extending into the wellbore, the surface torque (STOR) on the drill string, the surface drilling pressure (SWOB) on the drill string, and the drilling rate (ROP) of the drill string. The friction coefficient (1410) is determined based on the STOR when the drill bit is off the bottom of the wellbore, wherein the drill bit is connected to the lower end of the drill string; When the drill bit touches the bottom of the wellbore, determine (1415) the downhole torque (DTOR) and the downhole bit pressure (DWOB) on the drill bit, wherein the DTOR and the DWOB are determined based on the STOR, the SWOB and the coefficient of friction; In the model, the drill bit is simulated (1420) to drill through a variety of different rock types to generate multiple simulated torques (BTOR) and multiple simulated bit pressures (BWOB) on the drill bit. The DTOR is compared with the BTOR (1425) to produce a first comparison; The DWOB is compared with the BWOB (1430) to produce a second comparison; Based on the first comparison and the second comparison, one of the rock types (1435) is identified, wherein the identified rock type has minimal difference between or between the DTOR and the BTOR, the DWOB and the BWOB; Based on the identified rock type, (1440) the simulated surface torque (STOR') and simulated surface drilling pressure (SWOB') on the drill string are determined; The STOR is compared with the STOR' (1445) to produce a third comparison; The SWOB is compared with the SWOB' (1450) to produce a fourth comparison; The friction coefficient is calibrated (1455) based on the third comparison and the fourth comparison to produce a calibrated friction coefficient; The rock type is calibrated (1460) based on the third comparison and the fourth comparison to produce a calibrated rock type; as well as (1465) Wellfield actions are performed based on the calibrated friction coefficient and the calibrated rock type, wherein the wellfield actions include generating or transmitting signals that adjust the RPM, the STOR, the SWOB, the ROP, or a combination thereof.

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