Ground stress prediction method and device for complex fault zone, equipment and medium

Abstract

The invention relates to a crustal stress prediction method, device, equipment and medium for a complex fault zone, and belongs to the technical field of oil-gas field development, and the method comprises the steps: calculating rock physical parameters of the complex fault zone according to a rock physical parameter calculation formula; calculating rock mechanical parameters of the complex fault zone according to a rock mechanical parameter calculation formula; reconstructing the logging curve of the complex fault zone to obtain a reconstructed logging curve; and predicting the crustal stress of the complex fault zone through a three-dimensional crustal stress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters. According to the scheme, the logging curve of the complex fault zone is reconstructed, the reconstructed logging curve is obtained, and data missing or distortion is made up. According to the method, the application bottleneck of a traditional method under the complex geological condition of the ultra-deep oil and gas reservoir is broken through, the prediction precision is remarkably improved, and technical support is provided for safe and efficient development of the ultra-deep oil and gas reservoir.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a method, device, equipment and medium for predicting geostress in a complex fault zone. Background Art

[0002] With the gradual deepening of oil and gas exploration and development, ultra-deep carbonate oil and gas reservoirs have attracted much attention due to their huge potential reserves. However, these oil and gas reservoirs are often accompanied by complex fault zone structures and multi-stage strike-slip faults, which makes it difficult to accurately characterize the geological and mechanical characteristics of the reservoirs. Traditional rock mechanics parameter modeling mainly relies on well logging data and seismic data, but due to the lack of well logging curves, complex fault zone structures, and limited measured data on formation pressure and stress, the existing geostress prediction methods have low accuracy when applied to ultra-deep oil and gas reservoirs and cannot meet the needs of wellbore stability prediction and reservoir transformation design.

[0003] Existing geostress prediction methods mainly rely on the separate application of well logging data or seismic inversion data. These methods usually have the problem of insufficient accuracy when dealing with complex geological conditions (such as fault zones, ultra-deep wells, etc.). Well logging data will be distorted in the case of wellbore expansion, data loss, etc., resulting in a decrease in the accuracy of geostress prediction. In addition, although seismic inversion technology can provide three-dimensional geostress field distribution information, it is limited by resolution and data integration capabilities, making it difficult to accurately describe the local stress field. Summary of the invention

[0004] In view of this, it is necessary to provide a method, device, equipment and medium for predicting geostress in complex fault zones to solve the problem that the existing methods are insufficiently accurate when dealing with complex geological conditions (such as fault zones, ultra-deep wells, etc.), and the logging data will be distorted when the wellbore is expanded, the data is missing, etc., resulting in a decrease in the accuracy of geostress prediction.

[0005] In order to solve the problem that the existing methods have insufficient accuracy when dealing with complex geological conditions (such as fault zones, ultra-deep wells, etc.), the logging data will be distorted in the case of wellbore expansion, data loss, etc., resulting in a decrease in the accuracy of geostress prediction, the present invention provides a geostress prediction method for complex fault zones, including: Calculate the rock physical parameters of complex fault zones according to the rock physical parameter calculation formula; Calculate the rock mechanics parameters of complex fault zones according to the rock mechanics parameter calculation formula; Reconstruct the logging curve of the complex fault zone to obtain the reconstructed logging curve; The geostress of a complex fault zone is predicted by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

[0006] In a possible implementation, the method further includes: establishing the rock physical parameter calculation formula and the rock mechanical parameter calculation formula according to lithology data of different formations.

[0007] In a possible implementation manner, the method further includes: fitting the rock physical parameter calculation formula and the rock mechanical parameter calculation formula to obtain the well logging curve.

[0008] In a possible implementation, the logging curve includes: a density curve and a shear wave time difference curve; The step of fitting the rock physical parameter calculation formula and the rock mechanical parameter calculation formula to obtain the well logging curve specifically includes: By fitting the rock physical parameter calculation formula, a density curve is obtained; By fitting the rock mechanics parameter calculation formula, a shear wave time difference curve is obtained.

[0009] In a possible implementation, the reconstructing the logging curve of the complex fault zone specifically includes: The density curve is reconstructed by using the formation mineral composition and formation porosity of the complex fault zone to obtain a reconstructed density curve.

[0010] In a possible implementation, the method further includes: calculating elastic parameters through seismic inversion data to obtain rock elastic parameters; Assigning the rock elastic parameters to the three-dimensional geostress model to obtain the assigned three-dimensional geostress model; The three-dimensional geostress model is used to predict the geostress of a complex fault zone in combination with the reconstructed well logging curve, the rock mechanical parameters and the rock physical parameters, specifically including: The geostress of a complex fault zone is predicted by using the assigned three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanics parameters and the rock physics parameters.

[0011] In a possible implementation, the method further includes: verifying the geostress through three-dimensional geostress simulation, and when the verification result meets the requirements, performing stability analysis on the wellbore wall of the complex fault zone according to the geostress.

[0012] The present invention also provides a device for predicting geostress in a complex fault zone, comprising: a first calculation module, a second calculation module, a reconstruction module and a prediction module; The first calculation module is used to calculate the rock physical parameters of the complex fault zone according to the rock physical parameter calculation formula; The second calculation module is used to calculate the rock mechanics parameters of the complex fault zone according to the rock mechanics parameter calculation formula; The reconstruction module is used to reconstruct the well logging curve of the complex fault zone to obtain the reconstructed well logging curve; The prediction module is used to predict the geostress of a complex fault zone by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

[0013] Another technical solution of the present invention to solve the above technical problems is as follows: The present invention also provides an electronic device, comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in a method for predicting geostress in a complex fault zone in any of the above-mentioned solutions.

[0014] Another technical solution of the present invention to solve the above technical problems is as follows: The present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of a method for predicting geostress in a complex fault zone of any of the above-mentioned schemes.

[0015] The beneficial effect of the present invention is that the well logging curve of the complex fault zone is reconstructed by the scheme of the present invention to obtain the reconstructed well logging curve, which makes up for the missing or distorted data. The present invention breaks through the application bottleneck of the traditional method under the complex geological conditions of ultra-deep oil and gas reservoirs, significantly improves the prediction accuracy, and provides technical support for the safe and efficient development of ultra-deep oil and gas reservoirs. The scheme has a wide market application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of an embodiment of a method for predicting geostress in a complex fault zone provided by the present invention; Figure 2 A schematic flow chart of another embodiment of a method for predicting geostress in a complex fault zone provided by the present invention; Figure 3 for Figure 2 A schematic flow chart of an embodiment of step S202; Figure 4 A structural framework diagram of an embodiment of a device for predicting ground stress in a complex fault zone provided by the present invention; Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention; Figure 6 A vertical geostress distribution diagram provided by the present invention; Figure 7A horizontal geostress distribution diagram provided by the present invention; Figure 8 A schematic diagram of the decreasing propagation of shear waves provided by the present invention; Fig. 9 A schematic diagram of the incremental propagation of shear waves provided by the present invention; Fig.10 A schematic diagram of the failure conditions under different normal stresses and shear stresses provided by the present invention; Fig.11 A displacement field diagram provided by the present invention; Fig.12 A schematic diagram of the expansion direction of the hydraulic fracturing cracks in Shunbei area provided by the present invention; Fig.13 A schematic diagram of three-dimensional fracturing crack expansion provided by the present invention; Fig.14 The stress distribution around the wellbore in Shunbei area provided by the present invention; Fig.15 The in-situ stress profile diagram of Shunbei area provided by the present invention. DETAILED DESCRIPTION

[0017] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

[0018] like Figure 1 As shown, a specific embodiment of the present invention discloses a method for predicting geostress in a complex fault zone, comprising: S101. Calculate rock physical parameters of a complex fault zone according to a rock physical parameter calculation formula. It should be noted that, in a certain embodiment, the rock physical parameters include: total density of rock, longitudinal wave velocity, and transverse wave velocity.

[0019] The total density of the rock is calculated based on the mineral components. , the formula is as follows: , in Indicates Density of the mineral; Indicates Volume fraction of the mineral; represents the volume fraction of pore fluid; Indicates porosity.

[0020] The longitudinal wave velocity and the transverse wave velocity are calculated by first calculating the bulk modulus and the shear modulus of the rock, and then calculating the longitudinal wave velocity and the transverse wave velocity using the elastic modulus of the rock.

[0021] According to the effective medium theory of mixed media, such as the Chladni-Hamilton (Kuster-Toksöz) model, the elastic modulus of rock can be expressed as a combination of minerals and pore fluids. First, the bulk modulus of rock is calculated. and shear modulus : , , in: represents the bulk modulus and shear modulus of dry rock; , It represents the bulk modulus and shear modulus of the mineral, which can be obtained by weighted average of the mineral components; , represents the bulk modulus and shear modulus of the pore fluid (for fluid, ).

[0022] Then, the elastic modulus of rock is used to calculate and : , , S102. Calculate the rock mechanical parameters of the complex fault zone according to the rock mechanical parameter calculation formula; it should be noted that, in a certain embodiment, the rock mechanical parameters are specifically calculated Young's modulus and Poisson's ratio, shear modulus and bulk modulus, Lame constant and rock strength parameters.

[0023] (a) Young's modulus and Poisson's ratio Calculation Young's modulus and Poisson's ratio can be calculated from the bulk modulus and shear modulus: , , Derivation process, according to the elastic mechanics relationship: , , (b) Shear modulus and bulk modulus Calculation of and , calculated directly from the above (c) Lamé constant calculate: , (d) Rock strength parameters, specifically, the rock compressive strength is calculated by the rock tensile strength, and the rock compressive strength is calculated according to the Hooke-Brown criterion. Determine the Hooke-Brown parameters based on lithology , combined with the minimum principal stress and the tensile strength of rock , calculate the compressive strength of rock , the specific formula is as follows: , S103. Reconstruct the logging curves of the complex fault zone to obtain the reconstructed logging curves; the logging curves include: density curves and shear wave time difference curves, and the density curves are reconstructed using mineral composition and porosity; due to the high cost of shear wave logging or data missing, other logging curves can be used for prediction.

[0024] S104, predicting the geostress of a complex fault zone by using a three-dimensional geostress model and combining the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters. The geostress of a complex fault zone includes: horizontal stress, vertical stress and tectonic stress of the well wall.

[0025] It should be noted that, in a certain embodiment, establishing a geostress model, calculating vertical geostress and horizontal geostress, and estimating tectonic stress specifically include: Vertical in-situ stress , the vertical geostress is usually equal to the weight of the overlying rock layer of the formation, which can be calculated by density integration: , in: Indicates depth; Indicates depth The density at ; Represents the acceleration due to gravity.

[0026] The vertical ground stress diagram is shown in Figure 6 As shown in the figure, from top to bottom, the stress changes gradually from high to low, showing a typical vertical stress gradient. This distribution may be due to the stress increase caused by the deadweight of the material.

[0027] The stress distribution in the figure is relatively uniform, with no obvious stress concentration or mutation areas, indicating that the vertical stress field in this area is relatively stable.

[0028] Horizontal ground stress and , the calculation of horizontal ground stress can adopt the plane strain model of elastic mechanics: ,

[0029] in ： is the maximum horizontal principal stress; is the minimum horizontal principal stress; is Poisson's ratio; is the Biot poroelastic coefficient, i.e., rock elastic parameter; is the pore pressure; ， is the tectonic stress increment.

[0030] Schematic diagram of horizontal geostress distribution, such as Figure 7 As shown in the figure, the stress increases gradually from left to right in the horizontal direction, showing a horizontal stress gradient. The stress distribution in the vertical direction is relatively uniform, with no obvious vertical changes, indicating that the stress is mainly affected by the horizontal position. This horizontal stress distribution diagram reveals the stress changes at different horizontal positions. The stress increases gradually from left to right, which may reflect the lateral stress concentration trend of the material or formation in this area. This diagram can be used to analyze the impact of horizontal stress on the wellbore or formation, especially in the stability assessment of high stress areas.

[0031] The estimation of tectonic stress may include: using seismic data and fault slip trends, combined with finite element simulation, to estimate the tectonic stress increment. In one implementation, the estimation of tectonic stress may specifically include: Through multi-angle seismic data, the longitudinal wave impedance, shear wave impedance and density are obtained by inversion.

[0032] 1. Data collection and preprocessing: Seismic data and fault slip trend data are the basis for estimating tectonic stress increment. The specific steps include: Seismic data: Extract information such as fault location, orientation, dip, etc. from seismic data. These data help determine the geometry and distribution of faults.

[0033] Fault slip trend: The slip trend of a fault can usually be obtained through seismological analysis, including information such as slip direction and slip rate. These parameters help determine the direction and magnitude of stress release of the fault under the tectonic stress field.

[0034] 2. Establish a 3D geological model: Based on seismic data and fault geometry, a 3D geological model is constructed using geological modeling software (such as Petrel, GOCAD, etc.), including: Stratigraphic boundaries: Different strata are divided based on seismic reflection layers.

[0035] Fault structure: The 3D geometric structure of the fault is embedded in the geological model to ensure that the fault orientation, dip and displacement are consistent with the actual seismic data.

[0036] 3. Stress boundary conditions and initial tectonic stress field: On the basis of the 3D geological model, the boundary conditions and initial tectonic stress field of the finite element simulation are set: Boundary conditions: Boundary conditions set the structural environment of the corresponding area (such as compression, tension, shear, etc.), usually including displacement boundary conditions or stress boundary conditions. Fig.11 As shown in Figure 1, the displacement is maximum in the upper part of the region (top center region) and gradually decreases toward the sides and bottom. This distribution forms a parabola-like shape.

[0037] The displacement field distribution shown in the figure usually corresponds to a deformation caused by a concentrated load or bending, indicating that there is a large stress or deformation effect above the area, causing the displacement in the area to be distributed in an arc shape. The displacement field diagram can be used to analyze the deformation of materials or strata under loading or external stress. The top center area has the largest displacement, which may be affected by concentrated stress or boundary conditions. Such displacement distribution diagrams are often used in structural analysis or geotechnical engineering to help identify high displacement areas to assess the potential impact of deformation on the structure. In geological and engineering applications, this diagram helps to identify possible unstable areas or predict force distribution patterns.

[0038] Initial tectonic stress field: The initial stress field is set according to the geological background information (such as regional tectonic background, historical tectonic events). These data can be determined through previous stress measurement results and literature data.

[0039] 4. Finite element meshing: Finite element analysis software is used to mesh the 3D geological model. The meshing needs to be fine, especially in the area near the fault, to improve the accuracy of the simulation.

[0040] Mesh refinement near faults: To accurately simulate the stress distribution along the fault, a denser mesh is usually required in the fault region.

[0041] Appropriate element type: Select an appropriate element type (such as tetrahedral elements or hexahedral elements) to ensure simulation accuracy.

[0042] 5. Simulation of fault slip and tectonic stress increment: The fault slip trend is applied to the finite element model to simulate the stress release process of the fault under the initial tectonic stress field and calculate the tectonic stress increment. The key steps are as follows: Apply fault slip: Apply slip boundary conditions on the fault plane to simulate the sliding behavior of the fault under the action of regional tectonic stress.

[0043] Stress calculation: The stress field changes during fault slip are obtained through finite element simulation, and then the structural stress increment around the fault is calculated. In a certain embodiment, the three-dimensional geostress field model mainly involves the superposition of vertical stress, horizontal stress and tectonic stress. Combined with the equilibrium equation of elastic mechanics, the model formula can be summarized as follows: The three-dimensional geostress field model mainly involves the superposition of vertical stress, horizontal stress and tectonic stress. Combined with the equilibrium equation of elastic mechanics, the model formula can be summarized as follows: , in, is the stress tensor, which represents the stress component in each direction in the three-dimensional coordinate system ; is the contribution of the vertical stress, which is obtained by integrating the weight of the overlying rock layer of the formation: , in, With depth Variation of density, is the acceleration due to gravity.

[0044] and are the contributions of horizontal stresses, calculated based on the plane strain model of elasticity theory, representing the maximum and minimum horizontal principal stresses respectively: , in, is Poisson's ratio; is the Biot poroelastic coefficient, i.e., rock elastic parameter; is the pore pressure; and are the increments of tectonic stress respectively. The influence of tectonic stress on the whole stress field is mainly estimated through seismic data and finite element simulation. Tectonic stress has different components in different regions and has an important influence on the final geostress field.

[0045] Preferably, in one of the above embodiments, Figure 2 As shown, it also includes: S201. Establish the rock physical parameter calculation formula and the rock mechanical parameter calculation formula according to the lithology data of different strata.

[0046] Preferably, in one of the above embodiments, the method further comprises: S202, fitting the rock physical parameter calculation formula and the rock mechanical parameter calculation formula to obtain the logging curve. It should be noted that, in one embodiment, based on the theoretical model of the above rock physical parameters and rock mechanical parameters, core experimental data and measurement data are used to fit the rock physical and mechanical parameter calculation formulas of different lithologies through multivariate nonlinear regression.

[0047] For example, for Young's modulus: , in: is the regression coefficient; is the error term; is Young's modulus, is the total density of the rock, is the longitudinal wave velocity.

[0048] Preferably, in one of the above embodiments, the logging curves include: a density curve and a shear wave time difference curve; The step of fitting the rock physical parameter calculation formula and the rock mechanical parameter calculation formula to obtain the well logging curve specifically includes: S301, obtaining a density curve by fitting the rock physical parameter calculation formula; S302, obtaining a shear wave time difference curve by fitting the rock mechanics parameter calculation formula.

[0049] It should be noted that the rock mechanics parameter calculation formula is obtained through logging data, and the shear wave time difference curve is obtained by fitting according to the rock mechanics parameter calculation formula.

[0050] It should be noted that in a certain implementation, if Figure 8 As shown in the figure, the S-wave velocity decreases with increasing distance, which may reflect that the resistance of the stratum or material to the propagation of shear waves increases with the depth or position. This trend of decreasing velocity is usually related to changes in parameters such as stratum density and porosity, and may be caused by changes in material properties or geological structures.

[0051] like Fig. 9 As shown in the figure, the S-wave time difference increases with the distance. This indicates that on the same propagation path, the propagation speed of the S-wave gradually decreases with the increase of distance, resulting in an increase in the cumulative time difference.

[0052] This increase in time difference may be related to changes in formation properties, such as increased formation density or changes in porosity, which affect the propagation speed of the S wave.

[0053] Preferably, in one of the above embodiments, the reconstructing the logging curve of the complex fault zone specifically includes: The density curve is reconstructed by using the formation mineral composition and formation porosity of the complex fault zone to obtain a reconstructed density curve.

[0054] It should be noted that, in a certain embodiment, the well logging curve is reconstructed, the density curve based on the mineral components is reconstructed, and the shear wave time difference curve is reconstructed; Density curve reconstruction based on mineral components. If a certain section of the density curve is missing or distorted, it can be reconstructed using mineral components and porosity. Reconstruction formula: , in: is the mineral volume fraction; Well logging data is the volume fraction of pore fluid; is the porosity.

[0055] Mineral volume fraction It can be calculated through logging data (such as natural gamma, spectral gamma, nuclear magnetic resonance, etc.) and mineral models.

[0056] Reconstruction of shear wave time difference curve, due to the high cost or lack of data of shear wave logging, other logging curves can be used for prediction, such as compressional wave time difference curve, density curve, and natural gamma curve.

[0057] The high S-wave time difference area corresponds to the low ground stress area: In some areas of Shunbei Oilfield, if the shear wave time difference curve shows a higher time difference value, it usually indicates that the rock formation in this area is relatively weak and the wave velocity is low. Since stress affects the compactness and crack characteristics of the rock, a higher shear wave time difference usually corresponds to a lower ground stress. Such areas have more stress release and are prone to cracks, so it is necessary to pay attention to the risk of well wall collapse during mining. Fig.12 As shown in the figure, the expansion of hydraulic fractures in Shunbei area in different stress directions. The fractures expand longer in the direction of maximum principal stress, indicating that the stress in this direction is greater, which is more conducive to the extension of fractures. In the development of oil and gas fields, understanding the expansion direction of fractures helps to optimize the fracturing plan and thus improve oil and gas recovery. This figure can help engineers predict the expansion path of fractures so as to more effectively guide the growth of fractures in the formation and improve reservoir connectivity.

[0058] like Fig.13 As shown in the figure, the three-dimensional fracturing crack expansion map shows the expansion characteristics of cracks in Shunbei area in different stress directions. The cracks expand more in the direction of maximum principal stress (lighter color), while they expand less in the direction of minimum principal stress (darker color). This information is of great reference value for optimizing fracturing construction, and can help engineers predict the direction of crack extension, improve fracturing effects and optimize production capacity layout.

[0059] like Fig.14 The figure shows the stress distribution around the wellbore in Shunbei area, with low radial stress and high annular stress. A significant increase in annular stress will affect the stability of the wellbore and may cause instability or wellbore damage. In practical applications, this figure helps engineers evaluate the stress state around the wellbore and guide wellbore support and fracturing operations.

[0060] like Fig.15 As shown, the vertical stress Sv curve: represents the increase of vertical stress with depth. Vertical stress is usually linearly related to depth. The greater the depth, the greater the vertical stress. The maximum horizontal principal stress SH curve: represents the increase of maximum horizontal principal stress with depth. This stress value increases with depth, but is usually smaller than the vertical stress. The minimum horizontal principal stress Sh curve: represents the increase of minimum horizontal principal stress with depth. The minimum horizontal principal stress is usually the smallest of the three stresses, and the rate of stress increase with depth is also lower.

[0061] Gray area (stress area): The gray filled area represents the stress interval between different types of stress. This area is usually used to represent the stress state of the formation within a specific depth range, and is a key area for the assessment of wellbore stability and fracture pressure. Stress change characteristics: The vertical stress Sv curve increases rapidly with increasing depth, showing the most obvious depth dependence. The maximum horizontal stress SH curve and the minimum horizontal stress Sh also increase with depth, but the increase is smaller than the vertical stress. With the increase in depth, different types of stress gradually differentiate, and the gap between the maximum and minimum horizontal principal stresses and the vertical stress widens. This stress difference affects the stability of the formation and the behavior of crack propagation. This figure shows the distribution of geostress with depth in the Shunbei area through a profile, which helps to understand the stress state of the formation. With increasing depth, vertical stress, maximum horizontal principal stress and minimum horizontal principal stress all show different growth trends. Understanding the distribution characteristics of these stresses is crucial for wellbore stability assessment and fracturing design, especially the stress state in the gray area, which can guide the design of protection and support measures in wellbore and reservoir development. The low shear wave time difference area corresponds to the high geostress area: Areas with lower shear wave delays often mean that the rock formation is denser and the wave speed is faster. Dense rock formations usually produce higher geostress because the formation is tightly compacted and has fewer internal fractures. These areas have higher geostress, so the wellbore is more stable, but higher drilling pressure may be required during drilling to prevent blowouts.

[0062] The change in shear wave time gradient reflects the stress concentration area: If the gradient of the shear wave time difference curve changes significantly at certain depths, it may indicate a sudden change in the ground stress field. This situation is particularly common near geological faults or fracture zones. Stress concentration areas appear as sudden changes or sharp increases on the shear wave time difference curve. This change can help identify potential stress concentration locations and guide drilling operations to avoid high-risk areas.

[0063] Calibration of shear wave time difference and ground stress prediction: By comparing the shear wave time difference data with the actual ground stress data of downhole testing, a prediction model of shear wave time difference and ground stress can be established. For example, through the linear or nonlinear regression model of shear wave time difference and ground stress value, ground stress can be predicted by shear wave time difference in other undrilled areas, thereby realizing a wider range of ground stress distribution prediction.

[0064] In Shunbei Oilfield, the shear wave time difference curve can be used as an auxiliary tool for geostress prediction. High shear wave time difference usually indicates low geostress areas, while low shear wave time difference is related to high geostress areas. By analyzing the changing trend of shear wave time difference, the geostress distribution of the oilfield can be more accurately evaluated, providing guidance for wellbore stability and fracture development.

[0065] Multiple regression model: , in: represents the shear wave time difference; represents the longitudinal wave time difference; represents natural gamma; represents neutron porosity; represents density log; represents the regression coefficient.

[0066] Preferably, in one of the above embodiments, it further comprises: calculating elastic parameters through seismic inversion data to obtain rock elastic parameters; It should be noted that, in a certain embodiment, three-dimensional geostress prediction is performed based on seismic data, elastic parameters are obtained through seismic inversion, AVO analysis and elastic parameter calculation are performed; three-dimensional geostress simulation is performed, including numerical simulation, calculation of stress field, and analysis of geostress around the well.

[0067] Step (a) AVO (amplitude versus offset) analysis using the Aki-Richards approximation: , in: represents the reflection coefficient; represents the angle of incidence; , , Represents the coefficient related to rock elastic parameters.

[0068] Through multi-angle seismic data, the longitudinal wave impedance is obtained by inversion and shear wave impedance , and density .

[0069] Step (b) elastic parameter calculation, using the inversion obtained , and , calculate the elastic modulus: , Assigning the rock elastic parameters to the three-dimensional geostress model to obtain the assigned three-dimensional geostress model; The three-dimensional geostress model is used to predict the geostress of a complex fault zone in combination with the reconstructed well logging curve, the rock mechanical parameters and the rock physical parameters, specifically including: The geostress of a complex fault zone is predicted by using the assigned three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanics parameters and the rock physics parameters.

[0070] It should be noted that, in a certain embodiment, seismic inversion to obtain elastic parameters includes: (a) AVO (amplitude variation with offset) analysis: Using the Aki-Richards approximation formula: , in: represents the reflection coefficient; represents the angle of incidence; , , Represents the coefficient related to rock elastic parameters.

[0071] Through multi-angle seismic data, the longitudinal wave impedance is obtained by inversion and shear wave impedance , and density .

[0072] (b) Elastic parameter calculation, using the inversion results , and , calculate the elastic modulus: , Preferably, in one of the above embodiments, it further includes: verifying the geostress through three-dimensional geostress simulation, and when the verification result meets the requirements, performing stability analysis on the wellbore wall of the complex fault zone according to the geostress.

[0073] It should be noted that, in a certain embodiment, the three-dimensional geostress field simulation: (a) Numerical simulation: Establish a three-dimensional geological model, including stratigraphic structure, faults, lithology distribution, etc.; Assign the elastic parameters obtained from seismic inversion to the model; The finite element method is used to impose boundary conditions and tectonic stresses to simulate the geostress field.

[0074] (b) Stress field calculation: Solve the elasticity equation: , in: represents the stress tensor; Represents body density (such as gravity).

[0075] Combined with the geological structure characteristics, the stress components of each point are calculated.

[0076] (c) Analysis of ground stress around the well: Extract the geostress distribution around the wellbore; Considering the disturbance effect of the wellbore, the stress concentration at the wall is calculated. The ultimate goal of the geostress field prediction is to optimize the wellbore stability and reservoir reconstruction design: In one embodiment, the method further includes: performing wellbore stability analysis and fracturing parameter optimization, wellbore peripheral stress calculation, and instability criteria.

[0077] Wellbore stability analysis: Based on the three-dimensional geostress field, the possibility of wellbore instability is judged by the Moore-Coulomb criterion or the Drucker-Prager criterion. The main causes of wellbore instability include high stress concentration areas and low stress release areas, and a comprehensive geostress field is required for risk assessment. Fig.10 As shown in the figure, the Mohr-Coulomb criterion is used to describe the failure conditions of materials under different normal stresses and shear stresses. The solid envelope is the Mohr-Coulomb failure line, which represents the maximum shear stress that the material can withstand under different normal stresses. The dotted line represents the tensile strength limit of the material. In engineering analysis, this diagram is used to determine the instability conditions of the material and help determine the risk of failure of structures such as wellbore walls under complex stress states.

[0078] (a) Calculation of wellbore stress: Considering the existence of the wellbore, the Cauchy-Davenport (Kirsch) solution is used to calculate the stress around the wellbore: , , , , in: , , Indicates radial, annular, and axial stresses; Indicates the distance from any position to the center of the well; Represents the wellbore radius; Indicates azimuth; It indicates the difference between the well pressure (mud pressure) and the formation pore pressure; represents Poisson's ratio.

[0079] (b) Instability criteria: Use the Moore-Coulomb criterion or the Drucker-Prager criterion to determine the possibility of wellbore instability: Mohr-Coulomb criterion: , in: represents the maximum shear stress; represents the effective normal stress; It indicates cohesion; represents the internal friction angle. Fig.10 As shown in the figure, the Mohr-Coulomb criterion is used to describe the failure conditions of materials under different normal stresses and shear stresses. The solid envelope is the Mohr-Coulomb failure line, which represents the maximum shear stress that the material can withstand under different normal stresses. The dotted line represents the tensile strength limit of the material. In engineering analysis, this diagram is used to determine the instability conditions of the material and help determine the risk of failure of structures such as wellbore walls under complex stress states.

[0080] Derek Prager Criteria: , in: represents the second deviatoric stress invariant; represents the mean normal stress; , Represents material parameters.

[0081] In one embodiment, the optimization of fracturing parameters includes: predicting the expansion direction and shape of the cracks based on the ground stress field, and calculating the fracture pressure. :

[0082] in: Indicates the tensile strength of rock.

[0083] Optimize fracturing fluid viscosity, injection rate and proppant properties to control fracture morphology.

[0084] In one embodiment, the application examples of wellbore stability analysis may include: In the Shunbei oil and gas field, the Shunbei No. 1 and Shunbei No. 5 fault zones are important oil and gas production areas, but due to the deep burial of the formations and complex fault structures in the area, the risk of wellbore instability is high. There are multiple strike-slip faults in the area, and the lithology of the formations changes dramatically, resulting in a very uneven distribution of the ground stress field, especially near the faults where the ground stress is concentrated, and the wellbore stability faces great challenges.

[0085] Analysis of geostress around the wellbore. In Shunbei area, a three-dimensional geostress model is used to conduct a detailed analysis of the geostress around the wellbore, mainly including: Vertical stress : Determined by the weight of the overlying rock strata, the vertical stress in Shunbei area is usually higher due to its great burial depth.

[0086] Horizontal stress : Due to the existence of faults, the distribution of horizontal stress in Shunbei area is significantly uneven, especially the tectonic stress increment near the faults is significant.

[0087] The application of the wellbore instability criterion, combined with the calculation results of the wellbore circumferential stress in the Shunbei area, adopts the following criteria to evaluate the wellbore instability risk. The Mohr-Coulomb criterion is used to predict the instability risk of the wellbore under high shear stress. , hoop stress and effective normal stress , combined with the internal friction angle and cohesion of the rock, it can be determined whether there is a risk of shear instability in the wellbore wall.

[0088] Near the Shunbei No. 1 fault zone, due to the combined effect of horizontal stress and tectonic stress, the annular stress in some well sections The shear failure is significantly higher than other areas. By increasing the density of drilling fluid to increase the pressure in the well, the risk of shear instability can be effectively reduced.

[0089] The faults in Shunbei area are well developed and the formation stress state is complex. The Derek-Prager criterion is suitable for describing the yield behavior of rocks under such complex stress conditions. By calculating the stress around the wellbore wall, high-risk areas where plastic yield may occur are identified.

[0090] Example application: Near the Shunbei No. 5 fault zone, due to the large tectonic stress increment of the formation, the stress state of some well sections meets the yield condition of the Derek-Prager criterion. In these well sections, by optimizing the wellbore trajectory and avoiding the high tectonic stress area, the possibility of instability can be effectively reduced.

[0091] Mud pressure optimization: In order to ensure the stability of the wellbore, the pressure of the drilling fluid needs to be reasonably controlled. Due to the large changes in formation pressure in Shunbei area, the optimization of mud pressure can balance the external pressure on the wellbore with the ground stress, thus avoiding the collapse of the wellbore and leakage.

[0092] In Shunbei No. 1 Well, the mud pressure was adjusted to a range slightly higher than the pore pressure but lower than the fracture pressure, thereby reducing the risk of wellbore instability in areas of high stress concentration.

[0093] Wellbore reinforcement measures: For high stress concentration areas identified in the analysis, casing reinforcement measures are recommended. For example, in the key well section of Shunbei No. 5 Well, expansion pipes were installed to enhance the pressure bearing capacity of the wellbore, thereby effectively reducing the risk of wellbore instability.

[0094] The scheme of the present invention establishes a high-precision three-dimensional geostress field model and optimizes reservoir fracturing design and wellbore stability analysis through multi-source data fusion, including logging data reconstruction, seismic inversion and finite element simulation. The specific technical scheme includes: reconstruction based on logging data such as mineral composition and porosity to make up for data missing or distortion; using GABP genetic algorithm and neural network model to optimize rock mechanics parameter modeling and improve prediction accuracy; through seismic data and finite element simulation technology, generate three-dimensional geostress field in complex fault zones, and optimize reservoir fracturing and wellbore stability design. This technology breaks through the application bottleneck of traditional methods under complex geological conditions of ultra-deep oil and gas reservoirs, significantly improves prediction accuracy, provides technical support for the safe and efficient development of ultra-deep oil and gas reservoirs, and has broad market application prospects.

[0095] In one embodiment, a method for predicting geostress for ultra-deep carbonate oil and gas reservoirs includes: Establish calculation formulas for rock physics and rock mechanics parameters of different strata; Calculate rock physical parameters; Calculate rock mechanical parameters; Fitting calculation formulas for rock physics and mechanical parameters of different lithologies; Reconstruct logging curves, density curves based on mineral components, and shear wave time difference curves; Establishing geostress model; Calculate vertical geostress, horizontal geostress, and estimate tectonic stress; 3D geostress prediction based on seismic data; Obtain elastic parameters through seismic inversion, perform AVO analysis and elastic parameter calculation; Conduct three-dimensional geostress simulation, including numerical simulation, calculation of stress field, and analysis of geostress around the well; 3D geostress prediction based on seismic data; Conduct wellbore stability analysis and fracturing parameter optimization; Wellbore stress calculation, instability criteria.

[0096] According to a specific embodiment of the present invention, the calculation of rock physical parameters is specifically to calculate the total density, longitudinal wave velocity and shear wave velocity of the rock.

[0097] According to a specific embodiment of the present invention, the total density of the rock is calculated based on the mineral components. , the formula is as follows:

[0098] in Indicates Density of the mineral; Indicates Volume fraction of the mineral; represents the volume fraction of pore fluid; represents porosity; n represents a conventional coefficient, for example: 1.2.3…

[0099] According to a specific embodiment of the present invention, the calculation of the longitudinal wave velocity and the transverse wave velocity is specifically to first calculate the bulk modulus and shear modulus of the rock, and then calculate the longitudinal wave velocity and the transverse wave velocity using the elastic modulus of the rock.

[0100] According to a specific embodiment of the present invention, the calculation of rock mechanics parameters is specifically the calculation of Young's modulus and Poisson's ratio, shear modulus and bulk modulus, Lame constant, and rock strength parameters.

[0101] According to a specific embodiment of the present invention, the Young's modulus, Poisson's ratio and Lame's constant are calculated, and the Young's modulus, Poisson's ratio and Lame's constant can be calculated by bulk modulus and shear modulus.

[0102] According to a specific embodiment of the present invention, the rock strength parameter is calculated according to the Hooke-Brown criterion to calculate the compressive strength of the rock. Determine the Hooke-Brown parameters based on lithology , combined with the minimum principal stress and the tensile strength of rock , calculate the compressive strength of rock , the specific formula is as follows: , According to a specific embodiment of the present invention, the calculation formulas for fitting the rock physics and mechanical parameters of different lithologies are fitted by using core experimental data and measured data through multivariate nonlinear regression.

[0103] According to a specific embodiment of the present invention, the well logging curve is reconstructed, the density curve based on the mineral components is reconstructed, and the shear wave time difference curve is reconstructed.

[0104] According to a specific embodiment of the present invention, the density curve reconstruction based on mineral components, assuming that a certain section of the density curve is missing or distorted, can be reconstructed using mineral components and porosity.

[0105] According to a specific embodiment of the present invention, the shear wave time difference curve reconstruction can be predicted using other logging curves due to the high cost or data loss of shear wave logging.

[0106] According to a specific implementation of the present invention, a geostress model is established, vertical geostress and horizontal geostress are calculated, and tectonic stress is estimated.

[0107] According to a specific implementation scheme of the present invention, three-dimensional geostress prediction is performed based on seismic data, elastic parameters are obtained through seismic inversion, AVO analysis and elastic parameter calculation are performed; three-dimensional geostress simulation is performed, including numerical simulation, calculation of stress field, and analysis of geostress around the well.

[0108] According to a specific embodiment of the present invention, a wellbore stability analysis is performed. Considering the existence of the wellbore, the wellbore stress is calculated using the Cauchy-Davenport (Kirsch) solution. The possibility of wellbore instability is determined using the Moore-Coulomb criterion or the Drucker-Prager criterion.

[0109] The present invention improves the calculation accuracy of rock mechanical parameters. Through accurate logging data reconstruction and multi-dimensional correction technology, the problem of distortion and loss of logging data under complex geological conditions is solved. The calculation accuracy of rock mechanical parameters is improved, especially in complex formations such as fault zones and ultra-deep wells, accurately reflecting the real physical and mechanical properties of rocks. It lays a solid foundation for the accurate prediction of geostress fields and makes up for the shortcomings of traditional methods in terms of insufficient accuracy under complex structural conditions.

[0110] The present invention generates a high-precision three-dimensional geostress field. Combining seismic data inversion and finite element simulation, a high-precision three-dimensional geostress field model is successfully constructed. The model fully considers the anisotropy of geological conditions and the dynamic influence of fault activity, and can accurately describe the stress gradient and changes in complex geological structures. This is of great significance for the prediction of stress distribution in fault zones and ultra-deep formations, and improves the reliability and accuracy of geostress field prediction.

[0111] The present invention optimizes the reservoir transformation design and improves the fracturing effect. The high-precision geostress field model is used to simulate the expansion path of reservoir fracturing cracks to ensure that the cracks extend along the direction of the maximum principal stress. The reservoir transformation effect is improved, the oil and gas production is increased, and the efficient development of resources is achieved. It provides a scientific basis for fracturing construction, optimizes the design of fracturing parameters, and significantly improves the success rate and economic benefits of fracturing operations.

[0112] The present invention reduces the risk of wellbore instability and ensures safe production. The wellbore stability is dynamically analyzed and the instability risk of different well sections is predicted, especially the high stress concentration area and the low stress release area. Wellbore reinforcement and drilling fluid optimization schemes are proposed to reduce engineering risks such as wellbore collapse and leakage. It provides reliable technical guarantee for the safe production of oil and gas fields and reduces the risk of drilling operations under complex geological conditions.

[0113] The present invention provides a comprehensive solution to meet key technical requirements. The method integrates well logging data, seismic data and dynamic rock mechanics properties to provide a comprehensive rock mechanics parameter modeling and ground stress prediction solution. It is suitable for special geological environments such as complex fault zones, ultra-deep wells and high-temperature and high-pressure formations, and meets key technical requirements in engineering practice. It improves the optimization level of oil and gas well development plans and promotes the efficient and safe development of oil and gas resources.

[0114] In one embodiment, if Figure 4 As shown, a device 400 for predicting geostress in a complex fault zone includes: a first calculation module 401, a second calculation module 402, a reconstruction module 403 and a prediction module 404; The first calculation module 401 is used to calculate the rock physical parameters of the complex fault zone according to the rock physical parameter calculation formula; The second calculation module 402 is used to calculate the rock mechanical parameters of the complex fault zone according to the rock mechanical parameter calculation formula; The reconstruction module 403 is used to reconstruct the well logging curve of the complex fault zone to obtain the reconstructed well logging curve; The prediction module 404 is used to predict the geostress of a complex fault zone by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

[0115] The geostress prediction device for a complex fault zone provided in the above embodiment can implement the technical solution described in the above embodiment of a geostress prediction method for a complex fault zone. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above embodiment of a geostress prediction method for a complex fault zone, which will not be repeated here.

[0116] like Figure 5As shown, the present invention also provides an electronic device 500. The electronic device 500 includes a processor 501, a memory 502 and a display 503. Figure 5 Only some components of the electronic device 500 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0117] In some embodiments, the memory 502 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. In other embodiments, the memory 502 may also be an external storage device of the electronic device 500, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device 500.

[0118] Furthermore, the memory 502 may include both an internal storage unit of the electronic device 500 and an external storage device. The memory 502 is used to store application software installed in the electronic device 500 and various data.

[0119] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 502, such as a method for predicting geostress in a complex fault zone in the present invention.

[0120] In some embodiments, the display 503 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 503 is used to display information on the electronic device 500 and to display a visual user interface. The components 501-503 of the electronic device 500 communicate with each other via a system bus.

[0121] In some embodiments of the present invention, when the processor 501 executes the geostress prediction program for complex fault zones in the memory 502, the following steps may be implemented: Calculate the rock physical parameters of complex fault zones according to the rock physical parameter calculation formula; Calculate the rock mechanics parameters of complex fault zones according to the rock mechanics parameter calculation formula; Reconstruct the logging curve of the complex fault zone to obtain the reconstructed logging curve; The geostress of a complex fault zone is predicted by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

[0122] It should be understood that: when the processor 501 executes the geostress prediction program for complex fault zones in the memory 502, in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the corresponding method embodiments above.

[0123] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 500 mentioned, and the electronic device 500 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 500 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0124] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute a method for predicting in-situ stress of a complex fault zone provided by the above methods, the method comprising: Calculate the rock physical parameters of complex fault zones according to the rock physical parameter calculation formula; Calculate the rock mechanics parameters of complex fault zones according to the rock mechanics parameter calculation formula; Reconstruct the logging curve of the complex fault zone to obtain the reconstructed logging curve; The geostress of a complex fault zone is predicted by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

[0125] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0126] The above is a detailed introduction to the method, device, equipment and medium for predicting geostress in a complex fault zone provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for predicting geostress in a complex fault zone, characterized in that: include: Calculate the rock physical parameters of complex fault zones according to the rock physical parameter calculation formula; Calculate the rock mechanics parameters of complex fault zones according to the rock mechanics parameter calculation formula; Reconstruct the logging curve of the complex fault zone to obtain the reconstructed logging curve; The geostress of a complex fault zone is predicted by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

2. The method for predicting geostress in a complex fault zone according to claim 1, characterized in that: Also includes: The rock physical parameter calculation formula and the rock mechanical parameter calculation formula are established according to the lithology data of different strata.

3. A method for predicting geostress in a complex fault zone according to claim 1 or 2, characterized in that: Also includes: The rock physical parameter calculation formula and the rock mechanical parameter calculation formula are fitted to obtain the well logging curve.

4. The method for predicting geostress in a complex fault zone according to claim 3, characterized in that: The logging curves include: density curve and shear wave time difference curve; The step of fitting the rock physical parameter calculation formula and the rock mechanical parameter calculation formula to obtain the well logging curve specifically includes: By fitting the rock physical parameter calculation formula, a density curve is obtained; By fitting the rock mechanics parameter calculation formula, a shear wave time difference curve is obtained.

5. The method for predicting geostress in a complex fault zone according to claim 4, characterized in that: The reconstruction of the logging curve of the complex fault zone specifically includes: The density curve is reconstructed by using the formation mineral composition and formation porosity of the complex fault zone to obtain a reconstructed density curve.

6. The method for predicting geostress in a complex fault zone according to claim 1, characterized in that: Also includes: Calculate elastic parameters through seismic inversion data to obtain rock elastic parameters; Assigning the rock elastic parameters to the three-dimensional geostress model to obtain the assigned three-dimensional geostress model; The three-dimensional geostress model is used to predict the geostress of a complex fault zone in combination with the reconstructed well logging curve, the rock mechanical parameters and the rock physical parameters, specifically including: The geostress of a complex fault zone is predicted by using the assigned three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanics parameters and the rock physics parameters.

7. The method for predicting geostress in a complex fault zone according to claim 1, characterized in that: Also includes: The geostress is verified through three-dimensional geostress simulation, and when the verification result meets the requirements, the stability analysis of the wellbore wall of the complex fault zone is performed based on the geostress.

8. A device for predicting ground stress in a complex fault zone, characterized in that: include: A first calculation module, a second calculation module, a reconstruction module and a prediction module; The first calculation module is used to calculate the rock physical parameters of the complex fault zone according to the rock physical parameter calculation formula; The second calculation module is used to calculate the rock mechanics parameters of the complex fault zone according to the rock mechanics parameter calculation formula; The reconstruction module is used to reconstruct the well logging curve of the complex fault zone to obtain the reconstructed well logging curve; The prediction module is used to predict the geostress of a complex fault zone by using a three-dimensional geostress model in combination with the reconstructed logging curve, the rock mechanical parameters and the rock physical parameters.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for predicting geostress in a complex fault zone as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of a method for predicting geostress in a complex fault zone as described in any one of claims 1 to 7.

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