A bedding shale reservoir secondary fracturing fault activation risk grading prevention and control method

Abstract

The application belongs to the technical field of cross of electric digital data processing and rock mechanics, and discloses a bedding shale reservoir secondary fracturing fault activation risk grading prevention and control method. The application collects multi-source basic data of the bedding shale reservoir and carries out standardized pretreatment, constructs a fluid-solid coupling FDEM hydraulic pressure numerical model considering multi-factor coupling, embeds fault and wellbore parameters to construct a fracturing-fault-wellbore coupling FDEM numerical model and verifies it, executes multi-working-condition numerical simulation to obtain relevant data, identifies main control factors of fault activation and constructs a multi-dimensional quantitative risk grading evaluation model, inputs target well field data to calculate a risk grade, matches a preset strategy to generate a fracturing parameter optimization and construction control scheme. The application can effectively improve the accuracy and pertinence of fault activation risk prevention and control, and guarantee fracturing construction safety and effect.

Description

Technical Field

[0001] This application relates to the interdisciplinary fields of electrical digital data processing and rock mechanics, specifically to a method for risk classification and prevention of activation of fracturing faults in bedding shale reservoirs. Background Technology

[0002] In secondary fracturing of layered shale reservoirs, fault activation is easily induced, leading to wellbore deformation, casing damage, and other problems, affecting the safety and effectiveness of fracturing operations. Current technologies for controlling the risk of fault activation during secondary fracturing mostly employ simplified numerical models, failing to fully consider the impact of weak bedding planes, mineral heterogeneity, and natural fractures on fracture propagation. Furthermore, they lack a established coupling relationship between fracturing, faults, and the wellbore, resulting in inaccurate identification and assessment of fault activation risks and an inability to develop targeted control measures based on different risk levels. Summary of the Invention

[0003] To solve, or at least partially solve, the above-mentioned technical problems, this application provides a method for risk classification and prevention of activation of fracturing faults in layered shale reservoirs.

[0004] This application provides a method for risk classification and control of activation of fractured faults in layered shale reservoirs, including the following steps:

[0005] S1. Collect multi-source basic data of bedding shale reservoirs and perform standardized preprocessing;

[0006] S2. Based on the preprocessed multi-source basic data, construct a fluid-structure interaction FDEM hydraulic fracturing numerical model that considers multi-factor coupling.

[0007] S3. Based on the fluid-structure interaction FDEM hydraulic fracturing numerical model, fault parameters and wellbore parameters are embedded to construct a fracturing-fault-wellbore coupled FDEM numerical model, and the fracturing-fault-wellbore coupled FDEM numerical model is verified.

[0008] S4. Run the fracturing-fault-wellbore coupled FDEM numerical model, perform multi-condition numerical simulation, and obtain fracture propagation data, fault slip data and wellbore deformation data under different conditions;

[0009] S5. Based on the fracture propagation data, the fault slip data and the wellbore deformation data, identify the main controlling factors of fault activation and construct a multi-dimensional quantitative risk classification and evaluation model.

[0010] S6. Input the basic data of the target well site, and calculate the fault activation risk level of the target area in the target well site through the multi-dimensional quantitative risk classification and evaluation model.

[0011] S7. Based on the fault activation risk level of the target area, match the preset targeted prevention and control strategy library to generate corresponding fracturing parameter optimization schemes and construction management schemes.

[0012] Optionally, the fluid-structure interaction (FDEM) hydraulic fracturing numerical model is a numerical model that simultaneously couples bedding weak surface parameters, mineral heterogeneity parameters, and discrete fracture network parameters of natural fractures.

[0013] Optionally, the step of embedding fault parameters and wellbore parameters into the fluid-structure interaction FDEM hydraulic fracturing numerical model to construct a fracturing-fault-wellbore coupled FDEM numerical model specifically includes:

[0014] From the preprocessed multi-source basic data, the fault parameters, wellbore parameters, and initial fracturing damage zone parameters are extracted, wherein the fault parameters include fault interface mechanical parameters, and the wellbore parameters include wellbore geometric parameters and wellbore mechanical parameters; the fault parameters, wellbore parameters, and initial fracturing damage zone parameters are embedded into the grid cells corresponding to the fluid-structure interaction FDEM hydraulic fracturing numerical model;

[0015] The embedded parameters are assigned values ​​according to the geological zoning of the target area, and the parameters after zoning are corrected based on the results of indoor rock mechanics tests.

[0016] Generate the fracturing-fault-wellbore coupled FDEM numerical model;

[0017] The verification process of the fracturing-fault-wellbore coupled FDEM numerical model was carried out by combining comparison with KGD theoretical model, comparison with indoor physical test data, and comparison with field microseismic monitoring data.

[0018] Optionally, S4 specifically includes:

[0019] Multiple sets of different simulation conditions were set up, each corresponding to different natural crack strengths, different mineral component ratios, and different geostress differences.

[0020] Run the coupled FDEM numerical model of fracturing-fault-wellbore corresponding to each set of working conditions in sequence;

[0021] Extract the corresponding crack propagation data, fault slip data, and wellbore deformation data from the simulation results of each working condition;

[0022] Based on the fracture propagation data, fault slip data, and wellbore deformation data, the influence of natural fracture strength, mineral composition ratio, and geostress difference on the main fracture extension direction, fracture network complexity, and weak surface activation probability is analyzed. The influence rules are used to identify the main controlling factors of fault activation and optimize fracturing parameters.

[0023] Optionally, the main controlling factors of fault activation include fault dip angle, distance between the fault and the wellbore, and geostress difference; after identifying the main controlling factors of fault activation based on the fracture propagation data, the fault slip data, and the wellbore deformation data, the method further includes:

[0024] The nonlinear response laws of each controlling factor to fault slip and wellbore deformation are quantitatively analyzed, and the nonlinear response laws are used to construct the multidimensional quantitative risk classification evaluation model.

[0025] Optionally, the evaluation indicators of the multi-dimensional quantitative risk classification evaluation model include the fault dip angle, the distance between the fault and the wellbore, the geostress difference, and the mineral heterogeneity.

[0026] The multi-dimensional quantitative risk classification and evaluation model divides the fault activation risk level into three levels: high risk, medium risk, and low risk, which correspond to different fault slip thresholds, wellbore deformation thresholds, and fracture propagation range thresholds, respectively.

[0027] Optionally, the preset targeted prevention and control strategy library includes prevention and control strategies for high-risk areas, such as the calculation of the minimum safe distance between well location and perforation section, the setting of upper limit thresholds for injection flow rate and pump pressure, and the control method for the intensity of ground stress disturbance.

[0028] Optionally, the preset targeted prevention and control strategy library includes prevention and control strategies for medium-risk areas, such as fracturing parameter adjustment methods based on real-time monitoring data and early warning threshold setting and early warning triggering methods for microseismic monitoring.

[0029] Optionally, in the preset targeted prevention and control strategy library, the prevention and control strategy corresponding to the low-risk area is conventional fracturing construction and conventional monitoring;

[0030] The fracturing parameter optimization scheme is a scheme that optimizes multiple parameters, including perforation parameters, injection flow rate, pump pressure, and construction sequence.

[0031] Optionally, after S7, the method further includes:

[0032] Establish an update interface between real-time monitoring data and the coupled FDEM numerical model of fracturing-fault-wellbore.

[0033] The method provided in this application has the following beneficial effects:

[0034] This application, after collecting and standardizing preprocessed multi-source basic data of layered shale reservoirs, constructs a fluid-structure interaction FDEM (fluid-structure interaction emulation) hydraulic fracturing numerical model considering multi-factor coupling. This model can fully reflect the geological characteristics of layered shale reservoirs, simulate the interaction between fluid seepage and rock deformation during hydraulic fracturing, and improve the simulation accuracy of fracture propagation. Based on the above model, a fracturing-fault-wellbore coupled FDEM numerical model is constructed and validated. This model can simultaneously simulate the interaction between hydraulic fracture propagation, fault slip, and wellbore deformation during secondary fracturing, solving the problem that existing technologies cannot accurately reflect the coupling relationship among these three factors, making the simulation results more consistent with actual field conditions.

[0035] Multi-condition numerical simulations are performed to acquire fracture propagation, fault slip, and wellbore deformation data under different conditions. This allows for a systematic analysis of the impact of varying geological and construction conditions on the secondary fracturing process, providing ample data support for identifying the main controlling factors of fault activation. Based on the simulation results, the main controlling factors of fault activation are identified, and a multi-dimensional quantitative risk grading evaluation model is constructed. This model comprehensively considers the combined effects of multiple influencing factors, avoiding biases caused by single-factor evaluations and improving the accuracy of fault activation risk level calculations. Pre-defined targeted prevention and control strategies are matched according to the risk level of the target area, generating corresponding fracturing parameter optimization schemes and construction management schemes. This enables the development of differentiated prevention and control measures for different risk levels, effectively reducing fault activation risk, ensuring fracturing construction safety, while also considering fracturing stimulation effects and improving reservoir development efficiency. Attached Figure Description

[0036] Figure 1 A schematic diagram of a risk classification and control method for the activation of fracturing faults in layered shale reservoirs provided in this application embodiment;

[0037] Figure 2 A schematic diagram of the evolution of mineral components in shale water-rock interaction is provided for an embodiment of this application;

[0038] Figure 3 A schematic diagram illustrating the evolution of the mechanical properties of Brazilian shale splitting is provided in an embodiment of this application.

[0039] Figure 4 A schematic diagram illustrating the evolution of triaxial compressive mechanical properties of shale provided in this application embodiment;

[0040] Figure 5 A schematic diagram illustrating the fractal dimension variation of Brazilian splitting fractures in shale, provided as an embodiment of this application;

[0041] Figure 6 This is a schematic diagram illustrating the influence of mineral size on shale mechanical parameters, provided as an embodiment of this application.

[0042] Figure 7This application provides a schematic diagram of the hydraulic fracturing response of a natural fracture strength according to an embodiment of the present application.

[0043] Figure 8 A schematic diagram illustrating the influence of fault dip angle on slip amount provided in an embodiment of this application;

[0044] Figure 9 This is a schematic diagram illustrating the influence of ground stress difference on fault slippage, provided as an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0046] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0047] See Figures 1 to 9 This application provides a method for risk classification and control of activation of genetically modified fracturing faults in layered shale reservoirs, comprising the following steps:

[0048] S1. Collect multi-source basic data of bedding shale reservoirs and perform standardized preprocessing.

[0049] Specifically, the first step is to collect multi-source basic data on layered shale reservoirs and perform standardized preprocessing. This multi-source basic data can include geological data, rock mechanics data, fracturing operation data, fault data, wellbore data, and microseismic monitoring data. Geological data includes reservoir depth, reservoir thickness, magnitude and direction of geostress, bedding distribution characteristics, mineral composition distribution, and natural fracture distribution characteristics. Rock mechanics data includes uniaxial compressive strength, uniaxial tensile strength, elastic modulus, Poisson's ratio, cohesion, and internal friction angle. Fracturing operation data includes injection rate, pump pressure, injected fluid volume, perforation parameters, and operation sequence. Fault data includes fault spatial location, fault dip angle, fault strike, and fault interface mechanical parameters. Wellbore data includes wellbore geometric parameters, wellbore mechanical parameters, and wellbore layout. Microseismic monitoring data includes the occurrence time, spatial location, and magnitude of microseismic events.

[0050] The core basic parameters of typical deep marine bedding shale reservoirs can be used as reference benchmarks for data acquisition: reservoir burial depth 1900–3700 m, reservoir thickness 178.2–460 m, total organic carbon content 2.5%–4.3%, total gas content 2.3–6.1 m³ / t; the overall geostress is in a strike-slip stress state, with a stress ratio Q value of 1.1–2.0, averaging 1.6; faults are mainly small-displacement natural fractures and local small faults, with no large-scale penetrating main faults.

[0051] The collected multi-source basic data then underwent standardized preprocessing. First, the formats of data from different sources were unified, establishing standardized data coding and storage specifications. Next, outliers were removed using a 3-standard-deviation rule, identifying and removing data deviating more than 3 standard deviations from the dataset mean. For missing values, interpolation was used for completion: linear interpolation was used for time-series or continuously varying geological parameters; kriging interpolation was used for parameters with spatial distribution characteristics, calculating approximate values ​​for the missing values ​​based on surrounding valid data. Finally, data of different dimensions were normalized, converting them into dimensionless values ​​within the 0-1 range to eliminate the impact of dimensional differences on subsequent analysis results.

[0052] S2. Based on the preprocessed multi-source basic data, construct a fluid-structure interaction FDEM hydraulic fracturing numerical model that considers multi-factor coupling.

[0053] In some implementations, the fluid-structure interaction FDEM (finite element-discrete element method) hydraulic fracturing numerical model is a numerical model that simultaneously couples the parameters of weak bedding planes, mineral heterogeneity parameters, and discrete fracture network parameters of natural fractures.

[0054] The fluid-structure interaction (FDEM) numerical model for hydraulic fracturing can simulate the interaction between fluid injection, fracture propagation, fluid seepage, and reservoir rock deformation during hydraulic fracturing, providing a foundation for subsequent analysis of fracture propagation patterns and identification of the main controlling factors of fault activation.

[0055] When constructing a fluid-structure interaction (FDEM) numerical model for hydraulic fracturing, the model is first divided into grid cells based on the geological characteristics of the target reservoir. The size of the grid cells is determined according to the reservoir scale and computational accuracy requirements. Then, basic mechanical and fluid parameters of the reservoir rock are extracted from preprocessed multi-source data, and these parameters are assigned to the corresponding grid cells. For typical deep marine bedding shale, industry-standard parameters can be used as initial assignment references. Next, fluid flow control equations and solid deformation control equations are established. By iteratively solving these two control equations, the coupling of fluid pressure and solid deformation is achieved, simulating the fluid seepage in the fractures and reservoir matrix during hydraulic fracturing, as well as the deformation and fracturing process of the reservoir rock under fluid pressure.

[0056] The fluid-structure interaction (FDEM) hydraulic fracturing numerical model simultaneously couples parameters of weak bedding planes, mineral heterogeneity, and discrete fracture network parameters of natural fractures. Bedding is the most prominent geological feature of bedding shale reservoirs. The mechanical strength of weak bedding planes is much lower than that of the reservoir matrix, which significantly affects the propagation direction and morphology of hydraulic fractures. From preprocessed geological data, the distribution location, thickness, spacing, and mechanical parameters of bedding interfaces, such as cohesion, internal friction angle, and tensile strength, are extracted. In the FDEM hydraulic fracturing numerical model, weak bedding planes are treated as special interface units and assigned corresponding mechanical parameters. Typical bedding interface cohesion ranges from 5 to 15 MPa, and internal friction angles range from 20° to 35°, simulating the blocking, deflection, and penetration effects of weak bedding planes on hydraulic fractures.

[0057] Mineral heterogeneity is another important characteristic of bedding shale reservoirs. The significant differences in mechanical properties among different mineral components can affect fracture initiation and propagation. This study extracts the distribution characteristics and proportions of mineral components in different reservoir regions from preprocessed geological data. Based on these differences, the reservoir is divided into multiple mineral units, each assigned corresponding rock mechanical parameters. The typical mineral composition of deep marine bedding shale is approximately 50% calcite, 34% quartz, 6.5% dolomite, 5.9% illite, 1.7% kaolinite, and 1.7% pyrite. Figure 2As shown, the vertical axis represents immersion time, and the horizontal axis represents mineral composition content, illustrating the evolution of major shale minerals under flowback fluid immersion. Carbonate minerals remained generally stable, while illite showed a trend of first decreasing, then increasing, and then stabilizing. The most significant dissolution of clay minerals occurred between 3 and 7 days. The corresponding mineral mechanical parameters are: quartz elastic modulus 90 GPa, Poisson's ratio 0.07; calcite elastic modulus 80 GPa, Poisson's ratio 0.25; illite elastic modulus 40 GPa, Poisson's ratio 0.25. Units with higher brittle mineral content have lower tensile and shear strengths, making them more prone to cracking; units with higher ductile mineral content have stronger plastic deformation capacity, inhibiting crack propagation. Figure 3 As shown, in subfigure a, the horizontal axis represents strain, and the vertical axis represents stress; in subfigure b, the horizontal axis represents minerals of different sizes (mineral grain sizes are set to 0.0003 mm (model 1), 0.0005 mm (model 2), 0.0007 mm (model 3), 0.0009 mm (model 4), and 0.001 mm (model 5), respectively), and the vertical axis represents compressive strength; in subfigures c and d, the horizontal axis represents time, and the vertical axes represent the total fracture area and the proportion of tensile failure, respectively. The smaller the mineral size, the higher the peak rock strength and the greater the proportion of tensile failure; as the mineral size increases, the rock strength decreases, the total fracture area increases, and the failure mode changes from pure tension to a mixed tension-shear mode. In this way, the influence of mineral heterogeneity on the propagation path and fracture network complexity of hydraulic fractures is simulated.

[0058] Natural fractures are widely distributed in layered shale reservoirs, and the interaction between natural fractures and hydraulic fractures significantly alters the morphology and distribution of the fracture network. Spatial location, length, width, strike, dip angle, and mechanical parameters of the natural fracture interface were extracted from preprocessed geological data and microseismic monitoring data. A discrete fracture network was constructed based on these extracted parameters. This network was then embedded into the mesh of a fluid-structure interaction (FDEM) hydraulic fracturing numerical model, and assigned corresponding mechanical parameters. Typical tensile strengths of natural fractures ranged from 1 to 7 MPa, shear strengths from 3 to 21 MPa, and the tensile-to-shear ratio was 1:3. The model simulated the interaction process when hydraulic fractures encounter natural fractures, including scenarios such as hydraulic fractures extending along natural fractures, hydraulic fractures crossing natural fractures, and hydraulic fractures bifurcating at natural fracture points.

[0059] By simultaneously coupling bedding weak surface parameters, mineral heterogeneity parameters, and discrete fracture network parameters of natural fractures, the geological characteristics of bedding shale reservoirs can be more realistically reproduced, improving the simulation accuracy of the fluid-structure coupled FDEM hydraulic fracturing numerical model and making the simulated fracture propagation law more consistent with the actual field situation.

[0060] S3. Based on the fluid-structure interaction FDEM hydraulic fracturing numerical model, fault parameters and wellbore parameters are embedded to construct a fracturing-fault-wellbore coupled FDEM numerical model, and the fracturing-fault-wellbore coupled FDEM numerical model is verified.

[0061] In some implementations, fault parameters and wellbore parameters are embedded into the fluid-structure interaction FDEM hydraulic fracturing numerical model to construct a fracturing-fault-wellbore coupled FDEM numerical model, specifically including:

[0062] Fault parameters, wellbore parameters, and primary fracturing damage zone parameters are extracted from the preprocessed multi-source basic data. The fault parameters include mechanical parameters of the fault interface, and the wellbore parameters include geometric and mechanical parameters of the wellbore. The fault parameters, wellbore parameters, and primary fracturing damage zone parameters are then embedded into the corresponding grid cells of the fluid-structure interaction FDEM hydraulic fracturing numerical model.

[0063] The embedded parameters are assigned values ​​according to the geological zoning of the target area, and the parameters after zoning are corrected based on the results of indoor rock mechanics tests.

[0064] Generate a coupled FDEM numerical model of fracturing-fault-wellbore;

[0065] The process of validating the coupled FDEM numerical model of fracturing-fault-wellbore was carried out by combining comparison with KGD theoretical model, comparison with indoor physical test data, and comparison with field microseismic monitoring data.

[0066] The fluid-structure coupled FDEM hydraulic fracturing numerical model, which only considers the characteristics of the reservoir itself, cannot reflect the interaction between hydraulic fractures, faults, and wellbore during secondary fracturing. Furthermore, it does not consider the evolution of reservoir mechanical properties caused by water-rock interaction after primary fracturing. Fault activation induced by secondary fracturing can directly lead to wellbore deformation and casing damage. Therefore, it is necessary to construct a coupled numerical model that simultaneously includes the reservoir, faults, and wellbore to achieve synchronous simulation of the mechanical and fluid interactions among the three.

[0067] Specifically, fault parameters, wellbore parameters, and initial fracturing damage zone parameters are extracted from the preprocessed multi-source basic data. Fault parameters include fault interface mechanical parameters, as well as geometric parameters such as fault location, strike, dip angle, displacement, and fault zone width. The fault interface mechanical parameters specifically include the fault interface cohesion, internal friction angle, tensile strength, shear stiffness, and normal stiffness. Wellbore parameters include wellbore geometric parameters and wellbore mechanical parameters. Wellbore geometric parameters include wellbore diameter, wall thickness, depth, wellbore trajectory, perforation location, and perforation density. Wellbore mechanical parameters include the elastic modulus, Poisson's ratio, yield strength, tensile strength, and shear strength of the wellbore material. The parameters of the initial fracturing damage zone include the spatial extent of the damage zone formed by the initial fracturing, the distribution of damage severity, and the attenuation law of rock mechanical parameters within the damage zone. The typical baseline mechanical parameters of a reservoir after long-term water-rock interaction are: elastic modulus 22.79 GPa (recovered to 101.5% of the initial value), Poisson's ratio 0.35 (higher than the initial value by 0.29), tensile strength 10.74 MPa (recovered to 95.6% of the initial value), and triaxial compressive strength 326.40 MPa (recovered to 95.9% of the initial value). Figure 4 As shown, subplot a represents strain on the horizontal axis and stress on the vertical axis, while subplot b represents immersion time on the horizontal axis and tensile strength on the vertical axis. This verifies the non-monotonic evolution pattern of shale tensile strength weakening and recovering with immersion time. The shale tensile strength reaches a minimum value of 7.50 MPa after 15 days and recovers to 95.6% of its initial value after 45 days. All samples maintain typical brittle fracture characteristics. Figure 5 As shown, the horizontal axis of subplots represents strain, and the vertical axis represents stress. Subplots b, c, and d show the changes in triaxial compressive strength of shale with soaking time, and the vertical axes represent compressive strength, elastic modulus, and Poisson's ratio, respectively. The results demonstrate the trend of triaxial compressive strength of shale with soaking time, decreasing to a minimum of 256.92 MPa after 7 days and recovering to 95.9% of the initial value after 45 days. Poisson's ratio exhibits a change pattern opposite to that of strength. The initial fracturing will create a certain range of rock damage zone around the wellbore. The rock mechanical strength in this area is significantly reduced, which will affect the direction of fracture propagation and the probability of fault activation during secondary fracturing.

[0068] The extracted fault parameters, wellbore parameters, and primary fracturing damage zone parameters are embedded into the corresponding mesh elements of the fluid-structure interaction (FDEM) hydraulic fracturing numerical model. The fault is treated as a special interface element, assigned corresponding fault interface mechanical parameters, and the slip, opening, and closing behavior of the fault interface is simulated. The wellbore is embedded as a beam element into the reservoir mesh, assigned corresponding wellbore geometric and mechanical parameters, and the stress and deformation behavior of the wellbore under reservoir deformation and fault slip is simulated. The mesh elements corresponding to the primary fracturing damage zone are assigned attenuated rock mechanical parameters to simulate the impact of primary fracturing damage on the secondary fracturing process.

[0069] The embedded parameters were assigned values ​​according to the geological zoning of the target area, and the assigned parameters were then corrected based on the results of indoor rock mechanics tests. Based on the lithological distribution, bedding development, fault distribution characteristics, and the impact range of the initial fracturing in the target area, the area was divided into several different geological zones. These zones exhibit significant differences in geological characteristics and rock mechanical properties. For each grid cell within a geological zone, corresponding parameter values ​​were assigned to avoid simulation errors caused by using uniform parameters. Indoor rock mechanics tests were conducted on core samples collected from different geological zones to obtain mechanical parameters such as uniaxial compressive strength, uniaxial tensile strength, elastic modulus, and Poisson's ratio under different lithologies and damage levels. The parameters obtained from the indoor tests were used to correct the zoning parameters extracted from multi-source basic data, further improving the accuracy of the parameter assignment.

[0070] After completing parameter embedding, partition assignment, and parameter correction, a coupled FDEM numerical model of fracturing-fault-wellbore is generated. The generated coupled model is initialized, and the boundary conditions and initial conditions of the model are determined, including the geostress boundary conditions, fluid pressure boundary conditions, initial geostress field distribution, initial fluid pressure field distribution, and initial damage field distribution.

[0071] The verification process for the coupled hydraulic fracturing-fault-wellbore FDEM numerical model combines comparison with KGD (a classic two-dimensional fracture propagation model) theoretical model, indoor physical test data, and field microseismic monitoring data. First, the simulation results of the coupled model under simple boundary conditions and homogeneous reservoir conditions are compared with the calculation results of the KGD theoretical model to verify the model's correctness in simulating the basic hydraulic fracturing process. Typical verification results show that the numerical solution for fracture length deviates from the theoretical solution by less than 5%, and the numerical solution for fracture width deviates from the theoretical solution by less than 8%. Then, the indoor hydraulic fracturing physical test results simulated by the coupled model are compared with data on fracture propagation morphology, fracture length, fracture width, and fracture propagation velocity obtained from actual indoor tests. Simultaneously, verification is supplemented by the Brazilian splitting test and triaxial compression test, comparing peak strength, elastic modulus, failure mode, fracture fractal dimension, and other indicators to verify the model's simulation accuracy of the fracture propagation process. Finally, the range and direction of secondary fracturing fractures simulated by the coupled model in the drilled well were compared with the spatial distribution and timing of microseismic events obtained from on-site microseismic monitoring of the well, verifying the applicability of the model under complex on-site geological conditions. The coupled fracturing-fault-wellbore FDEM numerical model, validated through multiple methods, can accurately simulate the interaction between hydraulic fracture propagation, fault slip, and wellbore deformation during secondary fracturing.

[0072] S4. Run the coupled FDEM numerical model of fracturing-fault-wellbore, perform multi-condition numerical simulation, and obtain fracture propagation data, fault slip data and wellbore deformation data under different conditions.

[0073] In some implementations, S4 specifically includes:

[0074] Multiple sets of different simulation conditions were set up, each corresponding to different natural crack strengths, different mineral component ratios, and different geostress differences.

[0075] Run the coupled FDEM numerical model of fracturing-fault-wellbore corresponding to each set of working conditions in sequence;

[0076] The corresponding crack propagation data, fault slip data, and wellbore deformation data were extracted from the simulation results of each working condition.

[0077] Based on fracture propagation data, fault slip data, and wellbore deformation data, we analyze the influence of natural fracture strength, mineral composition ratio, and geostress difference on the main fracture extension direction, fracture network complexity, and weak surface activation probability. The influence patterns are used to identify the main controlling factors of fault activation and optimize fracturing parameters.

[0078] Through multi-condition numerical simulation, the influence of different geological and construction conditions on the secondary fracturing process can be systematically analyzed, and the correspondence between each factor and fracture propagation, fault slip, and wellbore deformation can be clarified, providing data support for subsequent identification of the main controlling factors of fault activation and the formulation of prevention and control measures.

[0079] Multiple simulation conditions were set up, each corresponding to different natural fracture intensities, mineral composition ratios, and geostress differences. The parameter gradients covered the range of common geological conditions in the target area: natural fracture intensities were set to 1 MPa, 3 MPa, 5 MPa, and 7 MPa; mineral composition was set to clay content of 6%–28% and corresponding brittle mineral content of 72%–94%; and geostress differences were set to 0 MPa, 5 MPa, 10 MPa, and 15 MPa. The simulation conditions were set up using a combination of single-factor and multi-factor combinations. First, single-factor conditions with only a single parameter changed were set up to analyze the independent effects of a single factor on fracture propagation, fault slip, and wellbore deformation. Then, multi-factor combination conditions with multiple parameters changing simultaneously were set up to analyze the interactions between different factors.

[0080] The coupled FDEM numerical model for each set of working conditions was run sequentially. During the model run, the fluid flow control equation, solid deformation control equation, and fracture propagation control equation were iteratively solved according to the preset time step. Simultaneously, the fluid pressure distribution, rock stress and deformation distribution, fracture propagation state, fault slip state, and wellbore stress and deformation state within the reservoir were calculated at each time step. The calculation results of each time step were stored to form complete simulation process data.

[0081] Subsequently, corresponding fracture propagation data, fault slip data, and wellbore deformation data were extracted from the simulation results of each working condition. Fracture propagation data included main fracture length, main fracture width, main fracture extension direction, number of branch fractures, branch fracture length, total fracture network area, fracture network complexity index, number of activated weak bedding planes, and number of activated natural fractures. The fracture network complexity was quantified using fractal dimension, with typical ranges for two-dimensional fracture fractal dimension being 1.02–1.10 and for three-dimensional fracture fractal dimension being 2.02–2.24. For example... Figure 6 As shown, the horizontal axis represents immersion time, and the vertical axis represents the fractal dimension of the fracture. The fractal dimension of the fracture first decreases and then increases with immersion time, reaching a minimum of 1.02 after 3 days and a peak of 1.10 after 30 days, reflecting the dynamic change in fracture complexity at different stages of water-rock interaction. Fault slip data includes maximum fault slip, average fault slip, fault slip velocity, fault slip range, time of fault slip occurrence, and stress changes during fault slip. Wellbore deformation data includes maximum radial deformation, average radial deformation, axial deformation, circumferential stress distribution, axial stress distribution, maximum stress value, and stress concentration location. A unified simulation results database was established based on all extracted data.

[0082] Based on fracture propagation data, fault slip data, and wellbore deformation data, this study analyzes the influence of natural fracture strength, mineral composition ratio, and geostress difference on the main fracture extension direction, fracture network complexity, and weak surface activation probability. When the natural fracture strength is low, hydraulic fractures are more likely to interact with natural fractures, increasing the probability of natural fracture activation and making it easier for fractures to extend along them, forming complex fracture network structures. When the natural fracture strength is high, hydraulic fractures are less likely to penetrate natural fractures, instead extending directly through them, resulting in a relatively simple fracture network structure. Specifically, when the natural fracture strength is 5 MPa, the total fracture area reaches its maximum value, increasing by approximately 40% compared to 1 MPa. Figure 7As shown, the horizontal axis of all subplots represents fracturing time, and the vertical axis represents the number of fracture units, the proportion of tensile failure, the total fracture length, the maximum fracture aperture, and the total fracture area, respectively. Different colored curves represent different natural fracture intensities (1, 3, 5, and 7 MPa). When the natural fracture intensity is 5 MPa, the total fracture area reaches its maximum value, achieving the best synergy between main fracture penetration and branch fracture activation, forming the most complex fracture network structure. When the proportion of brittle minerals in the mineral composition is high, the rock is more brittle and more likely to generate multiple branch fractures after fracturing, resulting in a more complex fracture network. When the proportion of plastic minerals is high, the rock is more prone to plastic deformation, the resistance to fracture propagation is greater, the number of branch fractures is less, and the fracture network structure is relatively simple. When the clay content reaches 28%, the proportion of tensile failure drops to 0.75, and the characteristics of mixed tensile-shear failure are significant. When the stress difference is large, the main fracture is more likely to extend along the direction of the maximum principal stress, the fracture extension direction is relatively stable, and fewer branch fractures develop. When the stress difference is small, the uncertainty of the fracture extension direction increases, making it more prone to deflection and bifurcation, and the fracture network becomes more complex. Among these, the fracture network complexity is highest when the stress difference is 5 MPa, with a fractal dimension exceeding 2.1. Furthermore, different combinations of parameters result in varying fracture network complexity, leading to differences in the range and intensity of stress disturbances within the reservoir. This, in turn, affects the activation probability and slippage degree of faults, as well as the stress and deformation of the wellbore.

[0083] The aforementioned influence patterns are used to identify the main controlling factors of fault activation and optimize fracturing parameters. By clarifying the degree and mode of influence of each factor on fracture propagation, fault slip, and wellbore deformation, the factors with the most significant impact on fault activation risk can be screened, providing a basis for subsequent identification of the main controlling factors of fault activation. Simultaneously, based on the influence patterns of different parameters on fracture network complexity, the parameter range that is conducive to the formation of complex fracture networks while reducing the risk of fault activation can be determined, providing guidance for subsequent fracturing parameter optimization.

[0084] S5. Based on fracture propagation data, fault slip data, and wellbore deformation data, identify the main controlling factors of fault activation and construct a multi-dimensional quantitative risk classification and evaluation model.

[0085] In some implementations, the main controlling factors of fault activation include fault dip angle, distance between the fault and the wellbore, and geostress difference; after identifying the main controlling factors of fault activation based on fracture propagation data, fault slip data, and wellbore deformation data, the method further includes:

[0086] The nonlinear response laws of each controlling factor to fault slip and wellbore deformation were quantitatively analyzed, and the nonlinear response laws were used to construct a multi-dimensional quantitative risk classification and evaluation model.

[0087] Based on fracture propagation data, fault slip data, and wellbore deformation data, the main controlling factors of fault activation are identified, and the nonlinear response laws of each controlling factor to fault slip and wellbore deformation are quantitatively analyzed. Combined with previously obtained fracture propagation patterns under different geological conditions, a more comprehensive analysis of the combined impact of various factors on the fault activation process can be conducted, clarifying the mechanisms and degrees of influence of different factors, and identifying the core factors that play a decisive role in fault activation risk.

[0088] Multiple numerical simulations were conducted using a combination of single-factor variable method and orthogonal experimental method. Based on previously obtained data on the influence of natural fracture strength, mineral composition ratio, and geostress difference on main fracture extension, fracture network complexity, and weak surface activation probability, potential influencing factors directly related to fault stress disturbance were preliminarily screened for further simulation analysis. The single-factor variable method clearly reflects the independent influence of a single factor on the fault activation process, while the orthogonal experimental method effectively analyzes the interaction between multiple factors. Combining these two methods improves the accuracy and reliability of the main control factor identification results. First, the single-factor variable method was used to change several factors that might affect fault activation, such as fault dip angle, distance between the fault and the wellbore, geostress difference, natural fracture strength, mineral composition ratio, and bedding development degree, while keeping all other parameters constant. The corresponding hydraulic fracturing-fault-wellbore coupled FDEM numerical models were run sequentially, and fault slip data and wellbore deformation data were extracted from each set of simulation results to analyze the degree of influence of changes in single factors on fault slip and wellbore deformation. Typical single-factor quantitative response patterns are as follows: the peak fault displacement reaches 0.08 mm at a fault dip angle of 15°, and is lowest (<0.02 mm) at 45°; for example... Figure 8 As shown, the horizontal axis of all sub-plots represents the fault path length, and the vertical axis represents the fault displacement. Different colored curves represent different dip angles. The fracturing time for sub-plot a is 1000 s, for sub-plot b it is 2000 s, and for sub-plot c it is 3000 s. Low-dip faults (15°) are more prone to overall slippage, with the highest peak displacement; 45° faults have the lowest displacement and the best stability. Fault displacement gradually increases with fracturing time, and the spatial distribution shows a high center and low ends. The local slippage peak is largest (>0.15 mm) when the distance between the fault and the wellbore is 50 m; the fault center displacement reaches 3.2 mm when the geostress difference is 15 MPa, and it reaches a stable state within the first 1000 s of fracturing. Figure 9As shown, the horizontal axis represents the fault path length, and the vertical axis represents the fault displacement. Different colored curves represent different geostress differences. The fracturing time for subplot a is 1000s, for subplot b it is 2000s, and for subplot c it is 3000s. The fault displacement increases monotonically with the increase of geostress difference, reaching 3.2mm under a geostress difference of 15MPa. Furthermore, the fault activation exhibits instantaneous response characteristics, reaching a stable state within 1000s in the initial stage of fracturing.

[0089] Based on single-factor analysis, an orthogonal experimental design was employed to simulate multi-factor combinations of working conditions. Three factors with significant influence from the single-factor analysis—fault dip angle, fault-to-wellbore distance, and geostress difference—were selected as the factors for the orthogonal experiment. Each factor had four different levels, with level values ​​covering common geological conditions in the target area, constructing a 3-factor, 4-level orthogonal experimental table. Numerical simulations were run sequentially according to the parameter combinations in the orthogonal experimental table, and the results of each simulation were extracted. Range and variance analyses were used to calculate the influence weight of each factor on fault slip and wellbore deformation, clarifying the magnitude of interactions between different factors. Ultimately, fault dip angle, fault-to-wellbore distance, and geostress difference were identified as the three main controlling factors for fault activation. This identification process was based on the correlation between fracture propagation and weak surface activation, comparing and screening to ensure that the identified main controlling factors better reflect the actual stress transfer characteristics during secondary fracturing.

[0090] Fault dip angle is the primary factor influencing fault activation risk, and faults with different dip angles exhibit significant differences in their mechanical responses under secondary fracturing stress disturbances. When the fault dip angle is small, the shear stress component at the fault interface is small, making fault slippage unlikely. As the fault dip angle increases, the shear stress component at the fault interface gradually increases, and the probability and amount of fault slippage also increase accordingly. When the fault dip angle exceeds a certain critical value, the normal stress component at the fault interface gradually becomes dominant, increasing the frictional force at the fault interface, making fault slippage more difficult, while the amount of slippage gradually decreases. Simultaneously, the fault dip angle also affects the intersection angle between the secondary fracturing fracture and the fault, thus affecting the stress transfer efficiency to the fault during fracture propagation. This corroborates the previously obtained finding that the fracture propagation direction is controlled by the geostress difference.

[0091] The distance between the fault and the wellbore is the second most important factor influencing fault activation risk, determining the extent to which stress disturbances generated by secondary fracturing affect the fault. The closer the fault is to the wellbore, the greater the intensity of the stress disturbances transmitted to the fault, making the fault more prone to activation, and resulting in greater fault slip and corresponding wellbore deformation. As the distance between the fault and the wellbore increases, the stress disturbances gradually attenuate in the reservoir, the impact on the fault gradually decreases, and the fault slip and wellbore deformation decrease exponentially. When the distance between the fault and the wellbore exceeds a certain critical value, the stress disturbances generated by secondary fracturing can no longer cause fault slip, and the risk of fault activation becomes negligible.

[0092] The stress difference is the third most important factor influencing fault activation risk. It not only controls the direction of fracture propagation and the complexity of the fracture network, but also determines the initial stress state of the fault. A larger stress difference leads to more pronounced stress anisotropy within the reservoir, making fractures more likely to extend along the direction of maximum principal stress, resulting in stronger directional fracture propagation and a more concentrated range of stress disturbance. Simultaneously, a larger stress difference places the fault in a higher initial stress state, bringing it closer to the critical slip state, making it more susceptible to activation after secondary fracturing stress disturbances. Conversely, a smaller stress difference results in poorer directional fracture propagation, a more complex fracture network, a wider but less intense range of stress disturbances, and a relatively lower probability of fault activation.

[0093] Quantitative analysis was conducted to examine the nonlinear response patterns of various controlling factors to fault slip and wellbore deformation, yielding correlation curves between different controlling factors and fault slip and wellbore deformation. Based on these curves, predicted values ​​of fault slip and wellbore deformation under arbitrary parameter combinations can be calculated, providing a quantitative basis for constructing a multi-dimensional quantitative risk grading and evaluation model.

[0094] Based on the nonlinear response patterns of various controlling factors, a basic computational framework for a multi-dimensional quantitative risk grading and evaluation model is established. In this model, a single-factor risk scoring function is established for each evaluation indicator. This function is determined based on the correlation curves between each indicator and fault slip and wellbore deformation. The actual value of each indicator is mapped to a risk score between 0 and 1, with a higher score indicating a higher risk. For indicators with critical values, such as fault dip angle, a piecewise function is used to establish the risk scoring function. Within the favorable dip angle range for fault activation, the risk score first increases and then decreases with the dip angle. For indicators exhibiting monotonic trends, such as the distance between the fault and the wellbore and the difference in ground stress, a continuous function is used to establish the risk scoring function. Combining the influence weights of each factor obtained from previous orthogonal experiments, a weighted summation method is used to construct a comprehensive risk score calculation formula.

[0095]

[0096] In the formula, For comprehensive risk scoring, , , , The risk scores are calculated based on single factors: fault dip angle, distance between the fault and the wellbore, geostress difference, and mineral heterogeneity. Field fault activation case data from drilled wells in the target area were collected. The comprehensive risk score calculation formula in the multi-dimensional quantitative risk grading evaluation model was calibrated, and the weight coefficients of each indicator and the parameters of the single-factor risk scoring function were adjusted to ensure that the comprehensive risk score calculated by the model matches the actual fault activation situation in the field.

[0097] S6. Input the basic data of the target well site, and calculate the fault activation risk level of the target area in the target well site through the multi-dimensional quantitative risk classification evaluation model.

[0098] In some implementations, the evaluation indicators of the multi-dimensional quantitative risk classification assessment model include fault dip angle, distance between the fault and the wellbore, geostress difference, and mineral heterogeneity.

[0099] The multi-dimensional quantitative risk classification and evaluation model divides the fault activation risk level into three levels: high risk, medium risk, and low risk, which correspond to different fault slip thresholds, wellbore deformation thresholds, and fracture propagation range thresholds, respectively.

[0100] Based on the previously obtained nonlinear response patterns of various controlling factors to fault slip and wellbore deformation, a multi-dimensional quantitative risk grading and evaluation model is constructed. This model comprehensively considers the combined effects of multiple influencing factors, quantitatively evaluating the fault activation risk in the target area and avoiding biases caused by evaluations based on a single factor.

[0101] The evaluation indicators of the multi-dimensional quantitative risk classification assessment model include fault dip angle, distance between the fault and the wellbore, geostress difference, and mineral heterogeneity. Among these, fault dip angle, distance between the fault and the wellbore, and geostress difference are the three main controlling factors of fault activation, playing a decisive role in the risk of fault activation. Mineral heterogeneity indirectly affects the risk of fault activation by influencing the fracture propagation morphology and fracture network complexity, thereby affecting the range and intensity of stress disturbance within the reservoir. Therefore, including it in the evaluation indicator system can further improve the accuracy of the evaluation results. All evaluation indicators adopt a unified quantitative calculation method: the fault dip angle is the measured angle value obtained from on-site geological exploration; the distance between the fault and the wellbore is the calculated value of the shortest three-dimensional spatial distance between the fault and the target wellbore; the geostress difference is the difference between the maximum and minimum horizontal principal stresses in the target area; the mineral heterogeneity is quantified by the total proportion of brittle minerals in the target reservoir. Brittle minerals include quartz, calcite, dolomite, etc. The higher the proportion of brittle minerals, the stronger the brittleness of the reservoir rock, the easier it is for fractures to propagate, and the larger the range of stress disturbance influence.

[0102] The weight of each evaluation indicator was determined based on its impact on the risk of fault activation. The weights were determined using variance analysis results from previous orthogonal experiments; the greater the impact, the higher the corresponding weight. The weights of the four evaluation indicators, from largest to smallest, are: fault dip angle, distance between the fault and the wellbore, stress difference, and mineral heterogeneity. The sum of the weights of all indicators is 1.

[0103] The multi-dimensional quantitative risk grading assessment model classifies fault activation risk levels into three levels: high risk, medium risk, and low risk, corresponding to different fault slip thresholds, wellbore deformation thresholds, and fracture propagation range thresholds, respectively. The thresholds for each risk level are determined based on extensive numerical simulation results and field engineering experience: For the high-risk level, the fault slip threshold is >0.1 mm (the critical slip that can cause irreversible deformation of the wellbore), the wellbore deformation threshold is >3 mm (the maximum allowable deformation of the casing), and the fracture propagation range threshold is the critical propagation distance at which the fracture can reach the fault location; for the medium-risk level, the fault slip threshold is 0.03–0.1 mm (slip that will not cause irreversible deformation of the wellbore but will produce significant stress concentration), the wellbore deformation threshold is 1–3 mm, and the fracture propagation range threshold is the propagation distance at which the fracture approaches the fault location; for the low-risk level, the fault slip is <0.03 mm, the wellbore deformation is <1 mm, and the fracture propagation range cannot reach the fault location.

[0104] Based on the thresholds corresponding to each level of risk, the interval division standard for the comprehensive risk score is determined: low risk corresponds to 0 ≤ comprehensive risk score < 0.3, medium risk corresponds to 0.3 ≤ comprehensive risk score < 0.7, and high risk corresponds to 0.7 ≤ comprehensive risk score ≤ 1.0. The quantitative values ​​of each evaluation indicator are normalized to eliminate the influence of dimensional differences on the scoring results. Then, the normalized indicator values ​​are multiplied by their corresponding weights and summed to obtain the comprehensive risk score for the target area. The higher the comprehensive risk score, the higher the fault activation risk of the target area. Based on the interval of the comprehensive risk score, the fault activation risk level corresponding to the target area can be determined.

[0105] Inputting the basic data of the target well site, the fault activation risk level of the target area within the target well site is calculated using a multi-dimensional quantitative risk grading evaluation model. From the preprocessed multi-source basic data, the fault dip angle, distance between the fault and the wellbore, geostress difference, and brittle mineral percentage data corresponding to the target area within the target well site are extracted. These data are then substituted into the multi-dimensional quantitative risk grading evaluation model, sequentially completing index quantification, normalization, weight calculation, and comprehensive score calculation to finally obtain the comprehensive risk score for the target area. Based on the preset comprehensive risk score interval division standard, the fault activation risk level corresponding to the target area is matched.

[0106] If the target well site comprises multiple areas with significantly different geological conditions, it needs to be divided into multiple independent evaluation units. A 50m × 50m grid size is recommended for each evaluation unit. A fault activation risk assessment should be performed on each unit to obtain its corresponding risk level. This zonal evaluation allows for more accurate identification of high-risk areas within the target well site.

[0107] S7. Based on the fault activation risk level of the target area, match the preset targeted prevention and control strategy library to generate corresponding fracturing parameter optimization schemes and construction management schemes.

[0108] In some implementations, the pre-defined targeted prevention and control strategy library includes prevention and control strategies for high-risk areas, such as calculating the minimum safe distance between well location and perforation section, setting upper limit thresholds for injection flow rate and pump pressure, and methods for controlling the intensity of ground stress disturbance.

[0109] In some implementations, the pre-defined targeted prevention and control strategy library includes prevention and control strategies for medium-risk areas, such as fracturing parameter adjustment methods based on real-time monitoring data and early warning threshold setting and early warning triggering methods for microseismic monitoring.

[0110] In some implementations, the pre-defined targeted prevention and control strategy library specifies that the prevention and control strategy for low-risk areas is conventional fracturing operations and conventional monitoring.

[0111] The fracturing parameter optimization scheme is a scheme that optimizes multiple parameters such as perforation parameters, injection flow rate, pump pressure, and construction sequence.

[0112] The pre-defined targeted prevention and control strategy library is formulated based on the fault activation characteristics and hazard levels of different risk levels. It can provide differentiated prevention and control measures for different risk levels, and minimize the risk of fault activation while ensuring the fracturing operation effect.

[0113] High-risk areas have a high probability of fault activation. Once activation occurs, it can lead to irreversible wellbore deformation and casing damage, causing serious economic losses and safety hazards. Therefore, the strictest prevention and control measures are required. Prevention and control strategies for high-risk areas include calculating the minimum safe distance between the well location and perforated section, setting upper limit thresholds for injection rate and pump pressure, and methods for controlling the intensity of geostress disturbance. The minimum safe distance between the well location and perforated section is determined based on the influence of the distance between the fault and the wellbore on the fault slip, obtained from previous numerical simulations. The minimum safe distance should be greater than 70m, ensuring that the stress disturbance generated by secondary fracturing is below the critical activation stress of the fault when it reaches the fault location. The upper limit thresholds for injection rate and pump pressure are calculated based on the initial stress state and mechanical parameters of the fault. The upper limit for single-stage injection rate is 0.00008m. 3 / s (cubic meters per second), with a single-stage pump pressure limit of 60MPa, to avoid excessive injection pressure causing shear stress at the fault interface to exceed its shear strength. The intensity of in-situ stress disturbance is controlled by limiting the injection volume and scale of single-stage fracturing, with a maximum single-stage injection volume of 200m³. A segmented, small-scale fracturing approach is adopted to avoid localized stress concentrations that could induce fault activation. Simultaneously, high-risk areas need to avoid the favorable activation dip angle range of the fault. For areas that cannot be avoided, the perforation length needs to be further reduced to decrease the impact range of single-stage fracturing.

[0114] The probability of fault activation in medium-risk areas is low, but activation can still occur under unfavorable geological and construction conditions. Therefore, a combination of dynamic control and real-time early warning is required. The control strategies for medium-risk areas include fracturing parameter adjustment methods based on real-time monitoring data, and the setting and triggering of early warning thresholds for microseismic monitoring. Real-time monitoring data includes wellhead pressure, injection rate, casing pressure, and microseismic event data. Based on the real-time monitored fracture propagation and stress disturbance, fracturing parameters are dynamically adjusted to prevent fractures from extending towards the fault. The early warning thresholds for microseismic monitoring are determined based on the correlation between fracture propagation range and fault activation obtained from previous numerical simulations. Two levels of early warning are set: threshold 1 is when a microseismic event is less than 20m from the fault, and threshold 2 is when the number of microseismic events exceeds 10 within 10 minutes. When either level of early warning is triggered, construction parameters are adjusted promptly or construction is suspended until the risk is eliminated before resuming construction.

[0115] The risk of fault activation in low-risk areas is extremely low. Stress disturbances generated by secondary fracturing cannot cause fault slippage and will not affect the safety of fracturing operations. Therefore, conventional fracturing operations and monitoring are sufficient. The focus of fracturing operations in low-risk areas is to improve the fracturing effect, creating as complex a network of fractures as possible to increase reservoir activation and oil and gas production. Conventional monitoring mainly includes monitoring basic operational parameters such as wellhead pressure and injection rate, ensuring the normal progress of the fracturing process.

[0116] The fracturing parameter optimization scheme involves multi-parameter optimization of perforation parameters, injection rate, pump pressure, and construction sequence. During the optimization process, the influence of previously obtained data on natural fracture strength, mineral composition ratio, and geostress difference on the main fracture extension direction, fracture network complexity, and weak surface activation probability is utilized. For areas with low natural fracture strength, the injection rate and pump pressure are appropriately reduced to prolong the fluid seepage time within the reservoir, promoting the interaction between hydraulic fractures and natural fractures, thereby activating more natural fractures and forming a complex fracture network structure. For areas with high natural fracture strength, the injection rate and pump pressure are appropriately increased to enhance fracture extension and penetration capabilities, thereby increasing the fracture network coverage and stimulation volume. For areas with a brittle mineral content greater than 80%, a segmented multi-cluster perforation method is adopted, with a cluster spacing of 15–20 m, increasing the number of fracture initiation points, inducing multiple fractures to extend simultaneously, and increasing the complexity of the fracture network. For areas with a high proportion of ductile minerals, the perforation density and injection rate of a single cluster perforation are appropriately increased to enhance fracture propagation dynamics and overcome the fracture propagation resistance caused by rock plastic deformation. For areas with a stress difference greater than 10 MPa, the perforation direction is optimized to ensure that the angle between the perforation direction and the direction of the maximum principal stress is less than 15°, reducing deflection of fracture propagation and improving fracture extension efficiency. For areas with a smaller stress difference, staggered perforation is used to induce fracture deflection and bifurcation, forming a more complex fracture network structure. For areas with well-developed natural fractures, the injection rate is reduced by 20%–30%, and the construction time is extended to promote full fluid infiltration and full activation of natural fractures.

[0117] Meanwhile, fracturing parameters are constrained according to the risk level of different areas. In high-risk areas, fracturing parameters do not exceed preset upper thresholds, and all parameter adjustments prioritize reducing the risk of fault activation. In medium-risk areas, fracturing parameters are dynamically adjusted based on real-time monitoring data to improve fracturing effectiveness while ensuring safety. In low-risk areas, fracturing parameters are optimized to maximize fracture network complexity and stimulation volume, without considering the constraint of fault activation risk. Construction control plans are developed based on the prevention and control requirements of different risk levels, including operating procedures for construction personnel, equipment operation requirements, monitoring equipment layout requirements, and emergency response procedures. Through differentiated prevention and control strategies and targeted optimization of fracturing parameters, the risk of fault activation caused by fracturing in layered shale reservoirs can be effectively reduced while ensuring the stimulation effect of fracturing operations, thereby improving reservoir development efficiency and economic benefits.

[0118] In some implementations, after S7, the method further includes:

[0119] Establish an update interface between real-time monitoring data and the coupled FDEM numerical model of fracturing-fault-wellbore.

[0120] This updated interface enables real-time monitoring data and numerical models to be synchronized during on-site construction, allowing the numerical models to be corrected in real time according to the actual on-site conditions. This solves the problem of discrepancies between the pre-construction evaluation and the actual on-site construction process, and further improves the accuracy and effectiveness of fault activation risk prevention and control.

[0121] The update interface can receive real-time data transmitted from various on-site monitoring devices, including wellhead pressure, injection rate, casing pressure, microseismic event data, and wellbore deformation monitoring data. The interface performs preliminary format conversion and outlier removal on the received real-time data, and then synchronizes the processed data to the fracturing-fault-wellbore coupled FDEM numerical model in real time.

[0122] When there is a significant deviation between the real-time monitoring data and the predicted data from the numerical simulation before construction, the model update process is triggered. During the model update process, the reservoir mechanical parameters, fracture propagation parameters, fault mechanical parameters, etc. in the model are corrected using real-time monitoring data, so that the corrected model can more accurately reflect the actual construction status and geological conditions on site.

[0123] After the model is updated, the fault activation risk level under the current construction condition is recalculated. Based on the updated risk level, the corresponding fracturing parameter optimization scheme and construction control scheme are adjusted. If the updated risk level increases, the injection displacement and pump pressure are reduced, the injection fluid volume per fracturing stage is decreased, and the early warning level of microseismic monitoring is increased. If the updated risk level decreases, the fracturing parameters can be appropriately adjusted within a safe range to improve the fracturing stimulation effect.

[0124] The updated fracturing parameter optimization scheme and construction management scheme will be output to the construction control terminal in real time to guide on-site construction personnel to make corresponding operational adjustments. In this way, various uncertainties that arise during construction can be addressed in a timely manner, the risk of fault activation can be effectively managed throughout the process, and the safety and smooth progress of fracturing operations can be guaranteed.

[0125] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application.

Claims

1. A method for risk classification and control of activation of fractured faults in layered shale reservoirs, characterized in that, Includes the following steps: S1. Collect multi-source basic data of bedding shale reservoirs and perform standardized preprocessing; S2. Based on the preprocessed multi-source basic data, a fluid-structure coupled FDEM hydraulic fracturing numerical model considering multi-factor coupling is constructed; the fluid-structure coupled FDEM hydraulic fracturing numerical model is a numerical model that simultaneously couples bedding weak surface parameters, mineral heterogeneity parameters and natural fracture discrete fracture network parameters. S3. Based on the fluid-structure interaction FDEM hydraulic fracturing numerical model, fault parameters and wellbore parameters are embedded to construct a fracturing-fault-wellbore coupled FDEM numerical model, and the fracturing-fault-wellbore coupled FDEM numerical model is verified. S4. Run the fracturing-fault-wellbore coupled FDEM numerical model, perform multi-condition numerical simulation, and obtain fracture propagation data, fault slip data and wellbore deformation data under different conditions; S5. Based on the fracture propagation data, the fault slip data, and the wellbore deformation data, identify the main controlling factors of fault activation and construct a multi-dimensional quantitative risk classification evaluation model; the main controlling factors of fault activation include fault dip angle, distance between the fault and the wellbore, and geostress difference; after identifying the main controlling factors of fault activation, quantitatively analyze the nonlinear response law of each main controlling factor to the fault slip and wellbore deformation, and the nonlinear response law is used to construct the multi-dimensional quantitative risk classification evaluation model; S6. Input the basic data of the target well site, and calculate the fault activation risk level of the target area in the target well site through the multi-dimensional quantitative risk classification and evaluation model. S7. Based on the fault activation risk level of the target area, match the preset targeted prevention and control strategy library to generate corresponding fracturing parameter optimization schemes and construction management schemes; Specifically, the construction of a hydraulic fracturing-fault-wellbore coupled FDEM numerical model by embedding fault parameters and wellbore parameters on the basis of the fluid-structure interaction FDEM hydraulic fracturing numerical model includes: From the preprocessed multi-source basic data, the fault parameters, wellbore parameters, and initial fracturing damage zone parameters are extracted, wherein the fault parameters include fault interface mechanical parameters, and the wellbore parameters include wellbore geometric parameters and wellbore mechanical parameters; the fault parameters, wellbore parameters, and initial fracturing damage zone parameters are embedded into the grid cells corresponding to the fluid-structure interaction FDEM hydraulic fracturing numerical model; The embedded parameters are assigned values ​​according to the geological zoning of the target area, and the parameters after zoning are corrected based on the results of indoor rock mechanics tests. Generate the fracturing-fault-wellbore coupled FDEM numerical model; The verification process of the fracturing-fault-wellbore coupled FDEM numerical model was carried out by combining comparison with KGD theoretical model, comparison with indoor physical test data, and comparison with field microseismic monitoring data.

2. The method according to claim 1, characterized in that, S4 specifically includes: Multiple sets of different simulation conditions were set up, each corresponding to different natural crack strengths, different mineral component ratios, and different geostress differences. Run the coupled FDEM numerical model of fracturing-fault-wellbore corresponding to each set of working conditions in sequence; Extract the corresponding crack propagation data, fault slip data, and wellbore deformation data from the simulation results of each working condition; Based on the fracture propagation data, fault slip data, and wellbore deformation data, the influence of natural fracture strength, mineral composition ratio, and geostress difference on the main fracture extension direction, fracture network complexity, and weak surface activation probability is analyzed. The influence rules are used to identify the main controlling factors of fault activation and optimize fracturing parameters.

3. The method according to claim 2, characterized in that, The evaluation indicators of the multi-dimensional quantitative risk classification evaluation model include the fault dip angle, the distance between the fault and the wellbore, the geostress difference, and the mineral heterogeneity. The multi-dimensional quantitative risk classification and evaluation model divides the fault activation risk level into three levels: high risk, medium risk, and low risk, which correspond to different fault slip thresholds, wellbore deformation thresholds, and fracture propagation range thresholds, respectively.

4. The method according to claim 3, characterized in that, The preset targeted prevention and control strategy library includes prevention and control strategies for high-risk areas, such as the calculation of the minimum safe distance between well location and perforation section, the setting of upper limit thresholds for injection flow rate and pump pressure, and the control method for the intensity of ground stress disturbance.

5. The method according to claim 3, characterized in that, The preset targeted prevention and control strategy library includes prevention and control strategies for medium-risk areas, such as fracturing parameter adjustment methods based on real-time monitoring data and early warning threshold setting and early warning triggering methods for microseismic monitoring.

6. The method according to claim 3, characterized in that, In the preset targeted prevention and control strategy library, the prevention and control strategy corresponding to the low-risk area is conventional fracturing construction and conventional monitoring; The fracturing parameter optimization scheme is a scheme that optimizes multiple parameters, including perforation parameters, injection flow rate, pump pressure, and construction sequence.

7. The method according to claim 1, characterized in that, Following S7, the method further includes: Establish an update interface between real-time monitoring data and the coupled FDEM numerical model of fracturing-fault-wellbore.

Images