A method and device for quantitatively evaluating formation drillability anisotropy and electronic equipment

Abstract

The present application provides a formation drillability anisotropy quantitative evaluation method, device and electronic equipment, taking the main structural plane of the formation as a unified angle reference, realizing multi-angle indoor experiment, digital core joint calibration, multi-parameter combined simulation expansion, direction drillability index calculation, prediction model establishment, single well multi-mode application, forming a set of formation drillability anisotropy quantitative evaluation method from limited experimental data, through physical simulation expansion, finally serving the actual drilling engineering decision. The present application can drillability evaluation covers the full angle range, provides quantitative data support for bit selection at any hole inclination angle; through preset multi-direction evaluation, the advantage rock breaking direction and the difficult drilling direction can be clearly distinguished, providing quantitative comparison reference for well trajectory optimization; the segmented parameter optimization of the whole well section is upgraded from experience trial and error to data driven, and single well data basis is provided for subsequent block three-dimensional space modeling.

Description

Technical Field

[0001] This invention relates to the field of drilling and production engineering technology, and in particular to a method, apparatus and electronic equipment for quantitative evaluation of formation drillability anisotropy. Background Technology

[0002] Formation drillability assessment is an important foundation for drilling engineering design and field optimization. Its assessment results can be used for drill bit selection, drilling parameter optimization, wellbore trajectory design, prediction of difficult-to-drill formations, and formulation of speed-up and efficiency-enhancing schemes.

[0003] In the drilling and development of deep, ultra-deep, unconventional oil and gas, horizontal wells, and wells with complex structures, formations typically exhibit significant heterogeneity and anisotropy. For shale, mudstone, coal, tight sandstone, thin interbedded layers, and fractured formations, structures such as bedding planes, fracture surfaces, weak planes, and the directional arrangement of minerals can lead to differences in rock mechanical properties, acoustic wave propagation characteristics, and drill bit rock-breaking response in different directions.

[0004] Existing drillability assessment methods can usually reflect the ease of rock breaking in formations to a certain extent, but most methods rely on a single evaluation index, which is difficult to fully characterize the directional rock breaking differences caused by bedding, fractures, weak surfaces and mineral texture in complex formations.

[0005] Especially in horizontal wells, highly deviated wells, and wells with complex trajectories, the angle between the drilling direction and the formation structure plane is constantly changing, and the rock-breaking resistance, rock-breaking energy consumption, and drilling efficiency may vary significantly under different drilling directions. If the existing average drillability index is still used for evaluation, it is easy to cause problems such as inaccurate drill bit selection, insufficient targeting of drilling parameter optimization, and inadequate prediction of difficult-to-drill formations.

[0006] Existing technologies also include methods for evaluating rock drillability anisotropy. However, these methods are merely theoretical research tools for verifying the existence of drillability anisotropy, only capable of verifying basic principles. They suffer from multiple technical shortcomings and cannot serve as a complete and implementable engineering decision-making system. When applied to actual drilling projects, they can cause a chain of negative reactions, from initial drill bit selection errors and unreasonable trajectory design to a lack of segmented control in single wells and distorted well location deployment in blocks, ultimately significantly increasing overall drilling costs. Specifically, existing methods for evaluating rock drillability anisotropy are prone to causing four major categories of engineering problems.

[0007] 1. Drill bit selection is simple and one-size-fits-all, resulting in poor adaptability to different formations.

[0008] Existing methods rely solely on data from parallel and perpendicular bedding directions to calculate the average anisotropy index, failing to characterize the variations in drillability at oblique angles such as 30°, 45°, and 60°. In directional and horizontal wells, the wellbore and formation often form oblique angles during the build-up and stabilization phases, making it impossible to simply equate actual rock-breaking conditions with purely vertical or horizontal drilling. Field technicians are forced to select drill bits based on compromise values ​​from two extreme directions, resulting in poor bit-formation matching and issues such as rapid wear of cutting teeth, bit slippage, and no progress. This necessitates frequent tripping and bit replacements, significantly increasing non-productive work time.

[0009] 2. Wellbore trajectory optimization lacks quantitative basis, and trajectory design is prone to falling into the difficult drilling range.

[0010] It is impossible to quantitatively distinguish between the dominant rock-breaking direction and the difficult-to-drill direction in the formation. In the design stage of horizontal wells and highly deviated wells, arranging the trajectory along the dominant rock-breaking direction can significantly improve the mechanical drilling rate. However, the optimal drilling direction cannot be determined by existing methods, which can easily lead to situations where the designed trajectory cuts perpendicularly to the bedding planes and is located in a difficult-to-drill direction; the torque fluctuates drastically and the sliding pressure is prominent during the actual drilling process. In severe cases, the wellbore trajectory needs to be adjusted midway, which further increases the downhole construction risk.

[0011] 3. It cannot support continuous drillability evaluation of the entire well section, and the optimization of segment parameters can only rely on experience.

[0012] While existing methods employ predictive models, they are essentially empirical regression models relying on limited directional core experimental data, rather than mapping relationships established based on continuous logging data. For sections without coring, the model lacks effective input parameters and cannot output predictive results. Furthermore, the model can only predict the relative ratios in the vertical and horizontal directions (i.e., the anisotropy index), failing to provide absolute drillability values ​​for arbitrary drilling directions, thus hindering directional well trajectory optimization. In addition, its logging inversion process relies on stringent data conditions and theoretical assumptions, resulting in insufficient reliability of inversion results for missing sections. In summary, this model can only serve to verify the patterns at coring points, failing to generate continuous, multi-directional drillability data output across the entire wellbore. This leads to a lack of dynamically changing directional data support for engineers, making it impossible to pre-match parameters such as drill pressure and rotation speed for difficult-to-drill layers. Parameter optimization across the entire wellbore relies solely on trial and error, making it difficult to guarantee drilling efficiency.

[0013] 4. Single-point evaluation alone cannot support three-dimensional prediction of blocks and may mislead the deployment of well locations in new areas.

[0014] The evaluation results of existing methods can only represent the local stratigraphic characteristics of the core point and lack the ability for inter-well interpolation and 3D spatial modeling of the block, making it difficult to generalize the single-point patterns to the entire development block. Within the same block, the stratigraphic structure and drillability vary significantly at different locations. Relying solely on single-well core data to plan new wells can easily lead to the placement of horizontal and directional wells in areas with extremely poor drillability, resulting in a significant decrease in the overall drilling efficiency of the block and causing substantial losses in development investment. Summary of the Invention

[0015] This specification provides a method, apparatus, and electronic equipment for quantitative evaluation of formation drillability anisotropy, in order to solve four major categories of engineering problems that are easily caused by existing methods for evaluating rock drillability anisotropy.

[0016] To address the aforementioned technical problems, this specification provides a method for quantitatively evaluating the anisotropy of formation drillability, comprising: identifying the main structural plane of the formation in the target formation that plays a dominant role in the difference in drillability direction; using the main structural plane as a unified angular reference, conducting rock mechanics tests, acoustic tests, and drillability tests on core samples obtained by multi-angle sampling of core samples from different lithologies, to obtain the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples of different lithologies and different included angle directions; and based on the rock mechanics parameters and acoustic parameters corresponding to core samples of different lithologies and different included angle directions... A digital core model is established based on the parameters of rock breaking and drillability. The microstructural parameters of the digital core model are jointly calibrated based on the errors between the mechanical response of the digital core model and rock mechanics experimental results, the errors between the acoustic response and acoustic test results, and the errors between the rock breaking response and drillability test results. Using the jointly calibrated digital core model, at least one of the lithology parameters, the angle of the principal structural planes of the formation, and the multi-scale constitutive parameters is changed to generate digital core models with different parameter combinations. Multi-directional rock breaking simulations are conducted on the digital core models with different parameter combinations. The simulation results based on the digital core model are then analyzed. Based on indoor test data, the rock-breaking energy, equivalent rock-breaking load, and footage per revolution were obtained at different directional angles. Using simulation data from a digital core model and indoor test data, the directional drillability index was calculated at different directional angles based on the rock-breaking energy, equivalent rock-breaking load, and footage per revolution. This directional drillability index was used as the output label of the prediction model. A prediction model was established based on the simulation data from the digital core model and indoor test data, relating formation physical parameters to the directional angle and the directional drillability index. For the target depth point of the target well, the formation physical parameters corresponding to the target depth point were compared with multiple preset parameters. After combining the directional angles, the results are input into the prediction model to obtain a sequence of directional drillability indices under different directional angles. Based on the directional drillability indices, at least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction is calculated. And / or, based on the wellbore trajectory parameters and formation principal structural plane occurrence parameters of the target well, the target angle between the actual drilling direction of the target well and the formation principal structural plane is calculated. The formation physical property parameters corresponding to the target angle and the target depth point are input into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock-breaking difficulty under the actual drilling direction.

[0017] In some embodiments, the method further includes: performing microstructure tests on core samples obtained by multi-angle sampling of cores of different lithologies to obtain microstructure parameters corresponding to core samples of different lithologies and different included angle directions; accordingly, when establishing a digital core model, the microstructure parameters are also used as the basis.

[0018] In some embodiments, the mesoscopic constitutive parameters of the digital core model are jointly calibrated based on the errors between the mechanical response of the digital core model and the rock mechanics experimental results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results. This includes: conducting mechanical response simulation, acoustic response simulation, and rock breaking response simulation on the initially constructed digital core model, respectively, and obtaining the simulated mechanical response results, simulated acoustic response results, and simulated rock breaking response results; calculating the mechanical response error between the simulated mechanical response results and the rock mechanics experimental results, and the acoustic response error between the simulated acoustic response results and the acoustic test results, respectively. The simulation results of rock-breaking response and the results of drillability tests are used to determine the rock-breaking response error. A comprehensive error function is constructed using the mechanical response error, acoustic response error, and rock-breaking response error as variables. This comprehensive error function is used as a unified convergence criterion for the joint calibration of the digital core model's multiple responses. When the calculated value of the comprehensive error function does not reach the preset convergence threshold, the microscopic constitutive parameters of the digital core model are iteratively adjusted. Based on the adjusted parameters, the simulation calculation, error solving, and comprehensive error determination operations are repeatedly performed until the calculated value of the comprehensive error function meets the preset convergence threshold, thus obtaining a digital core model with completed mechanical-acoustic-rock-breaking multi-response joint calibration.

[0019] In some embodiments, the method further includes: calculating the rock-breaking specific energy according to the following formula: , in, The rock breaking energy is the rock breaking specific energy along the target angle θ direction. The cutting force is in the direction of the target angle θ. The cross-sectional area of ​​the rock breaking. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. The rock-breaking velocity is defined by the target angle θ. The angular velocity of the drill bit in the direction of the target angle θ; Calculate the equivalent rock-breaking load using the following formula: , in, The equivalent rock-breaking load in the direction of the target angle θ is... The cutting force is located in the direction of the target angle θ. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. The cutting radius; Calculate the advance per revolution using the following formula: , in, For each revolution, the advance is given by the target angle θ. The rock-breaking velocity is defined by the target angle θ. Let θ be the angular velocity of the drill bit in the direction of the target angle.

[0020] In some embodiments, based on rock-breaking specific energy, equivalent rock-breaking load, and footage per revolution, the directional drillability index is calculated at different directional angles, including: calculating the drillability index at the target angle θ direction according to the following formula: , in, The drillability index is defined in the direction of the target included angle θ. As the benchmark drillability index, A drillability index greater than the baseline drillability index indicates that the drill is easier to drill than the average condition. The benchmark drillability index represents a near-average state. A drillability index lower than the baseline drillability index indicates that the drill is more difficult to drill than the average condition. and These are the weighting coefficients. , ,and ; The average rock-breaking energy per unit volume, For the average equivalent rock-breaking load, This represents the average advance per revolution.

[0021] In some embodiments, calculating at least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction based on the directional drillability index sequence includes: calculating the directional drillability index for multiple preset directional angles at the same depth point or the same core sample, thereby forming a directional drillability index sequence. , The maximum directional drillability index, minimum directional drillability index, and average directional drillability index are calculated using the following formulas: , , , in, The maximum directional drillability index. The minimum directional drillability index. The average directional drillability index. It is a set of angles between multiple preset directions; The drillability anisotropy coefficient A is calculated using the following formula. D : ; Dominant rock-breaking direction θ easy for: , Difficult drilling direction θ hard for: .

[0022] In some embodiments, the target angle between the actual drilling direction of the target well and the main structural plane of the formation is calculated based on the wellbore trajectory parameters and the attitude parameters of the main structural plane of the formation. This includes: converting the spherical or discrete coordinate representation of the wellbore trajectory into a unit drilling direction vector in a three-dimensional Cartesian coordinate system; converting the geological attitude parameters of the main structural plane of the formation into a unit normal vector in the same coordinate system as the drilling direction vector; performing a dot product operation between the unit drilling direction vector and the unit normal vector to obtain the angle between the drilling direction and the main structural plane of the formation; and using the angle as an input parameter of the prediction model.

[0023] In some embodiments, the method further includes establishing a drillability anisotropy spatial model of the target block by: assigning at least one of the following values ​​under multiple preset directional angles calculated at each depth point of the target well and adjacent wells to a three-dimensional space according to the corresponding well trajectory spatial coordinates, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, minimum directional drillability index, dominant rock-breaking direction, difficult-to-drill direction, and directional drillability index under the actual drilling direction; and based on seismic interpretation of strata and geological stratification... The assigned spatial scatter data is then stratified and organized according to its stratigraphic position, with data within the same stratigraphic position grouped into the same subset. Using seismic attributes, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, inter-well spatial predictions are performed within each stratigraphic unit for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The prediction results from each stratigraphic unit are then overlaid according to the original spatial coordinates to form a three-dimensional directional drillability index model and a three-dimensional drillability anisotropy coefficient model for the target block.

[0024] The second aspect of this specification provides a device for quantitatively evaluating the anisotropy of formation drillability, comprising: a first acquisition unit, used to identify the main structural plane of the formation in the target formation that plays a dominant role in the difference in drillability direction; using the main structural plane as a unified angular reference, rock mechanics tests, acoustic tests, and drillability tests are conducted on core samples obtained by multi-angle sampling of core samples of different lithologies to obtain rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples of different lithologies and different included angle directions; and a setup unit, used to establish the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples of different lithologies and different included angle directions. The system comprises the following components: a digital core model; a calibration unit, used to jointly calibrate the mesoscopic constitutive parameters of the digital core model based on the errors between the mechanical response and rock mechanics experimental results, the acoustic response and acoustic test results, and the rock-breaking response and drillability test results; a generation unit, used to generate digital core models with different parameter combinations by changing at least one of the lithological parameters, the angle of the main structural plane of the formation, and the multi-scale constitutive parameters using the jointly calibrated digital core model; and a second acquisition unit, used to conduct multi-directional rock-breaking simulations on the digital core models with different parameter combinations, targeting the digital core model... The system utilizes simulation data and laboratory test data to obtain rock-breaking energy, equivalent rock-breaking load, and footage per revolution under different directional angles. A calculation unit calculates the directional drillability index under different directional angles based on the rock-breaking energy, equivalent rock-breaking load, and footage per revolution, using simulation data and laboratory test data from the digital core model. A modeling unit uses the directional drillability index as the output label of the prediction model to establish a prediction model between formation physical parameters and directional angles and the directional drillability index, based on the simulation data and laboratory test data from the digital core model. An application unit is used to apply the directional drillability index to the target depth point of the target well. The formation properties are combined with multiple preset directional angles and input into the prediction model to obtain a sequence of directional drillability indices under different directional angles. At least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction is calculated based on the directional drillability index sequence. And / or, based on the wellbore trajectory parameters of the target well and the occurrence parameters of the main structural plane of the formation, the target angle between the actual drilling direction of the target well and the main structural plane of the formation is calculated. The formation properties corresponding to the target angle and the target depth point are input into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock-breaking difficulty under the actual drilling direction.

[0025] In some embodiments, the first acquisition unit further obtains the microstructure parameters corresponding to core samples of different lithologies and different included angle directions by performing microstructure tests on core samples obtained by multi-angle sampling of core samples of different lithologies; accordingly, the establishment unit also bases the establishment of the digital core model on the microstructure parameters.

[0026] In some embodiments, the calibration unit includes: an acquisition subunit, used to perform mechanical response simulation, acoustic response simulation, and rock-breaking response simulation on the initially constructed digital core model, respectively, and acquire the simulation mechanical response results, simulation acoustic response results, and simulation rock-breaking response results accordingly; a first calculation subunit, used to calculate the mechanical response error between the simulation mechanical response results and the rock mechanics test results, the acoustic response error between the simulation acoustic response results and the acoustic test results, and the rock-breaking response error between the simulation rock-breaking response results and the drillability test results, respectively; and a construction subunit, used to... The mechanical response error, acoustic response error, and rock-breaking response error are used as variables to construct a comprehensive error function, which is then used as a unified convergence criterion for the joint calibration of multiple responses of the digital core model. An iterative sub-unit is used to iteratively adjust the mesoscopic constitutive parameters of the digital core model when the calculated value of the comprehensive error function does not reach the preset convergence threshold. Based on the adjusted parameters, the simulation calculation, error solving, and comprehensive error judgment operations are repeatedly performed until the calculated value of the comprehensive error function meets the preset convergence threshold, thus obtaining a digital core model with completed mechanical-acoustic-rock-breaking multi-response joint calibration.

[0027] In some embodiments, the calculation unit calculates the drillability index in the direction of the target included angle θ according to the following formula: , in, The drillability index is defined in the direction of the target included angle θ. As the benchmark drillability index, A drillability index greater than the baseline drillability index indicates that the drill is easier to drill than the average condition. The benchmark drillability index represents a near-average state. A drillability index lower than the baseline drillability index indicates that the drill is more difficult to drill than the average condition. and These are the weighting coefficients. , ,and ; The average rock-breaking energy per unit volume, For the average equivalent rock-breaking load, This represents the average advance per revolution.

[0028] In some embodiments, the application unit includes: a second calculation subunit, used to calculate the directional drillability index for multiple preset directional angles at the same depth point or the same core sample, forming a directional drillability index sequence: , The maximum directional drillability index, minimum directional drillability index, and average directional drillability index are calculated using the following formulas: , , , in, The maximum directional drillability index. The minimum directional drillability index. The average directional drillability index. It is a set of angles between multiple preset directions; The drillability anisotropy coefficient A is calculated using the following formula. D : ; Dominant rock-breaking direction θ easy for: , Difficult drilling direction θ hard for: .

[0029] In some embodiments, the application unit includes: a first transformation subunit, used to uniformly convert the spherical coordinate representation or discrete coordinate representation of the wellbore trajectory into a unit drilling direction vector in a three-dimensional Cartesian coordinate system; a second transformation subunit, used to convert the geological occurrence parameters of the main structural plane of the formation into a unit normal vector in the same coordinate system as the drilling direction vector; and a calculation subunit, used to perform a dot product operation on the unit drilling direction vector and the unit normal vector to obtain the angle between the drilling direction and the main structural plane of the formation; the angle is used as the input parameter of the prediction model.

[0030] In some embodiments, the device further includes: an assignment unit, configured to assign at least one of the following attributes under multiple preset directional angles calculated at each depth point of the target well and adjacent wells to a three-dimensional space according to the corresponding well trajectory spatial coordinates, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, minimum directional drillability index, dominant rock-breaking direction, difficult-to-drill direction, and directional drillability index under the actual drilling direction; and a collection and organization unit, configured to organize the assigned spatial data according to the seismic interpretation horizon and geological stratification results. Scattered data are stratified and organized according to their stratigraphic position, with data within the same stratigraphic position grouped into the same subset. This forms units, which are used to perform inter-well spatial predictions within each stratum, using seismic attributes, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The prediction results from each stratum are then overlaid according to the original spatial coordinates to form a three-dimensional directional drillability index model and a three-dimensional drillability anisotropy coefficient model for the target block.

[0031] A third aspect of this specification provides an electronic device, comprising: a memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to implement the formation drillability anisotropy quantitative evaluation method as described in any one aspect.

[0032] A fourth aspect of this specification provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the formation drillability anisotropy quantitative evaluation method described in any one of the first aspects.

[0033] The fifth aspect of this specification provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method for quantitative evaluation of formation drillability anisotropy as described in the first aspect.

[0034] The quantitative evaluation method for formation drillability anisotropy provided in this manual uses the main structural plane of the formation as a unified angular reference for multi-angle sampling and multi-type tests to obtain mechanical parameters, acoustic parameters, and drillability parameters under different lithologies and different included angle directions. This ensures that the drillability evaluation covers the entire angular range, providing quantitative data support for drill bit selection at any well inclination angle and avoiding problems such as poor matching and increased non-productive time caused by compromise selection. Based on rock-breaking specific energy, equivalent rock-breaking load, and drillability index calculated per revolution, the method quantitatively characterizes the difficulty of rock breaking in any drilling direction. Through preset multi-directional evaluation, it can clearly distinguish... The advantageous rock-breaking direction and the difficult-to-drill direction provide a quantitative comparison benchmark for wellbore trajectory optimization, avoiding the design trajectory from falling into the difficult-to-drill range. A predictive model is established with formation physical parameters and directional angles as inputs and directional drillability index as output. It can continuously output the directional drillability index at any depth point with logging data and at that depth and drilling direction. At the same time, by inputting the actual drilling direction and the target angle of the main structural surface into the model, the drillability index under the actual drilling direction is obtained. This upgrades the segmented parameter optimization of the whole well section from "experience-based trial and error" to "data-driven", and provides a single-well data foundation for subsequent three-dimensional spatial modeling of the block. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This document presents a flowchart illustrating a method for quantitatively evaluating the anisotropy of formation drillability. Figure 2 This is a flowchart illustrating another method for quantitatively evaluating the anisotropy of formation drillability provided in this specification. Figure 3 This is a schematic diagram of core samples being drilled from a rock mass along different directions. Figure 4 This is a schematic diagram of some core samples prepared according to the above sampling plan; Figure 5 This document provides a flowchart of a method for establishing a digital core model. Figure 6 Flowchart of the method for joint calibration of digital core models; Figure 7 This is a schematic diagram illustrating the training of the prediction model; Figure 8This document provides a flowchart of the method for calculating the target angle between the drilling direction and the main structural plane of the formation based on the wellbore trajectory parameters and formation structural plane attitude parameters at the target depth of the target well. Figure 9 This document provides a flowchart of the method for establishing a drillability anisotropic spatial model of a target block. Figure 10 A schematic diagram of three-dimensional drillability anisotropic space modeling for the target block; Figure 11 This is a schematic diagram of the structure of the formation drillability anisotropy quantitative evaluation device provided in this manual. Figure 12 This is a structural block diagram of an electronic device provided in this specification. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0038] To address the four major categories of engineering problems easily arising from existing methods for evaluating the anisotropy of rock drillability, this specification proposes a quantitative evaluation method for formation drillability anisotropy, such as... Figure 1 As shown, the method includes S10 to S70, and S80-S90 and / or S100-S110. The method may include only S80-S90 and not S100-S110, or only S100-S110 and not S80-S90, or both S80-S90 and S100-S110.

[0039] S10: Identify the main structural plane of the formation that plays a dominant role in the difference in drillability direction in the target formation. Using the main structural plane of the formation as a unified angle reference, conduct rock mechanics tests, acoustic tests, and drillability tests on core samples obtained by sampling cores of different lithologies from multiple angles to obtain the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples of different lithologies and different included angle directions.

[0040] In some embodiments, such as Figure 2 As shown, S10 may also include: performing microstructure testing on core samples obtained by sampling cores from different lithologies at multiple angles.

[0041] Directional core sampling is required before S10. Specifically, based on core observations of the target block, imaging logging interpretation, seismic tectonic interpretation, and geological stratification data, the spatial attitude of weak surfaces such as bedding planes and fracture planes can be identified. (Spatial attitude is a standard parameter in geology used to quantitatively describe the extension direction and dip state of planar geological structures such as bedding, fractures, and joints in the three-dimensional underground space, and it is the core basis for determining the spatial attitude of a weak surface.) Based on the development degree of various structural surfaces in the target strata and their influence weight on rock mechanical properties and rock breaking response, following the principle of "prioritizing the main controlling factors," the weak surface that plays a dominant role in drilling anisotropy is selected as the main structural surface, and directional core sampling is carried out based on this main structural surface.

[0042] Both bedding planes and fracture planes are planar geological weak surfaces where the internal strength of the rock is significantly lower than that of the matrix. They are the fundamental geological cause of the differences in drilling difficulty in different directions, and each has its own emphasis in terms of formation mechanism, development scale, and functional characteristics. The main structural plane is not an independent type of weak surface, but rather a core control interface selected from various geological weak surfaces based on its influence weight on drillability anisotropy. It serves as a unified reference benchmark for directional sampling and anisotropy evaluation.

[0043] For example, in sedimentary strata such as shale, layered mudstone, and thin interbedded sandstone, the continuous and stable distribution of bedding planes, with strong penetration, is the core factor causing directional differences in rock strength, acoustic wave propagation characteristics, and rock-breaking efficiency. Therefore, bedding planes are selected as the main structural planes for directional sampling and subsequent evaluation. In strata such as fractured carbonate rocks and dense fractured sandstone, the dense development of tectonic fractures has a much greater impact on the propagation path of cutting cracks and rock-breaking resistance than other structural planes, while the bedding characteristics are not significant. In this case, the dominant fracture plane is selected as the main structural plane. In metamorphic rocks such as schist and slate, the foliation plane is the main controlling weak plane of the rock. Therefore, the foliation plane is used as the benchmark for directional sampling. In homogeneous strata such as massive granite and thick homogeneous sandstone without significant directional weak planes, when it is determined that there is no main structural plane that dominates drillability anisotropy, sampling is performed based on the geometric axis of the core itself. Test samples are prepared along the axial, radial, and typical oblique directions of the core to verify the degree of development and magnitude of influence of strata anisotropy.

[0044] The sampling angle is defined as the angle between the core axis and the reference, and can be set to seven gradients: 0°, 15°, 30°, 45°, 60°, 75°, and 90°, fully covering the entire angular range from parallel to perpendicular structural planes. For homogeneous rocks with underdeveloped structural planes, sampling can be performed along the core axis, radial direction, and any oblique direction; for strongly anisotropic rocks with well-developed bedding, fractures, or weak planes, priority should be given to covering parallel structural planes, perpendicular structural planes, and multiple sets of oblique directions to ensure complete characterization of stratigraphic directional differences.

[0045] The above-mentioned directional sampling method can enable core samples prepared based on the same set of directional sampling rules to be tested, ensuring that the test objects and angle reference standards of the four types of tests are completely consistent. The resulting multi-source parameters are matched with the same caliber and can be used in a precise and coordinated manner, without causing data misalignment due to inconsistencies in samples or definitions.

[0046] Figure 3 This diagram illustrates the process of drilling core samples from a rock mass along different directions. Using bedding planes as a reference, core samples are drilled from the rock mass along directions parallel to the bedding planes (0°), perpendicular to the bedding planes (90°), and oblique to the bedding planes (15°, 30°, 45°, 60°, and 75°). The sampling angle θ is defined as the angle between the core axis and the bedding plane. This sampling scheme covers the entire angle range from 0° to 90°, systematically acquiring core samples in different directions, providing a complete series of oriented samples for subsequent laboratory tests and digital core modeling.

[0047] Figure 4 The images show physical (or 3D digital model) illustrations of some core samples prepared according to the above sampling scheme, illustrating the morphology of core samples at seven angular gradients: 0°, 15°, 30°, 45°, 60°, 75°, and 90°. All samples originated from the same rock mass, differing only in sampling direction, ensuring sample homology for subsequent mechanical, acoustic, drillability, and microstructure tests. This sample series comprehensively covers the entire angular range from parallel to perpendicular bedding, providing a data foundation with sufficient directional resolution for anisotropy evaluation.

[0048] Rock mechanics tests are used to quantitatively characterize the strength and deformation characteristics of rocks in different directions. They are a fundamental testing method for revealing the anisotropic laws of formation mechanics. The test results can be used to explain the mechanical causes of drillability anisotropy, and at the same time provide true values ​​for the calibration of the mechanical response of digital core models (where true values ​​represent the real objective properties of formation rocks, rather than the results of numerical calculations).

[0049] Rock mechanics experiments can be conducted using one or more combinations of uniaxial compression tests, Brazilian splitting tests, and triaxial compression tests, depending on the formation characteristics and testing requirements. Uniaxial compression tests are used to obtain the strength and deformation parameters of rocks under unconfined pressure conditions; Brazilian splitting tests determine the tensile strength of rocks using an indirect tensile method; triaxial compression tests simulate the actual confining pressure environment of the formation to obtain the shear strength parameters of the rocks. All tests are conducted comparatively on core samples from different sampling angles under uniform test conditions to obtain comparable data on directional differences in mechanical response.

[0050] Rock mechanics parameters obtained from rock mechanics experiments may include: elastic modulus, Poisson's ratio, uniaxial compressive strength, tensile strength, cohesion, and internal friction angle in different directions.

[0051] Acoustic wave testing is used to determine the propagation law of elastic waves in rocks in different directions. It is a key bridge connecting laboratory tests and field logging data. Its test aperture is consistent with that of field acoustic wave logging, which can realize the parameter connection from laboratory scale to wellbore scale.

[0052] Acoustic testing can be conducted separately for longitudinal and transverse waves. Elastic waves are excited into the core sample by transmitting transducers, and wave velocity and time difference data are collected by receiving transducers. The anisotropic characteristics of the acoustic waves are then calculated. The test results can be directly correlated with well logging data, providing intermediate mapping parameters for subsequent well logging inversion of drillability.

[0053] The acoustic parameters obtained from acoustic testing can include: longitudinal wave velocity, transverse wave velocity, longitudinal wave time difference, transverse wave time difference, and acoustic anisotropy coefficient in different directions.

[0054] Drillability testing is used to directly simulate the physical process of drill bit breaking rocks and quantitatively evaluate the difficulty of breaking rocks in different directions. It is the core true value test link for drilling anisotropy evaluation (where the true value represents the real objective properties of the formation rocks, rather than the deduction results of numerical calculation).

[0055] Drillability testing can be conducted using one or more of the following methods, depending on the test conditions and evaluation objectives: micro-drilling test, single-tooth cutting test, rock-breaking energy consumption test, or equivalent mechanical energy density test. The test simulates the cutting and drilling conditions of real wells, collecting dynamic data such as force, torque, drilling speed, and energy consumption during the drilling process. This provides direct data for subsequent calculation of the directional drillability index and calibration of the digital core rock-breaking response.

[0056] Drillability parameters obtained from drillability tests may include: rotational speed, cutting force, torque, rock breaking speed, rock breaking energy consumption, and drillability index in different directions.

[0057] Microstructure testing reveals the intrinsic causes of rock anisotropy at the microscale, providing structural basis for digital core microscale modeling and anisotropic mechanism analysis.

[0058] Microstructure testing can be performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), CT scanning, or thin section identification, depending on the testing requirements. This allows for the acquisition of information such as mineral composition, microstructure, spatial distribution of pores and fractures, and development characteristics of structural planes. It clarifies the control effect of factors such as bedding, fractures, and mineral orientation on drillability anisotropy, providing input for the microstructure reconstruction of digital core models.

[0059] Microstructural parameters obtained from microstructure testing can include: mineral composition, degree of bedding development, crack density, pore structure, and content of brittle minerals.

[0060] The S10 can use oriented core samples of different lithologies in the target block as test objects to carry out four types of indoor tests: rock mechanics tests, acoustic tests, drillability tests, and microstructure tests. It systematically obtains the basic parameters of mechanics, acoustics, rock breaking, and microstructure of rocks under different lithologies and drilling angles, and establishes a standardized experimental database containing lithology, sampling angle, mechanical parameters, acoustic parameters, drillability parameters, and microstructure parameters. This provides true benchmarks and training samples for subsequent digital core model calibration and drillability anisotropy prediction model construction.

[0061] It is important to note that in this invention, "rock-breaking response" and "drillability parameters" represent a hierarchical relationship between the measured process response and standardized evaluation indicators, corresponding respectively to the phenomenological layer and the evaluation layer in rock-breaking research. Rock-breaking response is the basis for calculating drillability parameters.

[0062] The integration of the above six types of parameters (lithology, sampling angle, mechanical parameters, acoustic parameters, drillability parameters, and microstructure parameters) into the same standardized experimental database is not a simple data aggregation, but rather produces a significant synergistic value-added effect. Specific technical effects are as follows: 1. Achieving homogeneous matching of multi-source data eliminates systematic errors. Specifically, all six types of parameters originate from the same set of directional core samples, with completely consistent angle definitions, ensuring natural physical consistency between data generated from different testing platforms. Mechanical parameters reflect the rock's strength characteristics, acoustic parameters reflect the rock's wave propagation characteristics, drillability parameters reflect the rock's rock-breaking response characteristics, and microstructure parameters reflect the rock's internal composition characteristics. These four types of information are obtained simultaneously from the same set of samples, avoiding systematic biases introduced by different sample batches, different orientation definitions, or different testing conditions. This homogeneous matching is the technical prerequisite for multi-source data fusion and the foundation for the digital core model to achieve joint calibration of the mechanical, acoustic, and rock-breaking fields.

[0063] 2. Constructing rigid constraints for multi-physics joint calibration enhances the uniqueness of the digital core model. Specifically, the digital core model calibration in subsequent steps is a typical inversion problem. If only a single type of response data is relied upon (such as using only mechanical parameters), the model parameters exhibit "multiple solutions," meaning that multiple different combinations of microscopic parameters may produce the same mechanical response. Adding acoustic parameters provides an independent constraint on the elastic stiffness of the rock; adding drillability parameters provides additional constraints on the damage and fracture parameters of the model due to rock-breaking energy consumption and cutting force; adding microstructure parameters provides a priori constraints on the range of values ​​for structural parameters due to the degree of bedding development and mineral composition. Multiple types of data impose constraints on the same set of model parameters from different physical dimensions, equivalent to an overdetermined set of equations, significantly compressing the feasible domain of the parameters and making the calibration results closer to physical reality.

[0064] 3. Revealing the causal transmission chain of "structure-mechanics-acoustics-fracture" to support a quantitative explanation of the drillability mechanism. Specifically, microstructural parameters (layer density, mineral orientation, cementation type) are the material basis and structural root of rock anisotropy; mechanical parameters are the macroscopic response of this microstructure under stress conditions; acoustic parameters are the dynamic, non-destructive expression of this macroscopic response; and drillability parameters are the engineering performance of this structural feature under drill bit cutting conditions. Joint analysis can quantitatively answer "what structural features lead to what degree of drillability anisotropy," giving the evaluation results mechanistic explanatory power, rather than merely remaining at the level of statistical correlation.

[0065] 4. Establish a multi-physics correlation mapping to provide a reliable calibration benchmark for continuous inversion of logging data. Specifically, acoustic logging data is the only continuously obtainable physical field information for the entire well section. Without paired data of laboratory acoustic parameters and drillability parameters, the statistical relationship between acoustic logging and drillability will be affected by various interference factors such as lithological variations, pore fluids, and wellbore conditions, making the correlation unreliable. By simultaneously measuring acoustic parameters and drillability parameters on a uniform core sample, a direct physical correspondence between the two can be established, enabling the accurate "translation" of acoustic logging data into drillability indices, supporting continuous inversion across the entire well section.

[0066] 5. Provides multi-objective calibration constraints for digital core simulation, improving the reliability of the simulation database. Specifically, the massive amounts of data generated by multi-parameter combined simulation of digital cores need to be anchored to limited experimental data. If the experimental database only contains mechanical parameters, the simulation results can only approximate the experiment in terms of mechanical response, but cannot be verified in terms of acoustic response and rock-breaking response. After combining multiple types of data, the simulation model needs to match the three types of experimental responses—mechanical, acoustic, and drillability—simultaneously; any mismatch in response triggers model correction. This multi-objective calibration mechanism ensures that the computational accuracy of the simulation model is balanced across different physical fields, and the extrapolation capability of the prediction model built based on this simulation database under different working conditions is significantly enhanced.

[0067] 6. Establish standardized data assets to support continuous expansion of subsequent wells and blocks. Specifically, once the structure of the standardized experimental database is established, core data from new wells and new formations can be continuously incorporated in the same format, making the database scalable. As the sample size increases, the generalization ability and applicability of the prediction model continuously improve. The same set of data assets can serve the pre-drilling scheme design and real-time drilling optimization of multiple wells within the block, forming continuous engineering value output.

[0068] The fusion of the aforementioned six types of parameters is not a simple superposition of various data, but rather eliminates data benchmark bias through matching oriented samples from the same source, reduces the ambiguity of parameter inversion through the joint constraints of multi-physics responses, reveals the intrinsic mechanism of drillability anisotropy through the causal chain of micro-genesis-intermediate attributes-engineering response, improves the reliability of digital core simulation results through multi-objective joint calibration, and supports the expansion of full-process engineering applications through a standardized data architecture. Ultimately, it achieves synergistic effects from multi-source data, with overall technical results far superior to evaluation results from a single data source. This multi-source fusion mechanism effectively solves the technical defects of existing single-source drillability evaluation methods, such as weak data constraints and insufficient directional characterization accuracy, which is one of the significant technical advantages of this invention compared to existing technologies.

[0069] In some implementation scenarios of this invention, the microstructure testing step can be selectively omitted based on engineering evaluation accuracy requirements, testing cycle and cost constraints, and the rock microstructure parameters can no longer be obtained.

[0070] Even without microstructure testing, quantitative evaluation of the anisotropy of drillability in formations with different lithologies and drilling angles can still be achieved through only three types of laboratory tests: rock mechanics tests, acoustic tests, and drillability tests. Compared to the full-parameter approach, this simplified implementation method suffers from some reductions in the depth of micromechanical interpretation, the accuracy of digital core simulation, and the reliability of parameter inversion: the initial modeling parameters for digital cores need to rely more on empirical value inversion for determination, resulting in increased calibration and iteration costs, and it cannot reveal the intrinsic control mechanism of drillability anisotropy at the microstructure level. However, the core evaluation function and engineering application value of the solution are not fundamentally affected, and it is suitable for application scenarios with moderate accuracy requirements and a focus on implementation efficiency and cost control.

[0071] As an optional embodiment of the present invention, the simultaneous incorporation of microstructure testing and mesostructure parameters can form a multi-dimensional synergistic gain with macroscopic mechanical, acoustic, and rock-breaking parameters. The core technical advantages include the following three aspects: 1. Complete mechanism explanation system: It can establish a complete causal logic chain from the microscopic structural genesis, through the macroscopic mechanical and acoustic intermediate properties to the rock breaking engineering response. From the microscopic dimensions such as mineral composition, degree of bedding development, fracture morphology and distribution pattern, it quantitatively reveals the internal control mechanism of drillability anisotropy, breaking through the limitation of existing technologies that can only fit macroscopic laws and cannot explain the genetic stratigraphic mechanism. 2. Improved simulation calibration accuracy: Using measured microstructural parameters as the initial modeling input for digital cores can significantly improve the geological realism of the initial model configuration and reduce the parameter inversion range and iteration number in the calibration process; at the same time, the addition of microscopic dimension constraints can effectively reduce the ambiguity of multi-parameter joint inversion, further improving the accuracy and reliability of digital core simulation output. 3. Enhanced engineering scalability: Based on the quantitative correlation between microstructural parameters and macroscopic drillability, parameter expansion and law extrapolation can be carried out for strata with different degrees of structural surface development and different mineral compositions, supporting the prediction of drillability in uncorked sections and blocks with similar geological conditions, effectively improving the universality and engineering scalability of the method.

[0072] S20: Establish a digital core model based on the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples with different lithologies and different included angles.

[0073] In some embodiments, such as Figure 2 As shown, S20 also uses the microstructure parameters when establishing the digital core model.

[0074] Digital core models are simulation models of the mapping relationship between the microscopic and macroscopic parameters of rocks. They are digital representations obtained by spatially discretizing real rock cores. Specifically, they use voxels (the smallest volume unit in space, whose side length depends on the imaging resolution or discretization accuracy) to characterize the rock skeleton, mineral composition, pore structure, and discontinuities such as bedding and cracks in real rock cores.

[0075] In this invention, the digital core model is further expanded into a comprehensive numerical representation system comprising three levels: a geometric model, a material model, and a mechanical model. The geometric model reflects the particle arrangement, bedding distribution, and fracture development characteristics of the real core in terms of spatial morphology. The material model is consistent with the mineral types and contents of the real core in terms of composition. The mechanical model matches the results of laboratory experiments in three physical fields: mechanical response (elastic parameters, strength parameters), acoustic response (longitudinal wave velocity, transverse wave velocity), and drillability response (cutting force, rock breaking speed).

[0076] The construction of digital core models follows a modeling principle based on the discrete spatial information of real core samples and constrained by physical experimental data. Specifically, firstly, the geometric structure information of real core samples is obtained through methods such as CT scanning, thin section identification, or digital image processing, and the core samples are discretized into three-dimensional digital geometries composed of voxel or particle units. Subsequently, initial microscopic constitutive parameters (including matrix elastic parameters, particle contact stiffness, cementation strength, bedding plane strength, fracture surface strength, friction coefficient, fracture criterion parameters, acoustic wave propagation parameters, and rock-breaking response parameters) are assigned to each unit of the geometry and the contact surfaces between units to form a material model. Finally, macroscopic response data (mechanical stress-strain curves, acoustic wave velocity, cutting force, and rock-breaking velocity) obtained from laboratory experiments are used as calibration targets. By adjusting the microscopic parameters, the macroscopic output of the digital core model is matched with the experimental data, thereby completing the calibration of the mapping relationship from "microscopic input" to "macroscopic output". The core of this construction principle lies in the fact that the geometric structure determines the initial spatial form and topological relationship of the model, the microscopic parameters determine the material properties and mechanical rules of the digital core model, and the macroscopic experimental data determines the final calibration state of the model. All three are indispensable.

[0077] In some embodiments, such as Figure 5 As shown, S20 may include the following S21 to S23.

[0078] S21: Classify core samples based on rock mechanical parameters, acoustic parameters, mineral composition, and structural characteristics.

[0079] The classification method can be K-means clustering, hierarchical clustering, density clustering or other clustering algorithms; the clustering input parameters include one or more of the following: elastic modulus, Poisson's ratio, compressive strength, tensile strength, P-wave velocity, S-wave velocity, density, bedding angle, fracture density and mineral composition.

[0080] S22: Determine the range of values ​​for the microscopic constitutive parameters of various types of core samples to form a material model.

[0081] The same digital core modeling method can be used for all types of core samples. Alternatively, the appropriate digital core modeling method can be determined for each type of core sample based on the classification results.

[0082] Digital core modeling methods can employ one or more of the following: discrete element model, finite element model, finite difference model, finite-discrete element coupled model, lattice Boltzmann model, digital core image reconstruction model, or multiphysics coupled model.

[0083] The constitutive parameters examined in detail include matrix elastic parameters, grain contact stiffness, cementation strength, bedding plane strength, fracture surface strength, friction coefficient, fracture criterion parameters, acoustic wave propagation parameters, and rock-breaking response parameters. The range of values ​​for each parameter was determined based on literature data, historical experimental data, or cluster analysis results for similar rocks.

[0084] S23: Obtain the geometric structure information of the real rock core, discretize the rock core into a three-dimensional digital geometry composed of voxels or particle units, and assign the material model to the three-dimensional digital geometry to form a digital rock core model.

[0085] Geometric structure information of real rock cores can be obtained through CT scanning, thin section identification, or digital image processing, and the rock cores can be discretized into three-dimensional digital geometries composed of voxels or particle units.

[0086] Three-dimensional digital geometry reflects the grain arrangement, bedding distribution, and fracture development characteristics of real rock cores in terms of spatial morphology.

[0087] In the phrase "and assigning the material model to the three-dimensional digital geometry," the material model refers to the various microscopic constitutive parameters mentioned above (matrix elastic parameters, cementation strength, bedding plane strength, etc.). These parameters are assigned to each element and the contact surfaces between elements of the three-dimensional digital geometry. For example, the contact stiffness between voxels A and B is set to a certain value, and the contact strength of the element at the location of the bedding plane is set to another value. At this point, the three-dimensional digital geometry is no longer an "empty shell"; each voxel element and contact surface possesses corresponding physical and mechanical properties.

[0088] S30: Based on the errors between the mechanical response of the digital core model and the rock mechanics test results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results, the mesoscopic constitutive parameters of the digital core model are jointly calibrated.

[0089] In some embodiments, such as Figure 6 As shown, S30 includes S31 to S34 as follows.

[0090] S31: Conduct mechanical response simulation, acoustic response simulation, and rock breaking response simulation on the initially constructed digital core model, and obtain the simulation mechanical response results, acoustic response results, and rock breaking response results respectively.

[0091] Mechanical response simulation, acoustic response simulation, and drillability response simulation can be performed based on physical governing equations and constitutive relations, and the difference between simulation data and laboratory test data can be calculated. Physical governing equations may include Newton's second law, laws of contact mechanics, and wave equations. Constitutive relations may include the stiffness-displacement relationship, strength-fracture criteria, and energy dissipation rules for voxel elements or contact surfaces under stress.

[0092] Mechanical response simulation refers to the numerical calculation of the entire process of deformation, yielding, and failure of rock under stress conditions, corresponding to indoor rock mechanics tests (uniaxial compression tests and triaxial compression tests). Mechanical response simulation can obtain the strength and deformation parameters of rock in different directions, providing mechanical background information for subsequent drillability evaluation. The mechanical response results are obtained by performing mechanical response simulation.

[0093] The specific content includes: applying displacement or force loading in specific directions (parallel bedding, perpendicular bedding, or oblique bedding) of the digital core model; the program, based on Newton's second law and the laws of contact mechanics, calculates the displacement, contact force changes, and bond fracture process of each element / particle under stress step by step until macroscopic failure of the rock occurs. The output simulation data may include: stress-strain curves, elastic modulus, Poisson's ratio, uniaxial compressive strength, tensile strength, and failure mode (tensile failure, shear failure, or mixed failure).

[0094] Acoustic response simulation refers to the numerical calculation of the propagation process of elastic waves (longitudinal and transverse waves) in a digital core model, corresponding to indoor acoustic velocity testing. Acoustic response simulation can obtain the acoustic properties of rocks, supporting the joint interpretation of subsequent well logging data and indoor experimental data. The acoustic response simulation is performed to obtain the acoustic response results.

[0095] The specific process involves applying transient or steady-state elastic wave excitation (force pulse or displacement disturbance) to one end of a digital core model, causing the wave to propagate within the model, and recording the wave arrival time and waveform at the other end. The program calculates the wave propagation velocity and attenuation between elements based on the wave equation and the model's elastic parameters (stiffness matrix, density, etc.). By changing the wave propagation direction (parallel to bedding, perpendicular to bedding, etc.), the acoustic response in different directions can be obtained.

[0096] The output simulation data may include: P-wave velocity, S-wave velocity, P-wave time difference, S-wave time difference, and acoustic anisotropy coefficient (P-wave velocity ratio or S-wave velocity ratio).

[0097] Drillability response simulation refers to the numerical calculation of the interaction between drill bit cutting teeth and rock, and the rock-breaking process. It corresponds to indoor drillability tests (micro-drilling tests, single-tooth rock-breaking tests). Drillability response simulation can obtain the rock-breaking response under drill bit cutting conditions, directly contributing to the calculation of the drillability index. Drillability response simulation yields the rock-breaking response results.

[0098] The specific process includes: setting a cutting tooth (simplified as a two-dimensional or three-dimensional rigid body) on the surface of a digital core model, applying a certain drilling pressure and cutting speed, and causing it to cut the rock along a set direction. Based on contact mechanics and fracture criteria, the program calculates the contact force between the cutting tooth and the rock element, the element bonding fracture, and the rock cuttings detachment process step by step, and records the mechanical response and energy consumption during the rock breaking process.

[0099] The output simulation data can include: cutting force (normal force, tangential force), torque, rock breaking speed, and rock breaking specific energy.

[0100] Mechanical response simulation ensures the model is "true" in terms of strength, acoustic response simulation ensures the model is "true" in terms of acoustics, and drillability response simulation ensures the model is "true" in terms of rock breaking. The joint calibration of the three ensures that the predictions of the digital core model under any subsequent working conditions have physical reliability.

[0101] For example, uniaxial compression simulation, Brazilian splitting simulation, acoustic wave propagation simulation, and drillability simulation were conducted on the digital core model. In the uniaxial compression simulation, compressive strength, elastic modulus, Poisson's ratio, and failure mode were obtained in different directions; in the Brazilian splitting simulation, tensile strength and crack propagation mode were obtained in different directions; in the acoustic wave propagation simulation, P-wave velocity, S-wave velocity, and acoustic wave transit time were obtained in different directions; and in the drillability simulation, rock-breaking speed, cutting force, torque, rotational speed, and drillability index were obtained in different directions.

[0102] When digital core models are used for simulation, they follow computational principles based on physical governing equations and microscopic constitutive parameters. When conducting mechanical response simulations, acoustic response simulations, or drillability response simulations based on the calibrated model, the simulation program uses three types of information stored in the model as the basis for computation: First, spatial discretization information, namely the spatial coordinates, geometric dimensions, and topological connections of each element, which defines the force transmission path and wave propagation channel; second, material property information, namely the specific values ​​of the aforementioned nine types of microscopic constitutive parameters, which define the behavior rules of each element and contact surface under stress, such as stiffness, strength, friction, fracture, and energy dissipation; and third, boundary conditions and loading information, namely the loading method, constraint conditions, and drill bit trajectory, which define the external driving conditions for the simulation. At each time step, the program iteratively calculates based on Newton's second law (the relationship between force and acceleration), the law of contact mechanics (the force-displacement relationship between particles or units), and the wave equation (the relationship between the speed and attenuation of sound wave propagation), and updates the force state, motion state, and contact state of each unit in turn, and outputs the macroscopic physical quantities at each time step.

[0103] Taking the Discrete Element Method (DEM) as an example, its computational cycle is as follows: calculate the resultant force on each element, solve Newton's second law to obtain acceleration, update velocity and displacement, update contact force, proceed to the next time step, and so on until the simulation ends. Throughout the entire calculation process, all macroscopic output quantities (stress, strain, wave velocity, cutting force, etc.) are uniquely determined by the preset mesoscopic constitutive parameters and geometric topological relationships in the model. Adjusting any mesoscopic parameter is equivalent to changing the mechanical behavior rules of the model. The program re-executes the above iterative calculation based on the updated parameters, and the output macroscopic response changes accordingly.

[0104] Digital core models, based on the aforementioned construction and computational principles, serve as a core hub connecting limited physical experimental data with large-scale engineering simulations. As a "reusable virtual experimental platform," each simulation output has a clear physical input-output relationship, ensuring the physical consistency and engineering reliability of the simulation dataset. It can conduct multi-physics numerical simulations under different boundary conditions, generating drillability anisotropy simulation data covering different lithologies, drilling directions, and working conditions. This overcomes the limitations of limited physical core sample quantity, insufficient sampling directions, and high experimental costs, supporting multi-scale collaborative evaluation from the core scale to the wellbore scale and then to the block scale.

[0105] It is important to note that in areas of the digital core model not covered by experimental data, the data is calculated by solving the equations of motion and contact mechanics using "calibrated physical rules + given boundary conditions." This mode of "using physical rules to drive calculations, rather than using data to drive interpolation" ensures the physical consistency, completeness, and reliability of the simulation dataset, which is the essential characteristic that distinguishes digital core simulation from purely data-driven methods.

[0106] S32: Calculate the mechanical response error between the simulated mechanical response result and the rock mechanics test result, the acoustic response error between the simulated acoustic response result and the acoustic test result, and the rock breaking response error between the simulated rock breaking response result and the drillability test result, respectively.

[0107] S33: Construct a comprehensive error function using the mechanical response error, acoustic response error, and rock-breaking response error as variables, and use the comprehensive error function as a unified convergence criterion for the joint calibration of multiple responses of the digital core model.

[0108] In some embodiments, the error function can be expressed as: E=α E m +β E a +γ E d Where E is the overall error, E mE represents the error between the mechanical response of the digital core model and the experimental results of rock mechanics. a E represents the error between the acoustic response and the acoustic test results. d The error between the rock breaking response and the drillability test results is represented by α, β, and γ, which are the weighting coefficients for various types of errors, and the value of γ is set differently according to the lithology category determined by the classification results.

[0109] S34: When the calculated value of the comprehensive error function does not reach the preset convergence threshold, the micro-constitutive parameters of the digital core model are iteratively adjusted, and the simulation calculation, error solving and comprehensive error judgment operations are repeatedly performed based on the adjusted parameters until the calculated value of the comprehensive error function meets the preset convergence threshold, and a digital core model with completed mechanical-acoustic-rock breaking multi-response joint calibration is obtained.

[0110] It should be noted that in the aforementioned process of establishing a digital core model, microstructural parameters (mineral composition, bedding development, fracture density, pore structure, brittle mineral content, etc.) obtained through X-ray diffraction, scanning electron microscopy, CT scanning, or thin section identification can be selectively introduced as supplementary constraints to the initial parameter range setting, depending on the available data. When microstructural test data is available, the initial value range of fine parameters such as bedding plane strength and fracture plane strength can be more reasonably limited based on the degree of structural development and mineral composition information, thereby improving calibration efficiency and model accuracy. If such data is temporarily unavailable, model calibration can be completed directly based on macroscopic data from multi-angle core laboratory tests, without affecting the establishment of the digital core model and subsequent simulation. The essence of microstructural parameters is to provide auxiliary prior constraints for model construction, rather than necessary conditions for the existence of the model.

[0111] S40: Using the jointly calibrated digital core model, change at least one of the lithological parameters, the angle of the main stratigraphic structural plane, and the multi-scale constitutive parameters to generate digital core models with different parameter combinations.

[0112] Using the jointly calibrated digital core model as a benchmark template, the system adjusts the lithological parameters, the angle of the main structural planes of the formation, and the constitutive parameters at multiple scales to generate digital core models with different parameter combinations. These models are then used for subsequent multi-directional rock breaking simulation and expansion of the drillability anisotropy database.

[0113] Lithological parameters refer to parameters that describe the composition and basic physical properties of rock materials, including mineral composition (content of quartz, feldspar, clay minerals, etc.), porosity, brittle mineral content, density, and grain size distribution. Lithological parameters determine the "material basis" of a rock, that is, what minerals make it up and how much pore space it contains. Changing lithological parameters can generate digital core model variants for different lithological types, such as highly brittle shale and highly porosity sandstone, allowing the simulation to cover the rock conditions of different lithological segments within the target block.

[0114] The principal structural plane angle refers to the spatial orientation angle of bedding planes, fracture surfaces, or weak planes in a digital core model; that is, the angle between the structural planes within the model and the reference direction. The principal structural plane angle is the inherent dip angle of the structural planes within the model, reflecting the anisotropic attitude of the strata themselves. By changing the dip angle settings of the structural planes, it is possible to examine the differences in rock-breaking response caused by different attitudes of the structural planes under the same set of lithology and constitutive parameters, providing a model basis for subsequent analysis of the anisotropic characteristics of drillability in different structural locations.

[0115] Multi-scale constitutive parameters, which are undetermined coefficients in constitutive relations, are the core inputs for digital core simulation calculations. They are divided into three levels according to spatial scale: mineral / particle-level parameters (particle elastic modulus, cementation strength, contact stiffness, internal friction coefficient, etc.) determine the microscopic deformation and damage initiation laws of the rock matrix; rock block matrix-level parameters (elastic modulus, Poisson's ratio, compressive strength, tensile strength, cohesion, internal friction angle, etc.) control the macroscopic deformation and strength limit of the intact rock block; and structural plane-level parameters (weak plane normal / tangential stiffness, weak plane cohesion, weak plane internal friction angle, weak plane tensile strength, etc.) are the core control parameters leading to directional differences. These parameters are further classified according to physical processes into rock mechanics-related parameters (controlling elastic deformation, plastic yielding, static strength limit) and rock fracture response-related parameters (controlling fracture toughness, dynamic damage evolution, strain rate sensitivity coefficient, etc.).

[0116] The three types of parameters mentioned above have a clear hierarchical relationship in their adjustment logic: lithological parameters determine the material composition and basic physical property levels of the model; multi-scale constitutive parameters further refine the mechanical behavior rules at each scale; together, they constitute a complete "material-mechanical" description of the model; the angle of the main stratigraphic structural plane changes the spatial attitude of the structural plane within this framework, expanding the research dimension of directional differences. The coordinated adjustment of these three parameters allows the generated digital core model to cover various rock state combinations within the target block, including different lithologies, structural characteristics, and mechanical properties, such as "highly brittle shale + high-angle bedding + strong cementation" or "high-porosity sandstone + low-angle bedding + weak cementation." Each set of parameters corresponds to a rock state with clear geological and mechanical meaning, providing a broad input sample space for subsequent batch expansion simulations.

[0117] The specific parameters for S40 adjustment include, but are not limited to: compressive strength, tensile strength, elastic modulus, Poisson's ratio, bedding angle, fracture density, cementation strength, mineral composition, porosity, matrix stiffness, bedding plane strength, and fracture surface friction coefficient. During adjustment, the values ​​of each parameter should not exceed the physically reasonable range for similar rocks (i.e., the aforementioned initial value range). Single-factor rotation (changing only one parameter at a time) or multi-factor orthogonal combination (multiple parameters changing simultaneously) methods can be used.

[0118] Due to the limited number of physical core samples, it is difficult to cover all rock states with different lithologies and structural characteristics within the target block using only a few sets of experimental data. By adjusting the system parameters mentioned above, a large number of digital core models with different parameter combinations can be generated. Each parameter combination essentially corresponds to a specific "rock state," providing diverse input carriers for subsequent multi-directional rock breaking simulations. This allows the simulation output to cover a wider range of rock state spaces, fundamentally compensating for the limitations of insufficient physical experimental samples and providing ample training samples for constructing a drillability anisotropy prediction model with sufficient generalization ability.

[0119] S50: Conduct multi-directional rock breaking simulations on digital core models with different parameter combinations, and obtain rock breaking specific energy, equivalent rock breaking load, and advance per revolution under different directional angles based on simulation data and indoor test data of the digital core model.

[0120] "Rock breaking energy, equivalent rock breaking load, and footage per revolution under different directional angles" refers to the rock breaking energy, equivalent rock breaking load, and footage per revolution calculated for the same rock or the same stratum in multiple different drilling directions.

[0121] Rock breaking energy (also known as rock breaking energy per unit volume) is the total energy required to break a unit volume of rock. The higher the rock breaking energy, the more energy is required to break a unit volume of rock, and the more difficult the rock is to drill.

[0122] When calculating the dimensions of a three-dimensional drill bit, the rock-breaking energy can be calculated using the following formula: , , in, The rock breaking energy is the rock breaking specific energy along the target angle θ direction. The cutting force is located in the direction of the target angle θ. The cross-sectional area of ​​the rock breaking. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. The rock-breaking speed (i.e., axial mechanical drilling speed) is the rock-breaking speed in the direction of the target angle θ. Let θ be the angular velocity of the drill bit in the direction of the target angle. This represents the drill bit rotation speed under the corresponding operating conditions.

[0123] When performing calculations in a two-dimensional single-tooth dimension, the rock-breaking energy can be calculated using the following formula: , in, The rock breaking energy is the rock breaking specific energy along the target angle θ direction. The normal force at the target angle. The tangential force at the target angle. The tangential cutting velocity at the target angle. The axial intrusion velocity at the target angle (i.e., the velocity at which the cutting edge intrudes into the rock along the normal direction). It represents the equivalent rock-breaking cross-sectional area of ​​a single tooth.

[0124] Rock-breaking load is the collective term for the reaction force and torque exerted by rock against the rock when rock-breaking tools such as cutting teeth / drill bits act on the rock during the rock-breaking process. The larger the equivalent rock-breaking load, the more difficult the rock is to drill in that direction.

[0125] When calculating the three-dimensional dimensions of a drill bit, the equivalent rock-breaking load can be calculated using the following formula: , in, The equivalent rock-breaking load in the direction of the target angle θ is... The cutting force is located in the direction of the target angle θ. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. Where is the cutting radius. Both cutting force and torque are considered, and the torque is converted into an equivalent tangential force.

[0126] When performing calculations in a two-dimensional single-tooth dimension, the equivalent rock-breaking load can be calculated using the following formula: , in, The two-dimensional single-tooth equivalent rock-breaking load is defined in the direction of the target angle θ. The normal force at the target angle. The tangential force at the target angle.

[0127] Footage per revolution refers to the depth of penetration into the rock along the axial direction per revolution of the drill bit (or equivalent cutting teeth). The smaller the footage per revolution, the more difficult it is to drill into the rock in that direction.

[0128] When calculating the dimensions of a three-dimensional drill bit, the advance per revolution can be calculated using the following formula: , , in, For each revolution, the advance is given by the target angle θ. The rock-breaking speed (i.e., axial mechanical drilling speed) is the rock-breaking speed in the direction of the target angle θ. Let θ be the angular velocity of the drill bit in the direction of the target angle. This represents the drill bit rotation speed under the corresponding operating conditions.

[0129] When calculating the two-dimensional single-tooth dimension, the advance per revolution can be calculated using the following formula: , in, The advance per revolution of a single tooth in two dimensions under the direction of the target angle θ is the axial depth of the cutting tooth penetrating the rock per revolution under the equivalent rotational working condition. The equivalent cutting radius is a preset geometric conversion parameter. The two-dimensional single-tooth simulation is a linear cutting motion and does not involve real rotation. This parameter can be used to convert the linear velocity of linear cutting into the rotational angular velocity of the rotating condition, thus unifying the evaluation criteria of two-dimensional and three-dimensional models. The tangential cutting velocity at the target included angle; The axial intrusion velocity at the target angle (i.e., the velocity at which the cutting ruler intrudes into the rock along the normal direction).

[0130] The calculations of the rock-breaking energy, equivalent rock-breaking load, and advance per revolution mentioned above are based not only on simulation data based on the digital core model, but also on the data from the aforementioned indoor tests (i.e., the various tests in S10).

[0131] S60: Based on simulation data and indoor test data based on digital core models, the directional drillability index is calculated at different directional angles based on rock breaking energy, equivalent rock breaking load, and footage per revolution.

[0132] Before calculating the drillability index, the rock breaking energy, equivalent rock breaking load, and footage per revolution can be dimensionlessly processed, and then the "average" rock breaking energy, "average" equivalent rock breaking load, and "average" footage per revolution can be calculated.

[0133] Specifically, the "average" rock-breaking energy, "average" equivalent rock-breaking load, and "average" footage per revolution are calculated using the following formulas: , , , The average rock breaking energy per unit volume under the target drilling direction is the arithmetic mean of multiple test results in this direction, representing the stable rock breaking energy consumption level per unit volume under this direction; n is the total number of independent tests under the target drilling direction, that is, the number of repeated tests / simulations carried out for the same target angle, which improves the stability and representativeness of the results through multiple measurements; i is the test sequence number. The actual drilling angle corresponding to the i-th test is the actual angle between the drilling direction and the main structural plane of the formation in this test (the actual angle may vary slightly each time due to the influence of sample preparation accuracy, clamping deviation and simulation grid error). For the i-th test (the actual included angle is...) The corresponding two-dimensional single-tooth rock-breaking specific energy is calculated from the parameters such as tangential force, normal force, and cutting speed output in this test.

[0134] In some embodiments, the drillability index in the direction of the target included angle θ can be calculated according to the following formula: , in, The drillability index is defined in the direction of the target included angle θ. The benchmark drillability index (for example, it can be set to 100). A drillability index greater than the baseline drillability index indicates that the drill is easier to drill than the average condition. The benchmark drillability index represents a near-average state. A drillability index lower than the baseline drillability index indicates that the drill is more difficult to drill than the average condition. and These are the weighting coefficients. , ,and ; The average rock-breaking energy per unit volume, For the average equivalent rock-breaking load, This represents the average advance per revolution.

[0135] It is important to note that while existing technologies offer numerous methods for calculating drillability indices, the method used in this invention differs from existing techniques. Specifically, existing technologies calculate an average, isotropic drillability index, failing to reflect the influence of formation anisotropy. In contrast, this invention explicitly proposes the concept of a "directional drillability index," defined as a function of the drilling direction angle θ. This allows the method to quantitatively evaluate rock drillability under different drilling directions (such as parallel or perpendicular to bedding planes).

[0136] The calculation of the aforementioned directional drillability index is based not only on simulation data based on the digital core model, but also on data from the aforementioned indoor tests (i.e., the various tests in S10).

[0137] S70: Using the directional drillability index as the output label of the prediction model, establish a prediction model between formation physical parameters and directional angle and directional drillability index based on simulation data of digital core model and indoor test data.

[0138] "Indoor test data" can be test data from various tests in S10, and "simulation data based on digital core model" can be mechanical response results, acoustic response results, and rock breaking response results obtained from various simulations in S40, as well as the drillability anisotropy coefficient calculated based on the rock breaking response results.

[0139] Formation physical properties are characteristic parameters related to drillability anisotropy. They are key input variables that can effectively reflect or characterize the variation of formation drillability with drilling direction. These parameters characterize the controlling factors affecting the differences in rock breaking direction from different physical dimensions (acoustic, mechanical, structural, and engineering), and are the basic inputs for establishing drillability anisotropy prediction models.

[0140] Specifically, these parameters can include the following five categories: 1. Acoustic parameters: P-wave velocity, S-wave velocity, P-wave transit time, S-wave transit time, and acoustic anisotropy coefficient. Acoustic velocity is the core bridge connecting laboratory experiments and well logging data; differences in acoustic velocity in different directions directly reflect the degree of anisotropy of the rock's elastic properties. 2. Rock mechanics parameters: Elastic modulus, Poisson's ratio, uniaxial compressive strength, tensile strength, cohesion, and internal friction angle. These parameters reflect the rock's ability to resist deformation and damage, and are the mechanical basis for controlling the ease of rock breaking. 3. Structural parameters: Bedding angle (the angle between the drilling direction and the bedding plane), fracture density, bedding development degree, and structural plane attitude (dip angle, dip direction). These parameters directly determine the geometric relationship between the drilling direction and the formation structural plane, and are the structural root of drillability anisotropy. 4. Drilling engineering parameters: Drilling pressure, torque, rotational speed, mechanical drilling rate, and bottom hole pressure differential. These parameters reflect the engineering response during actual drilling and can serve as auxiliary information in the model input; 5. Lithological and physical property parameters: density, mineral composition, porosity, and brittle mineral content. These parameters reflect the material composition and physical state of the rock, providing constraints for lithological classification and model generalization.

[0141] Through a three-tiered process of initial mechanism selection, data screening, and iterative optimization, the feature set can be ensured to possess both physical rationality and predictive effectiveness. The specific steps are as follows: First, mechanism-driven initial selection of the candidate set. Based on classical theories of rock mechanics and drilling rock engineering, and mature industry experience, all parameters related to rock fracturing mechanisms and anisotropy causes are initially selected to form an initial candidate feature set. This ensures that all candidate parameters have clear geological and mechanical connotations, eliminating redundant parameters without physical basis. Second, data-driven significance screening. Based on indoor experimental databases and pre-simulation datasets, a combination of Pearson correlation analysis and mutual information is used to quantitatively calculate the correlation between each candidate feature and the directional drillability index, eliminating weakly correlated features with correlations below a preset threshold. Simultaneously, one-way ANOVA is used to verify the significance of differences in each feature under different drilling directions, retaining only features sensitive to anisotropy changes. Third, optimization of the contribution of model iterations. During model training, redundant features with extremely low contribution are gradually eliminated by ranking the importance of features (such as the gain weight of a tree model and the regression coefficient of a linear model), ultimately forming the optimal feature subset. This reduces model complexity and improves model generalization ability while ensuring prediction accuracy.

[0142] The prediction model takes formation physical parameters (including logging parameters, mechanical parameters, sonic parameters, etc.) and directional angle (the angle between the drilling direction and the normal to the main structural plane of the formation) as inputs and outputs a directional drillability index. The model's task is to establish a mapping relationship from input to output, so that given the formation physical parameters and directional angle at any depth point, it can accurately predict the drillability index in that direction.

[0143] In some embodiments, the input parameters of the prediction model include: P-wave velocity, S-wave velocity, density, acoustic transit time, elastic modulus, Poisson's ratio, compressive strength, tensile strength, and drilling direction angle. The mapping relationship of the prediction model can be expressed as: ,in, For direction The drillability index below; For longitudinal wave velocity; The transverse wave velocity; Density; For sound wave time difference; It is the elastic modulus; Poisson's ratio; Uniaxial compressive strength; Tensile strength; This refers to the drilling direction or sampling direction angle.

[0144] This embodiment is merely one example of how the prediction model can be implemented. In actual implementation, the input and output parameters of the prediction model can be adjusted according to the actual situation. However, the input data must include logging parameters (such as rock density), rock mechanics parameters (such as elastic modulus, Poisson's ratio, uniaxial compressive strength, tensile strength), acoustic parameters (such as P-wave velocity, S-wave velocity, and acoustic transit time), and drilling direction angle.

[0145] The training sample set for the prediction model comes from two sources: first, an experimental database obtained from multi-angle indoor experiments, containing mechanical parameters, acoustic parameters, drillability parameters, and microstructure parameters under different lithologies and sampling angles; second, a simulation database obtained from digital core multi-parameter combination simulation, containing rock parameters, structural plane parameters, acoustic responses, and rock-breaking responses under different parameter combinations and directions. The data from the two databases are merged according to a unified data format and field definitions to form the original sample set.

[0146] Based on this, sample preprocessing is carried out, including the following operations: normalization, which maps the numerical range of each input feature to the same order of magnitude to eliminate the impact of dimensional differences on model training; missing value handling, which imputs or removes missing values ​​in the samples due to experimental limitations or incomplete data collection; and feature selection, which uses correlation analysis or feature selection algorithms to select a subset of features from the original feature set that are highly correlated with the directional drillability index and have clear physical meaning, and removes redundant or excessively noisy variables. The preprocessed sample set is divided into training, validation, and test sets according to a preset ratio, or cross-validation is used for model training and evaluation.

[0147] Predictive models can be implemented using various methods, including but not limited to: multiple regression, nonlinear fitting, random forest, support vector machine, neural network, physical constraint regression, or one or more of the mechanism-data fusion models. Preferably, a mechanism-data fusion model can be used, where the mechanism part (such as rock breaking energy theory or rock fracture mechanics model) provides physical constraints and prior knowledge, and the data part (such as machine learning algorithms) is responsible for fitting residual errors and complex nonlinear relationships, so that the model has high accuracy within the coverage of training data and maintains physical rationality outside the coverage.

[0148] like Figure 7As shown, during model training, both indoor experimental samples and digital core simulation samples serve as the sample library. The training set is used for model parameter learning, the validation set is used for hyperparameter tuning and overfitting monitoring, and the test set is used for independent evaluation of the final model performance. Model evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Preferably, cross-well validation or adjacent-well validation can be used to test the model's generalization ability; that is, the model is trained using data from some wells, and the prediction effect is validated using data from other wells to assess the model's applicability under different well location conditions.

[0149] After model training is completed, the model accuracy needs to be systematically verified. The verification process is as follows: If the model achieves all preset accuracy thresholds on both the validation and test sets, it passes the verification and outputs a drillability anisotropy prediction model for subsequent target well drillability anisotropy inversion. If the model accuracy does not meet the requirements, the representativeness of the training samples, the effectiveness of feature selection, and the rationality of the model structure are checked. Iterative optimization is then performed by increasing sample diversity, adjusting feature combinations, changing the model algorithm, or adjusting hyperparameters until the model accuracy meets the preset requirements.

[0150] The prediction model, verified for accuracy, outputs results on two levels: first, a directional drillability index, which is the predicted value of the drillability index for a given formation physical parameters and directional angle; second, a drillability anisotropy coefficient, which is an evaluation index of anisotropy calculated by comparing index values ​​in different directions after obtaining drillability indices for multiple directions. The former is the direct output of the model, while the latter is a derived application of the model output. Together, they constitute a quantitative index system for drillability anisotropy evaluation, providing core data support for the inversion of the entire well section of the target well and the three-dimensional spatial modeling of the block.

[0151] It should be noted that the digital core model described in this invention is not an independent rock numerical simulation technology module, nor is the prediction model an independent general data fitting model. The two are not simply a juxtaposition of conventional techniques in the field, but rather an organic whole with a clear data transfer relationship and functional coupling relationship, formed through a unified angle benchmark, multi-response joint calibration, and directional sample expansion. This collectively supports the cross-scale extension of finite core test results to engineering scales. Specifically, this invention uses indoor multi-angle rock mechanics, acoustic, and drillability test data obtained under the same principal structural plane angle benchmark as a benchmark. The joint calibration of the digital core model is completed through comprehensive error constraints composed of mechanical response, acoustic response, and rock-breaking response. This allows the digital core to simultaneously reproduce the mechanical deformation characteristics, acoustic propagation characteristics, and rock-breaking characteristics of the rock, ensuring the consistency between simulation results and indoor test results. The calibrated digital core model is used to expand the simulation sample space under different lithologies, different structural plane angles, and different combinations of mechanical parameters, and through rock-breaking response... The calculated directional drillability index serves as a training label for subsequent prediction models, compensating for the limitations of limited sample size, incomplete directional coverage, and high testing costs in indoor experiments. This provides sufficient and reliable training data across all directions for the prediction models. The prediction models, using the directional drillability index as the training target, establish mapping relationships between continuous logging parameters, mechanical parameters, sonic parameters, drilling parameters, and the drilling direction angle to the directional drillability index. Combined with the calculated angle between the target well's wellbore trajectory and the formation's principal structural plane, continuous inversion of the drillability index across the entire wellbore under the actual drilling direction of the target well can be achieved. In summary, the digital core model, the directional drillability index calculation system, and the prediction model are sequentially linked through input-output dependencies and parameter constraints. These three elements work together to solve the technical problem of directly extending the results of limited multi-angle core tests to the quantitative evaluation of formation drillability anisotropy across the entire wellbore, in any drilling direction, and in the three-dimensional space of the target block. This achieves technical effects that cannot be realized by either individual digital core modeling or individual prediction model construction.

[0152] In practical applications, there are two types of outputs: The first type is omnidirectional output (as described in S80 and S90 below), which involves traversing multiple directional angles such as 0°, 15°, 30°, 45°, 60°, 75°, and 90° at a certain depth point of the target well to obtain the drillability index under different directions; the second type is the actual drilling direction output of the target well (as described in S100 and S110 below), which involves calculating the drilling direction vector based on the wellbore trajectory of the target well, calculating the structural plane normal vector based on the formation bedding plane, fracture plane, or weak plane attitude, and then obtaining the angle between the actual drilling direction of the target well and the structural plane. This angle is then input into the prediction model to obtain the drillability index under the actual drilling direction of the target well.

[0153] S80: For the target depth point of the target well, the formation physical property parameters corresponding to the target depth point are combined with multiple preset directional angles and then input into the prediction model to obtain the directional drillability index sequence under different directional angles; S90: Calculate at least one of the following based on the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction according to the drillability index sequence.

[0154] For multiple preset directional angles at the same depth point or the same core sample, calculate the directional drillability index for each angle, forming a directional drillability index sequence: .

[0155] The maximum directional drillability index, minimum directional drillability index, and average directional drillability index are calculated using the following formulas. , , , in, The maximum directional drillability index. The minimum directional drillability index. The average directional drillability index. It is a set of angles between multiple preset directions; The drillability anisotropy coefficient A is calculated using the following formula. D : .

[0156] Among them, A D The larger the value, the more significant the difference in drillability under different drilling directions; A D A value close to 0 indicates that the difference in drillability is small within the evaluation direction range.

[0157] Dominant rock-breaking direction θ easy for: , Difficult drilling direction θ hard for: .

[0158] Regarding the "quantitative identification of superior and inferior drilling azimuths under all working conditions", existing anisotropy evaluation methods can only obtain the relative ratio of the two extreme directions of parallel and perpendicular bedding, which cannot cover most actual working conditions such as oblique drilling, nor can they output the absolute drillability values ​​of each direction. Superior and inferior drilling azimuths can only be roughly judged based on engineering experience, lacking reliable quantitative basis. S120 and S90 can rely on pre-trained drillability anisotropy prediction models, using logging data, rock mechanics parameters, sonic parameters, and drilling parameters of the target well and target depth as fixed inputs. Through omnidirectional gradient scanning, they can complete batch calculations and output directional drillability indices covering all drilling conditions, including in-seam, oblique, and cross-seam drilling. Based on the quantitative correspondence between the drillability index and rock breaking difficulty, they can accurately locate the advantageous rock breaking direction interval corresponding to the drillability peak value and the difficult drilling direction interval corresponding to the drillability valley value. They can also divide the suboptimal rock breaking and relatively difficult drilling into multiple gradient intervals according to the on-site construction tolerance requirements, realizing the transformation of the superior and inferior drilling orientation from "empirical qualitative judgment" to "numerical quantitative classification".

[0159] The quantitative results can directly support the trajectory orientation selection in the drilling design stage, avoiding difficult drilling areas from the source. At the same time, it provides accurate directional dimension basis for the selection of drill bits for different directions and the matching of drilling parameters for different sections, avoiding problems such as poor formation adaptability and abnormal wear of cutting teeth caused by traditional compromise selection.

[0160] Regarding "supporting real-time dynamic correction of drilling direction to adapt to dynamic changes in actual drilling conditions," existing evaluation methods are mostly static single-point evaluations during the design phase. These methods cannot adapt to changes in working conditions caused by formation property fluctuations and wellbore trajectory deviations during actual drilling. Drilling direction adjustments can only rely on trial and error based on field experience, which can easily lead to complex downhole conditions such as abnormal torque fluctuations, sliding pressure, and premature drill bit failure. S120 and S90, based on predictive models, can adapt to real-time input of drilling data. During actual drilling, by combining real-time formation parameters obtained from logging while drilling and wellbore trajectory data measured while drilling, the drillability evaluation results for each drilling direction in the current well section can be dynamically updated. When signs of difficult drilling such as a sudden drop in mechanical drilling rate, excessive torque fluctuations, and excessive sliding pressure occur during actual drilling, quantitative references for fine-tuning towards the advantageous rock-breaking direction can be quickly provided based on the drillability distribution results in all directions. Dynamic optimization of the drilling direction can be completed without stopping work for retesting.

[0161] S100: Calculate the target angle between the actual drilling direction of the target well and the main structural plane of the formation, based on the wellbore trajectory parameters and the occurrence parameters of the main structural plane of the formation.

[0162] In some embodiments, such as Figure 8 As shown, S100 may include S1001, S1002 and S1003.

[0163] S1001: Convert the spherical or discrete coordinate representation of the wellbore trajectory into a unit drilling direction vector in a three-dimensional Cartesian coordinate system.

[0164] Well inclination angle and azimuth angle are angles defined relative to a vertical line and true north, belonging to the spherical coordinate system; stratigraphic attitude is an angle defined relative to a horizontal plane and true north. Since they belong to different angle definition systems, spatial angle calculations cannot be performed directly. Only after converting them to vectors in the same coordinate system can precise spatial relative angles be obtained through standard vector operations.

[0165] S1001 can uniformly convert the spherical coordinate representation (inclination angle, azimuth angle) or discrete coordinate representation of the wellbore trajectory into a unit drilling direction vector in a three-dimensional Cartesian coordinate system (east-north-down), providing a unified vector reference for subsequent spatial angle calculations.

[0166] In a typical surveying data scenario where the wellbore inclination angle I and azimuth angle A are known (defined in the wellbore coordinate system: x: east; y: north; z: vertically downward; azimuth angle A: calculated clockwise from north; inclination angle I: calculated vertically downward), the spherical coordinate parameters can be directly converted into unit direction vectors using trigonometric functions. Specifically, the calculation method can be expressed as follows: , in, Here, I is the unit drilling vector, I is the well inclination angle, and A is the azimuth angle.

[0167] In scenarios where the spatial coordinates of the wellbore trajectory are known, the average drilling direction vector within a segment can be calculated using the coordinate difference between adjacent measuring points, making it adaptable to coordinate-based trajectory data. Specifically, the calculation method can be expressed as follows: , in, Unit drilling vector; X i+1 X i These are the eastward coordinates (X-axis, positive for east) of the (i+1)th and ith measuring points, respectively. i+1 -X i Y represents the eastward coordinate increment for this well section. i+1 Y i Let Y and Y be the north coordinates (Y-axis, positive north) of the (i+1)th and ith measuring points, respectively. i+1 -Y i Z represents the northward coordinate increment for this well section. i+1 Z i Let Z be the vertical coordinates (Z-axis, vertical downward is positive) of the (i+1)th and ith measuring points, respectively. i+1 -Z i This represents the vertical coordinate increment for this well section; The two methods for calculating the unit drilling direction vector described above complement each other and can cover different data sources on site.

[0168] S1002: Convert the geological occurrence parameters of the main structural plane of the formation into a unit normal vector in the same coordinate system as the drilling direction vector.

[0169] By converting the geological occurrence parameters of the main structural planes of the formation (bedding, fractures, etc.) into unit normal vectors in the same coordinate system as the wellbore trajectory (east-north-lower three-dimensional Cartesian coordinate system), the formation occurrence can be vectorized and a unified calculation benchmark can be provided for subsequent calculation of the spatial angle between the drilling direction and the bedding plane.

[0170] Specifically, S1002 can be converted according to the following formula: , in, δ is the unit normal vector of the main structural plane of the stratum, β is the dip angle of the main structural plane of the stratum, and β is the dip azimuth angle of the main structural plane of the stratum.

[0171] At that time, the main structural plane of the stratum is a horizontal plane, and the normal vector is in the vertical direction.

[0172] S1003: Perform a dot product operation between the unit drilling direction vector and the unit normal vector to obtain the angle between the drilling direction and the main structural plane of the formation; the angle is used as the input parameter of the prediction model.

[0173] Specifically, the angle between the drilling direction and the normal vector of the main structural plane of the formation can be expressed as: , in, This indicates that the drilling direction is parallel to the main structural plane of the formation. This indicates that the drilling direction is perpendicular to the main structural plane of the formation. The larger the value, the closer the drilling direction is to perpendicular penetration of the strata; The smaller the value, the closer the drilling direction is to parallel bedding.

[0174] S110: Input the formation properties corresponding to the target angle and the target depth point into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock breaking difficulty under the actual drilling direction.

[0175] First, the matching and alignment of input parameters are completed: For the target depth point of the target well, two types of input features are retrieved simultaneously: (1) The target angle corresponding to the depth point, that is, the angle between the drilling direction and the main structural surface of the formation, which is calculated by the well trajectory parameters and formation structure surface attitude parameters in the previous steps. This parameter uniquely corresponds to the spatial relative relationship between the actual drilling direction and the formation directional structure at this depth, and is the core input variable that reflects the anisotropic directional characteristics; (2) The logging data, rock mechanics parameters, sonic parameters, and drilling parameters under the corresponding working conditions corresponding to the depth point. The above parameters together constitute the basic physical properties of the formation and the characteristics of the drilling working conditions. After standardization and preprocessing, all input parameters are consistent with the feature dimensions and numerical scales in the training stage of the prediction model to ensure the accuracy of the model output.

[0176] Subsequently, all preprocessed input features are fed into the pre-trained drillability anisotropy prediction model. Based on the mapping relationship between "multi-dimensional physical property features + directional features" and "drillability index" established during the training phase, the model directly outputs the absolute drillability index corresponding to the actual drilling direction at the target angle. Unlike existing technologies that can only obtain the relative anisotropy ratio of the two extreme directions of parallel and perpendicular bedding, the drillability index output by this scheme is an absolute quantitative value corresponding to the actual working condition, without the need for additional directional conversion and empirical correction. It can directly reflect the rock's resistance to fracture under the actual drilling direction and can be adapted to complex working conditions in horizontal wells and highly deviated wells where the wellbore and formation have arbitrary oblique angles.

[0177] Finally, a quantitative evaluation of the rock-breaking difficulty is completed based on the output drillability index: the drillability index is negatively correlated with the formation rock-breaking difficulty; that is, the larger the drillability index, the lower the rock-breaking energy consumption, the smaller the rock-breaking resistance, and the higher the drilling efficiency in that direction, and the easier the formation rock-breaking difficulty; the smaller the drillability index, the higher the rock-breaking energy consumption, the greater the rock-breaking resistance in that direction, and the more difficult the formation rock-breaking difficulty. In engineering applications, the drillability index can be divided into different levels such as easy to drill, relatively easy to drill, moderately drillable, relatively difficult to drill, and difficult to drill, based on the rock drillability classification system commonly used in the oil and gas industry, to intuitively represent the rock-breaking difficulty in the actual drilling direction at the target depth.

[0178] S100 and S110 address the accuracy deficiencies of existing evaluation methods that use average drillability indices or compromise values ​​of dual-limit directions to replace actual oblique working conditions. The evaluation results are highly consistent with the actual drilling rock breaking conditions, and can directly provide quantitative decision-making basis for single-well drill bit selection verification, segmented drilling parameter optimization, and early warning of difficult-to-drill sections.

[0179] The present invention provides a quantitative evaluation method for formation drillability anisotropy, which constructs a full-process quantitative evaluation system for formation drillability anisotropy covering laboratory tests, digital core simulation, prediction model construction, and single-well multi-mode application. Addressing the shortcomings of existing technologies such as single evaluation dimensions, insufficient sample support, and weak engineering applicability, this method achieves refined evaluation of drillability across multiple scales, dimensions, and scenarios. Specific technical effects are as follows: (1) Using the main structural plane of the formation as a unified angular benchmark, core samples of different lithologies are taken from multiple angles, and rock mechanics tests, acoustic tests, and drillability tests are carried out in parallel to obtain rock mechanics parameters, acoustic parameters, and drillability parameters under different lithologies and different included angle directions. Compared with existing methods that only rely on data from parallel and vertical directions, this invention enables drillability evaluation to cover the entire angular range from parallel structural planes to vertical structural planes, providing quantitative data support for drill bit selection under any well inclination angle, avoiding problems such as poor drill bit matching and increased non-productive time caused by compromise selection, and solving the problem of lack of full-angle data support for drill bit selection; (2) Based on rock-breaking energy, equivalent rock-breaking load, and footage per revolution, the directional drillability index is calculated under different directional angles. This index is an absolute value indicator that can quantitatively characterize the difficulty of rock breaking under any drilling direction. By combining formation physical parameters with multiple preset directional angles and inputting them into the prediction model, a sequence of directional drillability indices under different directions can be obtained. Based on this, the dominant rock-breaking direction and the difficult-to-drill direction can be clearly distinguished, providing a quantitative comparison benchmark for wellbore trajectory optimization, avoiding the design trajectory from falling into the difficult-to-drill range, and solving the problem of lack of quantitative basis for wellbore trajectory optimization.

[0180] (3) Using the directional drillability index as the output label, a predictive model is established between formation physical parameters, directional angle, and directional drillability index. This model uses formation physical parameters (such as logging parameters) and directional angle as input features, and can continuously output the directional drillability index at any depth point with logging data and at that depth and drilling direction, without relying on coring data. At the same time, by inputting the target angle between the actual drilling direction and the main structural plane of the formation into the model, the directional drillability index under the actual drilling direction can be obtained, directly evaluating the rock breaking difficulty of the current drilling direction, upgrading the segmented parameter optimization of the entire well section from "experience trial and error" to "data-driven", and solving the problem of missing continuous drillability evaluation of the entire well section.

[0181] (4) The above prediction model can continuously output evaluation results such as directional drillability index and drillability anisotropy coefficient at each depth point in the entire well section of the target well. After these results are assigned according to the spatial coordinates of the well trajectory, they can form a spatial scattered dataset with the well trajectory as the carrier, which can be used as the "hard data" input for subsequent block three-dimensional spatial modeling, laying the data foundation for the expansion from single-well evaluation to block spatial prediction, and providing single-well data foundation for block three-dimensional spatial modeling.

[0182] Using the main structural plane of the formation as a unified angular benchmark, this method enables multi-angle indoor experiments, digital core joint calibration, multi-parameter combined simulation expansion, directional drillability index calculation, prediction model establishment, and single-well multi-mode application. This forms a set of quantitative evaluation methods for formation drillability anisotropy that starts from limited experimental data, is expanded through physical simulation, and ultimately serves actual drilling engineering decision-making.

[0183] The aforementioned quantitative evaluation method for formation drillability anisotropy upgrades "formation drillability" from a traditional single average value to a "function that changes continuously with direction." Through a progressive logic of anchoring the true value through limited experiments, expanding the sample through digital model physical simulation, and applying the prediction model throughout the well section, it achieves quantitative evaluation of drillability anisotropy from the core scale to the wellbore scale, providing a quantitative decision-making basis covering the entire angular range for drill bit selection, trajectory optimization, and parameter adjustment.

[0184] Using the above S10 to S70, S100 and S110, the drillability of a single well can be calculated point by point along the wellbore trajectory through the prediction model. That is, by inputting the logging parameters, mechanical parameters and drilling direction angle of a well at each depth, the drillability index and drillability anisotropy coefficient corresponding to each depth point of the well can be output.

[0185] The final result of these processing steps is a series of discrete data points distributed along the "line" of the wellbore. Each point carries the well depth, spatial coordinates, and corresponding drillability data, which can only answer "whether the location of this well is easy to drill," but there is no direct drillability data for the large areas of formation between wells that have not been drilled.

[0186] Actual oilfield development is geared towards the entire three-dimensional block. Knowing the drillability of any location within the block in advance is crucial for well placement, drilling program planning, and overall block drilling rate prediction. This requires expanding the "control point data on a single well line" into a "three-dimensional data volume" covering the entire block, incorporating multi-source information such as geological and seismic data. To address this, this invention further proposes establishing a drillability anisotropic spatial model of the "target block" based on the prediction model.

[0187] pass Figure 9 The drillability anisotropy space model of the target block is established in the manner described in S121 to S123 below. (Refer to...) Figure 10 To understand.

[0188] S121: Assign at least one of the following values ​​under multiple preset directional angles calculated from the depth points of the target well and adjacent wells to the three-dimensional space according to the corresponding well trajectory spatial coordinates, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, minimum directional drillability index, dominant rock-breaking direction, difficult-to-drill direction, and directional drillability index under the actual drilling direction.

[0189] The multi-attribute results (including directional drillability index, drillability anisotropy coefficient, maximum / minimum directional drillability index, dominant rock-breaking direction, difficult-to-drill direction, and directional drillability index under the actual drilling direction) obtained from the inversion calculation of each depth point of the target well and adjacent wells are assigned to their corresponding positions in three-dimensional space according to the spatial coordinates of the well trajectory at each depth point, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier. The inversion results of the above depth points constitute the "hard data" or "true value control points" of the three-dimensional model of the target block. That is, all subsequent inter-well interpolation predictions use the known data at these well locations as the reference anchor points, and the interpolation results must precisely match these values ​​at the well points. Each data point also carries its own layer label, which is used for subsequent data collection and stratified interpolation by layer, ensuring that the data of different layers are clearly distinguishable in spatial classification, providing a data foundation for the stratified collection of S122.

[0190] Figure 10 The “single well result values” and “target well drillability anisotropy inversion results” correspond to the input data for this step, while “well trajectory spatial location assignment” and “establishing a single well modeling database (drillability index, anisotropy coefficient, well depth, spatial coordinates)” correspond to the assignment operations and data organization for this step.

[0191] Drillability index for the entire well section of each target well. These are the "true control points" of the entire target block model, and subsequent interpolation must be based on the measured / inversion results of these wells.

[0192] Each data point can carry its own layer label, which can be used for subsequent layered organization and interpolation.

[0193] S122: Based on the seismic interpretation horizon and geological stratification results, the assigned spatial scatter data are stratified and organized according to their respective horizons, with data within the same horizon grouped into the same data subset.

[0194] Data from different strata are stored independently, forming a single-well modeling database organized by stratum.

[0195] For each data point that has been assigned a three-dimensional spatial coordinate, the geological stratum to which it belongs (such as stratum A, stratum B, stratum C, etc.) is first determined based on the depth and spatial location of the well trajectory, combined with the seismic interpretation of the stratigraphic interface and the geological stratification results. Then, all data points within the same stratum are grouped into the same data subset, and independent sample datasets are established for different strata. Each data subset is independent of each other and does not participate in the subsequent interpolation calculation.

[0196] The core purpose of the aforementioned stratification process is to establish a stratigraphic constraint mechanism for subsequent spatial interpolation. Different geological strata, formed in different sedimentary environments and geological periods, may exhibit fundamental differences in lithological assemblage, mechanical properties, and drillability. Within the same block, the drillability values ​​of shallow, soft strata and deep, dense strata may differ by several times, and their spatial distribution is controlled by their respective independent sedimentary facies and tectonic morphology. If data from different strata are mixed together for unified interpolation, it is equivalent to conflating strata with drastically different physical properties, inevitably resulting in non-physical interpolation results across strata near stratigraphic boundaries (such as forcibly smoothing a transition between shallow, high-drillability data and deep, low-drillability data, creating a non-existent "intermediate" false layer), severely distorting the geological rationality of the 3D model. Through stratified aggregation and layer-by-layer interpolation, subsequent predictions are ensured to be performed only within their respective strata, fundamentally avoiding the mixing of strata of different geological ages, lithological assemblages, and mechanical characteristics by cross-strata interpolation, thus guaranteeing the geological rationality and engineering reliability of the interpolation results.

[0197] The purpose of stratified data aggregation is to establish stratigraphic constraints for subsequent inter-well spatial prediction. Different geological strata were formed in different sedimentary environments and geological periods, and their lithological assemblages, mechanical properties, and drillability characteristics differ fundamentally. Mixing and uniformly interpolating data from different strata will inevitably lead to non-physical prediction results across strata near the strata boundaries. By organizing data independently by stratum, it is ensured that subsequent interpolation predictions are performed only within their respective strata. In some embodiments, this can be further organized by lithofacies zoning as a supplementary constraint for secondary zoning within strata.

[0198] Figure 10 In this step, "stratigraphic modeling by stratigraphy" and "geological stratification data" together correspond to the stratification collection operation; "organization by lithofacies zoning" and "lithofacies distribution data" serve as auxiliary constraints and can be included simultaneously in this step.

[0199] S122 is a crucial intermediate link connecting "single-well data spatial positioning" and "stratum-constrained inter-well interpolation". Only after completing the stratified data collection can the subsequent stratum-constrained interpolation have a data foundation.

[0200] S123: Using seismic attributes, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, within each stratum, inter-well spatial prediction is carried out for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The prediction results of each stratum are superimposed according to the original spatial coordinates to form a three-dimensional directional drillability index model and a three-dimensional drillability anisotropy coefficient model for the target block.

[0201] Using seismic attributes, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, inter-well spatial predictions are performed within each stratum for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The spatial prediction methods include one or more of Kriging interpolation, co-Kriging interpolation, fractal interpolation, sequential Gaussian simulation, machine learning-based spatial prediction methods, or seismic-constrained inversion modeling. Co-Kriging interpolation with seismic attribute trend constraints is preferred, incorporating seismic attributes as covariate variables into the interpolation calculation to improve inter-well prediction accuracy. Each fault serves as a rigid boundary, and interpolation calculations must not cross faults; interpolation is performed independently on both sides of the fault. Lithofacies boundaries serve as zoning constraints, and interpolation within different lithofacies zones remains relatively independent. Structural morphology (anticlines, synclines, monoclins, etc.) controls the spatial attitude of the strata, ensuring that the interpolation results are consistent with the actual strata distribution.

[0202] The interpolation prediction results of each stratum (and its internal lithofacies zones) are overlaid according to the original spatial coordinates, ensuring that the layers do not overlap. The layer boundaries serve as the natural boundaries of attribute values, ultimately forming a rasterized attribute volume covering the entire block's three-dimensional space, namely the target block's three-dimensional drillability index model and three-dimensional drillability anisotropy coefficient model. After modeling, the model accuracy needs to be verified by comparing the predicted values ​​at well points with known measured / inverted values. If the error meets the preset accuracy requirements, the model is output; otherwise, iterative optimization is performed by adjusting interpolation parameters, refining constraints, or introducing more seismic attribute co-constraints until the accuracy meets the requirements.

[0203] Figure 10In this step, "Inter-well spatial prediction" and "Spatial interpolation or machine learning prediction (Kriging / Co-Kriging / Geostatistics / Machine Learning)" correspond to the core prediction operations; "Seismic attribute co-constraint" corresponds to seismic attributes as trend constraints; "Introduction of fault and structural constraints" corresponds to fault and structural morphology constraints; "Establishing a three-dimensional drillability index model," "Establishing a three-dimensional drillability anisotropy coefficient model," and "Outputting a three-dimensional drillability anisotropy spatial model of the target block" correspond to the final output of this step; "Whether the modeling results meet the requirements" corresponds to the accuracy verification step of this step; "Historical well drilling data" can be used as additional hard data points for interpolation; "Used for drill bit selection, drilling parameter optimization, and prediction of difficult-to-drill layers" corresponds to the engineering application direction of the three-dimensional model output.

[0204] This specification also provides a device for quantitatively evaluating the anisotropy of formation drillability, which can be used to implement the aforementioned method for quantitatively evaluating the anisotropy of formation drillability. For example... Figure 11 As shown, the device includes a first acquisition unit 10, an establishment unit 20, a calibration unit 30, a generation unit 40, a second acquisition unit 50, a calculation unit 60, a modeling unit 70, and an application unit 80.

[0205] The first acquisition unit 10 is used to identify the main structural plane of the formation in the target formation that plays a dominant role in the difference in drillability direction. Using the main structural plane of the formation as a unified angle reference, rock mechanics test, acoustic wave test and drillability test are carried out on the core samples obtained by multi-angle sampling of cores of different lithologies to obtain the rock mechanics parameters, acoustic wave parameters and drillability parameters corresponding to the core samples of different lithologies and different included angle directions.

[0206] Unit 20 is used to establish a digital core model based on the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples with different lithologies and different included angles.

[0207] The calibration unit 30 is used to jointly calibrate the mesoscopic constitutive parameters of the digital core model based on the errors between the mechanical response of the digital core model and the rock mechanics test results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results.

[0208] The generation unit 40 is used to generate digital core models with different parameter combinations by changing at least one of the lithological parameters, the angle of the main structural plane of the formation, and the multi-scale constitutive parameters after joint calibration of the digital core model.

[0209] The second acquisition unit 50 is used to conduct multi-directional rock breaking simulation on the digital core model with different parameter combinations, and to obtain the rock breaking specific energy, equivalent rock breaking load, and advance per revolution under different directional angles based on the simulation data and indoor test data of the digital core model.

[0210] The calculation unit 60 is used to calculate the directional drillability index at different directional angles based on simulation data and indoor test data based on digital core models, using rock breaking energy, equivalent rock breaking load, and footage per revolution.

[0211] The modeling unit 70 is used to use the directional drillability index as the output label of the prediction model, and to establish a prediction model between formation physical parameters and directional angle and directional drillability index based on the simulation data of the digital core model and the indoor test data.

[0212] Application unit 80 is used to input the formation physical property parameters corresponding to the target depth point of the target well into the prediction model after combining them with multiple preset directional angles to obtain a directional drillability index sequence under different directional angles. Based on the directional drillability index sequence, it calculates at least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction. And / or, based on the wellbore trajectory parameters and formation principal structural plane occurrence parameters of the target well, it calculates the target angle between the actual drilling direction of the target well and the formation principal structural plane, inputs the target angle and the formation physical property parameters corresponding to the target depth point into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock-breaking difficulty under the actual drilling direction.

[0213] In some embodiments, the first acquisition unit further acquires microstructure parameters corresponding to core samples of different lithologies and different angular directions by performing microstructure tests on core samples obtained from multi-angle sampling of core samples of different lithologies. Accordingly, the establishment unit also bases the establishment of the digital core model on the microstructure parameters.

[0214] In some embodiments, the calibration unit includes an acquisition subunit, a first calculation subunit, a construction subunit, and an iteration subunit.

[0215] The acquisition sub-unit is used to perform mechanical response simulation, acoustic response simulation and rock breaking response simulation on the initially constructed digital core model, and to obtain the simulation mechanical response results, simulation acoustic response results and simulation rock breaking response results respectively.

[0216] The first calculation subunit is used to calculate the mechanical response error between the simulated mechanical response result and the rock mechanics test result, the acoustic response error between the simulated acoustic response result and the acoustic test result, and the rock breaking response error between the simulated rock breaking response result and the drillability test result, respectively.

[0217] The sub-unit is constructed to build a comprehensive error function with the mechanical response error, acoustic response error and rock breaking response error as variables, and the comprehensive error function is used as a unified convergence criterion for the joint calibration of multiple responses of the digital core model.

[0218] The iterative subunit is used to iteratively adjust the mesoscopic constitutive parameters of the digital core model when the calculated value of the comprehensive error function does not reach the preset convergence threshold, and repeatedly perform simulation calculation, error solving and comprehensive error judgment operations based on the adjusted parameters until the calculated value of the comprehensive error function meets the preset convergence threshold, thus obtaining a digital core model with completed mechanical-acoustic-rock breaking multi-response joint calibration.

[0219] In some embodiments, the calculation unit calculates the drillability index in the direction of the target included angle θ according to the following formula: , in, The drillability index is defined in the direction of the target included angle θ. As the benchmark drillability index, A drillability index greater than the baseline drillability index indicates that the drill is easier to drill than the average condition. The benchmark drillability index represents a near-average state. A drillability index lower than the baseline drillability index indicates that the drill is more difficult to drill than the average condition. and These are the weighting coefficients. , ,and ; The average rock-breaking energy per unit volume, For the average equivalent rock-breaking load, This represents the average advance per revolution.

[0220] In some embodiments, the application unit includes: The second calculation subunit is used to calculate the directional drillability index for multiple preset directional angles at the same depth point or the same core sample, forming a sequence of directional drillability indices: , The maximum directional drillability index, minimum directional drillability index, and average directional drillability index are calculated using the following formulas: , , , in, The maximum directional drillability index. The minimum directional drillability index. The average directional drillability index. It is a set of angles between multiple preset directions; The drillability anisotropy coefficient A is calculated using the following formula. D : ; Dominant rock-breaking direction θ easy for: , Difficult drilling direction θ hard for: .

[0221] In some embodiments, the application unit includes a first conversion subunit, a second conversion subunit, and a computation subunit.

[0222] The first transformation subunit is used to convert the spherical coordinate representation or discrete coordinate representation of the wellbore trajectory into a unit drilling direction vector in a three-dimensional Cartesian coordinate system.

[0223] The second transformation sub-unit is used to convert the geological occurrence parameters of the main structural plane of the formation into a unit normal vector in the same coordinate system as the drilling direction vector.

[0224] The computational subunit is used to perform a dot product operation between the unit drilling direction vector and the unit normal vector to obtain the angle between the drilling direction and the main structural plane of the formation; the angle is used as the input parameter of the prediction model.

[0225] In some embodiments, the apparatus further includes an assignment unit, a collection and sorting unit, and a forming unit.

[0226] The assignment unit is used to assign at least one of the following values ​​under multiple preset directional angles calculated at each depth point of the target well and adjacent wells to the three-dimensional space according to the corresponding well trajectory spatial coordinates, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, minimum directional drillability index, dominant rock breaking direction, difficult drilling direction, and directional drillability index under the actual drilling direction. The data collection and organization unit is used to collect and organize the assigned spatial scatter data according to the stratigraphic position based on the seismic interpretation and geological stratification results. Data within the same stratigraphic position are grouped into the same data subset. The formation unit is used to perform inter-well spatial prediction within each stratum, using seismic attributes, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The prediction results of each stratum are superimposed according to the original spatial coordinates to form a three-dimensional directional drillability index model and a three-dimensional drillability anisotropy coefficient model for the target block.

[0227] The description and function of the above-mentioned device can be understood by referring to the section on quantitative evaluation methods for formation drillability anisotropy, and will not be repeated here.

[0228] This invention also provides an electronic device, such as... Figure 12 As shown, the electronic device may include a processor 1201 and a memory 1202, wherein the processor 1201 and the memory 1202 may be connected via a bus or other means. Figure 12 Taking the example of a connection between China and Israel via a bus.

[0229] Processor 1201 may be a central processing unit (CPU). Processor 1201 may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0230] Memory 1202, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the formation drillability anisotropy quantitative evaluation method in this embodiment of the invention (e.g., Figure 11 The first acquisition unit 10, the establishment unit 20, the calibration unit 30, the generation unit 40, the second acquisition unit 50, the calculation unit 60, the modeling unit 70, and the application unit 80 are shown. The processor 1201 executes various functional applications and data processing by running non-transitory software programs, instructions, and modules stored in the memory 1202, thereby realizing the formation drillability anisotropy quantitative evaluation method in the above method embodiment.

[0231] The memory 1202 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 1201, etc. Furthermore, the memory 1202 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1202 may optionally include memory remotely located relative to the processor 1201, and these remote memories may be connected to the processor 1201 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0232] The one or more modules are stored in the memory 1202, and when executed by the processor 1201, they perform the following: Figure 1 The method for quantitative evaluation of formation drillability anisotropy in the illustrated embodiment.

[0233] The specific details of the above-mentioned electronic device can be understood by referring to the relevant descriptions and effects in the method embodiments, and will not be repeated here.

[0234] This specification also provides a computer storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the above-described method for quantitative evaluation of formation drillability anisotropy.

[0235] This specification also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the above-described method for quantitative evaluation of formation drillability anisotropy.

[0236] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0237] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. The focus of each embodiment is to describe the differences from other embodiments.

[0238] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0239] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0240] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute certain parts of the methods of various embodiments of this application.

[0241] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0242] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0243] Although this application has been described through embodiments, those skilled in the art will know that this application has many modifications and variations without departing from the spirit of this application, and it is intended that the appended claims cover such modifications and variations without departing from the spirit of this application.

Claims

1. A method for quantitatively evaluating the anisotropy of formation drillability, characterized in that, include: Identify the main structural plane of the formation that plays a dominant role in the difference in drillability direction in the target formation. Using the main structural plane of the formation as a unified angle reference, rock mechanics test, sonic test and drillability test are carried out on the core samples obtained by multi-angle sampling of cores of different lithologies. The rock mechanics parameters, sonic parameters and drillability parameters corresponding to the core samples of different lithologies and different included angle directions are obtained. A digital core model is established based on the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples with different lithologies and different included angles. Based on the errors between the mechanical response of the digital core model and the rock mechanics experimental results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results, the mesoscopic constitutive parameters of the digital core model are jointly calibrated. Using the jointly calibrated digital core model, at least one of the lithological parameters, the angle of the main stratigraphic structural plane, and the multi-scale constitutive parameters is changed to generate digital core models with different parameter combinations. Multi-directional rock breaking simulations were conducted on digital core models with different parameter combinations. Based on simulation data and indoor test data from the digital core models, the rock breaking specific energy, equivalent rock breaking load, and advance per revolution were obtained at different directional angles. Based on simulation data and laboratory test data from digital core models, the directional drillability index is calculated at different directional angles, using rock breaking energy, equivalent rock breaking load, and footage per revolution. Using the directional drillability index as the output label of the prediction model, a prediction model is established based on the simulation data of the digital core model and the indoor test data to predict the relationship between formation physical parameters and directional angle and directional drillability index. For the target depth point of the target well, the formation physical parameters corresponding to the target depth point are combined with multiple preset directional angles and input into the prediction model to obtain a directional drillability index sequence under different directional angles. Based on the directional drillability index sequence, at least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction is calculated. And / or, based on the wellbore trajectory parameters and formation principal structural plane occurrence parameters of the target well, the target angle between the actual drilling direction of the target well and the formation principal structural plane is calculated. The target angle and the formation physical parameters corresponding to the target depth point are input into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock-breaking difficulty under the actual drilling direction.

2. The method according to claim 1, characterized in that, The method further includes: By sampling core samples from different lithologies at multiple angles, microstructure tests were conducted to obtain the microstructure parameters corresponding to core samples of different lithologies and different included angle directions. Accordingly, the microstructure parameters are also used when establishing the digital core model.

3. The method according to claim 2, characterized in that, Based on the errors between the mechanical response of the digital core model and the rock mechanics experimental results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results, the mesoscopic constitutive parameters of the digital core model are jointly calibrated, including: Mechanical response simulation, acoustic response simulation and rock breaking response simulation were carried out on the initially constructed digital core model, and the simulation results of mechanical response, acoustic response and rock breaking response were obtained respectively. Calculate the mechanical response error between the simulated mechanical response result and the rock mechanics test result, the acoustic response error between the simulated acoustic response result and the acoustic test result, and the rock breaking response error between the simulated rock breaking response result and the drillability test result, respectively. A comprehensive error function is constructed using the mechanical response error, acoustic response error, and rock breaking response error as variables, and the comprehensive error function is used as a unified convergence criterion for the joint calibration of multiple responses of the digital core model. When the calculated value of the comprehensive error function does not reach the preset convergence threshold, the mesoscopic constitutive parameters of the digital core model are iteratively adjusted, and the simulation calculation, error solving and comprehensive error judgment operations are repeatedly performed based on the adjusted parameters until the calculated value of the comprehensive error function meets the preset convergence threshold, and a digital core model with completed mechanical-acoustic-rock breaking multi-response joint calibration is obtained.

4. The method according to claim 1, characterized in that, The method further includes: Calculate the rock-breaking energy using the following formula: ， in, The rock breaking energy is the rock breaking specific energy along the target angle θ direction. The cutting force is in the direction of the target angle θ. The cross-sectional area of ​​the rock breaking. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. The rock-breaking velocity is defined by the target angle θ. The angular velocity of the drill bit in the direction of the target angle θ; Calculate the equivalent rock-breaking load using the following formula: ， in, The equivalent rock-breaking load in the direction of the target angle θ is... The cutting force is in the direction of the target angle θ. The rock-breaking torque is the rock-breaking torque in the direction of the target angle θ. The cutting radius; Calculate the advance per revolution using the following formula: ， in, For each revolution, the advance is given by the target angle θ. The rock-breaking velocity is defined by the target angle θ. Let θ be the angular velocity of the drill bit in the direction of the target angle.

5. The method according to claim 1, characterized in that, Based on rock-breaking energy, equivalent rock-breaking load, and footage per revolution, the directional drillability index is calculated for different directional angles, including: The drillability index in the direction of the target included angle θ is calculated using the following formula: ， in, The drillability index is defined in the direction of the target included angle θ. As the benchmark drillability index, A drillability index greater than the baseline drillability index indicates that the drill is easier to drill than the average condition. The benchmark drillability index represents a near-average state. A drillability index lower than the baseline drillability index indicates that the drill is more difficult to drill than the average condition. and These are the weighting coefficients. , ,and ; The average rock-breaking energy per unit volume, For the average equivalent rock-breaking load, This represents the average advance per revolution.

6. The method according to claim 1, characterized in that, Calculate at least one of the following based on the directional drillability index sequence: the drillability anisotropy coefficient, the dominant rock-breaking direction, and the difficult-to-drill direction: For multiple preset directional angles at the same depth point or the same core sample, calculate the directional drillability index for each angle, forming a directional drillability index sequence: ， The maximum directional drillability index, minimum directional drillability index, and average directional drillability index are calculated using the following formulas: ， ， ， in, The maximum directional drillability index. The minimum directional drillability index. The average directional drillability index. It is a set of angles between multiple preset directions; The drillability anisotropy coefficient A is calculated using the following formula. D : ； Dominant rock-breaking direction θ easy for: ， Difficult drilling direction θ hard for: 。 7. The method according to claim 1, characterized in that, Based on the wellbore trajectory parameters and formation principal structural plane attitude parameters of the target well, calculate the target angle between the actual drilling direction of the target well and the formation principal structural plane, including: The spherical or discrete coordinate representation of the well trajectory is uniformly converted into a unit drilling direction vector in a three-dimensional Cartesian coordinate system. The geological occurrence parameters of the main structural plane of the formation are converted into unit normal vectors in the same coordinate system as the drilling direction vector. The unit drilling direction vector and the unit normal vector are multiplied by a dot product to obtain the angle between the drilling direction and the main structural plane of the formation; the angle is used as the input parameter of the prediction model.

8. The method according to claim 1, characterized in that, The method also includes establishing a drillability anisotropy space model of the target block in the following manner; At least one of the following values, calculated from the depth points of the target well and adjacent wells under multiple preset directional angles, is assigned to the three-dimensional space according to the corresponding well trajectory spatial coordinates, forming a multi-attribute spatial scatter dataset with the well trajectory as the carrier: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, minimum directional drillability index, dominant rock-breaking direction, difficult-to-drill direction, and directional drillability index under the actual drilling direction. Based on the seismic interpretation horizon and geological stratification results, the assigned spatial scatter data are stratified and organized according to their respective horizons, with data within the same horizon grouped into the same data subset; Using seismic properties, lithofacies boundaries, fault locations, and structural morphology as spatial trends and boundary constraints, inter-well spatial prediction is carried out within each stratum for at least one of the following: directional drillability index, drillability anisotropy coefficient, maximum directional drillability index, and minimum directional drillability index. The prediction results of each stratum are superimposed according to the original spatial coordinates to form a three-dimensional drillability index model and a three-dimensional drillability anisotropy coefficient model for the target block.

9. A quantitative evaluation device for the anisotropy of formation drillability, characterized in that, include: The first acquisition unit is used to identify the main structural plane of the formation that plays a dominant role in the difference in drillability direction in the target formation. Using the main structural plane of the formation as a unified angle reference, rock mechanics test, acoustic wave test and drillability test are carried out on the core samples obtained by multi-angle sampling of core samples of different lithologies to obtain the rock mechanics parameters, acoustic wave parameters and drillability parameters corresponding to the core samples of different lithologies and different included angle directions. Establish a unit to build a digital core model based on the rock mechanics parameters, acoustic parameters, and drillability parameters corresponding to core samples with different lithologies and different included angles; The calibration unit is used to jointly calibrate the mesoscopic constitutive parameters of the digital core model based on the errors between the mechanical response of the digital core model and the rock mechanics experimental results, the errors between the acoustic response and the acoustic test results, and the errors between the rock breaking response and the drillability test results. The generation unit is used to generate digital core models with different parameter combinations by changing at least one of the lithological parameters, the angle of the main structural plane of the formation, and the multi-scale constitutive parameters of the jointly calibrated digital core model. The second acquisition unit is used to conduct multi-directional rock breaking simulation on the digital core model with different parameter combinations, and to obtain the rock breaking specific energy, equivalent rock breaking load, and advance per revolution under different directional angles based on the simulation data and indoor test data of the digital core model. The calculation unit is used to calculate the directional drillability index at different directional angles based on simulation data and indoor test data based on digital core models, using rock breaking energy, equivalent rock breaking load, and footage per revolution. The modeling unit is used to use the directional drillability index as the output label of the prediction model, and to establish a prediction model between formation physical parameters and directional angle and directional drillability index based on simulation data of digital core model and indoor test data. The application unit is used to input the formation physical property parameters corresponding to the target depth point of the target well into the prediction model after combining them with multiple preset directional angles to obtain a directional drillability index sequence under different directional angles. Based on the directional drillability index sequence, it calculates at least one of the drillability anisotropy coefficient, dominant rock-breaking direction, and difficult-to-drill direction. And / or, based on the wellbore trajectory parameters and formation principal structural plane occurrence parameters of the target well, it calculates the target angle between the actual drilling direction of the target well and the formation principal structural plane, inputs the target angle and the formation physical property parameters corresponding to the target depth point into the prediction model to obtain the directional drillability index under the actual drilling direction of the target well, so as to evaluate the rock-breaking difficulty under the actual drilling direction.

10. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to implement the method for quantitative evaluation of formation drillability anisotropy as described in any one of claims 1 to 8.

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