A strike-slip fault-controlled effective fracture evaluation method based on a tectonic stress field simulation

Abstract

The application discloses a strike-slip fault and fracture effective evaluation method based on structural stress field simulation, which comprises the following steps: determining a target layer and a fault and fracture distribution feature of a research area, determining a boundary condition of structural stress field simulation, constructing a reasonable structural model and a mathematical model, carrying out structural stress field simulation, calculating geological and engineering parameters for representing a fault and fracture system according to a simulation result, assigning weights by using an analytic hierarchy process, constructing a fault and fracture system effectiveness evaluation parameter by comprehensively using multiple parameters, calibrating the fault and fracture system effectiveness evaluation parameter, establishing a fault and fracture system effectiveness evaluation standard, and finally accurately determining a spatial distribution area of effective fractures in the fault and fracture system. The method is based on a rock rupture criterion and uses a ground stress as an applied condition to predict a development and distribution rule of effective fractures in a reservoir, and provides a set of effective schemes for professional and technical personnel who research distribution features of a fault and fracture system in a strike-slip fault zone and well site deployment.

Description

Technical Field

[0001] This application relates to the field of oil and gas geological exploration and development technology, and in particular to a method for evaluating effective fractures controlled by strike-slip faults based on tectonic stress field simulation. Background Technology

[0002] With the deepening of oil and gas exploration and development in China and the continuous improvement of technology, the utilization of deep and ultra-deep carbonate oil and gas reservoirs has become a crucial link in ensuring energy security and promoting economic development. Currently, research on deep and ultra-deep carbonate reservoirs is gradually shifting from focusing on static reservoir properties such as lithology, lithofacies, porosity, and permeability—traditional oil and gas geology studies—to emphasizing dynamic factors caused by geostress. This shift helps reveal the dynamic behavior of subsurface rocks under current geological conditions, including stress patterns, deformation states, and fracture behavior. It also facilitates the identification of high-quality fault-fracture reservoir spatial distribution areas and the prediction of fault-fracture system effectiveness in tight carbonate reservoirs under the influence of tectonic fracturing. Currently, the analysis of geostress in oilfield exploration and development mainly utilizes numerical simulation of tectonic stress fields. This method reconstructs the underground stress state to reveal the geostress distribution in a specific study area. By simulating the geostress field environment during a specific geological period, the distribution of geostress and strain energy within the rock mass of this area are calculated. Based on this, the spatial development and distribution patterns of fractures in the reservoir are predicted. Then, the effectiveness of fractures is reasonably calculated according to the geostress state, thus providing strong support for the assessment of the scale of high-quality strike-slip fault-fracture reservoirs and the optimization of well location deployment schemes during oil and gas exploration and development.

[0003] Currently, research on fracture effectiveness mainly utilizes five methods: core analysis, outcrop analysis, well logging, production dynamics data, and stress analysis. Fracture effectiveness evaluation based on core observation and field outcrops, supplemented by experimental methods, offers highly intuitive and accurate parameters. However, these methods, being detached from the subsurface environment, result in stress release, leading to measured results that exceed actual conditions. While well logging methods (conventional logging, imaging logging) preserve the subsurface state of fractures and offer high accuracy, they are prone to bias and limited practical guidance for predicting subsurface fracture effectiveness. Fracture effectiveness evaluation parameters obtained from a fracture network system near the wellbore using production dynamics data (well tests, historical production data) are highly accurate and provide significant guidance. However, this method can only monitor or measure fracture effectiveness in existing wells and cannot predict fracture effectiveness in un-well-deployed areas. Summary of the Invention

[0004] This application provides a method for evaluating effective fractures controlled by strike-slip faults based on tectonic stress field simulation, aiming to solve the problem that existing technologies are unable to predict the effectiveness of fractures in areas without wells.

[0005] A method for evaluating effective strike-slip fractures based on tectonic stress field simulation, the method comprising:

[0006] S1: Obtain the target stratigraphic level and fault-fracture distribution characteristics of the study area, clarify the simulation boundary conditions of the tectonic stress field, construct a reasonable tectonic model and mathematical model based on the simulation boundary conditions, carry out tectonic stress field simulation, and obtain simulation results;

[0007] S2: Based on the simulation results, obtain the geological parameters and engineering parameters used to characterize the fracture-fracture system, assign weights to the geological parameters and engineering parameters using the analytic hierarchy process, and construct the effectiveness evaluation parameters of the fracture-fracture system through multi-parameter synthesis;

[0008] S3: Calibrate the effectiveness evaluation parameters of the fracture-crack system, establish the effectiveness evaluation criteria of the fracture-crack system, and determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0009] Optionally, in the above scheme, S1 includes:

[0010] S11: Determine the scope of the study area, the distribution of well logging data, the target strata and the characteristics of fault-fracture distribution, extract the target strata and fault-fracture data, and perform data standardization processing;

[0011] S12: Define the boundary conditions for simulating the stress field, calculate the stress magnitude, analyze the stress direction, and set the range of the protective layer;

[0012] S13: Construct the structural model using the fracture-seam data of the target layer, and mesh it to build the mathematical model;

[0013] S14: Based on the stress magnitude and stress direction, construct a stress field simulation for the mathematical model.

[0014] Optionally, in the above scheme, in step S11,

[0015] The study area is defined, and based on the actual distribution of the study area, the distribution of well locations and logging data of each well location within the study area is investigated to determine whether there is conventional or special logging data that can be used to calculate the magnitude and direction of geostress.

[0016] When extracting the target layer horizon and fault-fault data, depth data must be extracted for calculating the magnitude of the vertical principal stress. After determining the target layer horizon and fault-fault distribution characteristics of the study area, the extracted target layer horizon and fault-fault data are normalized. Specifically, the normalization process includes two aspects: correcting outliers and supplementing blank values. Outliers in the target layer horizon data are abnormally high or low depth values. Outliers in the fault-fault data are abnormal offset values ​​of fault-fault coordinates and abnormally high or low depth values. Correcting outliers and supplementing blank values ​​involves calculating the difference between the coordinate data and depth data of four points around the outlier data point. The calculation result replaces the outlier point or supplements the blank value point. If the outlier point or blank value point is located at the boundary of the target layer and there are only three or two points around it, the difference is calculated based on the coordinate data and depth data of the three or two points. The calculation result replaces the outlier point or supplements the blank value point.

[0017] In the above scheme, optionally, in S12, the determination of the magnitude of the geostress in the study area is carried out in three aspects: calculating the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical principal stress.

[0018] If the study area contains conventional or special logging data, the calculation of the maximum and minimum horizontal principal stresses is mainly completed using the single-well geostress calculation formula.

[0019] If the study area does not contain conventional or special logging, the stress magnitude obtained from previous studies in the tectonic units outside the study area will be used as the geostress magnitude value of the study area through a pre-set literature survey.

[0020] The vertical principal stress is calculated based on the depth value of each data point in the stratigraphic information;

[0021] After calculating the maximum and minimum horizontal principal stresses for multiple wells in the study area, the maximum and minimum horizontal principal stresses at the same coordinate points of the target strata are determined based on the well trajectory coordinates, serving as the basis for applying stress to simulate the tectonic stress field.

[0022] The application of vertical principal stress in the study area is calculated based on the layer depth data. During the calculation process, the density data at different coordinate points are assigned values ​​according to the simulation requirements.

[0023] The direction of geostress in the study area is determined by accurately measuring the ancient and modern stress directions through experimental means, or by analyzing the current geostress direction using the wellbore collapse method of special logging.

[0024] In the above scheme, optionally, in step S13, the construction model is constructed based on stratigraphic information and fault-seam distribution data. When building the mathematical model, the fault-seam distribution area is distinguished from the original strata in the target layer by refining the grid.

[0025] Optionally, in the above scheme, S2 includes:

[0026] S21: Data format processing and standardization of structural stress field simulation results;

[0027] S22: Geological parameters used to characterize fracture-fracture systems include: expansion coefficient, sliding stability coefficient, and penetration index; engineering parameters used to characterize fracture-fracture systems include: horizontal stress difference and formation fracture pressure.

[0028] S23: Perform normalized data processing on the geological parameters and the engineering parameters respectively;

[0029] S24: Using the analytic hierarchy process (AHP), determine the weight values ​​of the geological parameters and the engineering parameters, and construct multi-parameter comprehensive evaluation parameters for the effectiveness of the fracture-joint system.

[0030] In the above scheme, optionally, in step S21, the calculation of the characterization parameters of the fracture-seam system is based on the node data in the structural stress field simulation. After the simulation is completed, the node data output by the simulation is formatted, and the node number, blank cells, blank lines and a large number of header lines of the node data are deleted. Then, the standardization process is performed, and the nodes where the outliers and zero values ​​of each type of parameter in the node data are located are deleted.

[0031] In step S22, the processed stress field simulation node data is used to obtain geological parameters such as expansion coefficient, sliding stability coefficient, and fracture penetration capacity coefficient, which characterize the effectiveness of the fracture-fracture system, as well as engineering parameters such as horizontal stress difference and formation fracture pressure, which characterize the effectiveness of the fracture-fracture system.

[0032] Optionally, in the above scheme, in step S23, the geological parameters such as expansion coefficient, sliding stability coefficient, fracture penetration capacity coefficient, and the engineering parameters used to characterize the effectiveness of the fracture-fracture system, horizontal stress difference and formation fracture pressure, are normalized.

[0033] In step S24, the weight values ​​of each parameter are determined using the analytic hierarchy process (AHP), and then the effectiveness evaluation parameters of the fracture-seam system are constructed by combining multiple parameters based on the magnitude of the weight values.

[0034] Optionally, in the above scheme, S3 includes:

[0035] S31: Compare the effectiveness evaluation parameters of the fracture-fracture system with the effectiveness parameters of a single well, calibrate the distribution range of the effectiveness data of the fracture-fracture system, and establish the effectiveness evaluation standard of the fracture-fracture system;

[0036] S32: Based on the established evaluation criteria for the effectiveness of the fracture-crack system, accurately determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0037] In the above scheme, optionally, in step S31, the effectiveness evaluation parameters of the fracture-fracture system are calibrated based on the actual single-well fracture effectiveness parameters, and the evaluation range of the effectiveness evaluation parameters of the fracture-fracture system is determined; when comparing using single-well fracture effectiveness parameters, the effectiveness evaluation parameters of the fracture-fracture system are calibrated using the fracture effectiveness parameters from imaging logging and production dynamic data, and after calibration, the effectiveness evaluation criteria of the fracture-fracture system are defined;

[0038] In step S32, after establishing the evaluation criteria for the effectiveness of the fracture-crack system, the spatial distribution area of ​​effective cracks in the fracture-crack system of the study area is determined through the evaluation criteria for the effectiveness of the fracture-crack system.

[0039] Compared with the prior art, this application has at least the following beneficial effects:

[0040] This application, based on further analysis and research of existing technical problems, recognizes the difficulty of predicting fracture effectiveness in areas without well placement. By determining the target stratigraphic level and fracture distribution characteristics of the study area, clarifying the boundary conditions for tectonic stress field simulation, constructing a reasonable structural and mathematical model, and conducting tectonic stress field simulation, this application determines the geological and engineering parameters characterizing the fracture-fracture system based on the simulation results. Then, using the analytic hierarchy process (AHP), weights are assigned, and multiple parameters are comprehensively constructed to evaluate the effectiveness of the fracture-fracture system. By calibrating these parameters, an evaluation standard for the effectiveness of the fracture-fracture system is established, and finally, a method for accurately determining the spatial distribution area of ​​effective fractures within the fracture system is developed. This method, based on rock fracture criteria and using geostress as the applied condition, predicts the development and distribution patterns of effective fractures in reservoirs, providing an effective solution for professionals studying the effective distribution characteristics of fracture-fracture systems in strike-slip fault zones and well placement. Attached Figure Description

[0041] Figure 1 A flowchart illustrating the effectiveness evaluation method for a strike-slip fracture fracture-crack system provided in one embodiment of this application;

[0042] Figure 2 A planar diagram of the expansion coefficient in geological parameters provided in one embodiment of this application;

[0043] Figure 3 A planar diagram of sliding stability coefficients in geological parameters provided in one embodiment of this application;

[0044] Figure 4 A plan view of the penetration index in geological parameters provided in one embodiment of this application;

[0045] Figure 5 A planar diagram of horizontal stress difference in engineering parameters provided in one embodiment of this application;

[0046] Figure 6 A formation fracture pressure plan view in engineering parameters provided in one embodiment of this application;

[0047] Figure 7 A plan view of the effectiveness evaluation parameters of a fracture-seam system provided in one embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] In one embodiment, such as Figure 1 As shown, a method for evaluating effective strike-slip fractures based on tectonic stress field simulation is provided, including the following steps:

[0050] S1: Obtain the target stratigraphic level and fault-fracture distribution characteristics of the study area, clarify the simulation boundary conditions of the tectonic stress field, construct a reasonable tectonic model and mathematical model based on the simulation boundary conditions, carry out tectonic stress field simulation, and obtain simulation results;

[0051] S2: Based on the simulation results, obtain the geological parameters and engineering parameters used to characterize the fracture-fracture system, assign weights to the geological parameters and engineering parameters using the analytic hierarchy process, and construct the effectiveness evaluation parameters of the fracture-fracture system through multi-parameter synthesis;

[0052] S3: Calibrate the effectiveness evaluation parameters of the fracture-crack system, establish the effectiveness evaluation criteria of the fracture-crack system, and determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0053] In this embodiment, S1 includes:

[0054] S11: Determine the scope of the study area, the distribution of well logging data, the target strata and the characteristics of fault-fracture distribution, extract the target strata and fault-fracture data, and perform data standardization processing;

[0055] S12: Define the boundary conditions for simulating the stress field, calculate the stress magnitude, analyze the stress direction, and set the range of the protective layer;

[0056] S13: Construct the structural model using the fracture-seam data of the target layer, and mesh it to build the mathematical model;

[0057] S14: Based on the stress magnitude and stress direction, construct a stress field simulation for the mathematical model.

[0058] In this embodiment, in step S11,

[0059] The study area is defined, and based on the actual distribution of the study area, the distribution of well locations and logging data of each well location within the study area is investigated to determine whether there is conventional or special logging data that can be used to calculate the magnitude and direction of geostress.

[0060] When extracting the target layer horizon and fault-fault data, depth data must be extracted for calculating the magnitude of the vertical principal stress. After determining the target layer horizon and fault-fault distribution characteristics of the study area, the extracted target layer horizon and fault-fault data are normalized. Specifically, the normalization process includes two aspects: correcting outliers and supplementing blank values. Outliers in the target layer horizon data are abnormally high or low depth values. Outliers in the fault-fault data are abnormal offset values ​​of fault-fault coordinates and abnormally high or low depth values. Correcting outliers and supplementing blank values ​​involves calculating the difference between the coordinate data and depth data of four points around the outlier data point. The calculation result replaces the outlier point or supplements the blank value point. If the outlier point or blank value point is located at the boundary of the target layer and there are only three or two points around it, the difference is calculated based on the coordinate data and depth data of the three or two points. The calculation result replaces the outlier point or supplements the blank value point.

[0061] In this embodiment, in step S12, the determination of the magnitude of the geostress in the study area is carried out in three aspects: calculating the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical principal stress.

[0062] If the study area contains conventional or special logging data, the calculation of the maximum and minimum horizontal principal stresses is mainly completed using the single-well geostress calculation formula.

[0063] If the study area does not contain conventional or special logging, the stress magnitude obtained from previous studies in the tectonic units outside the study area will be used as the geostress magnitude value of the study area through a pre-set literature survey.

[0064] The vertical principal stress is calculated based on the depth value of each data point in the stratigraphic information;

[0065] After calculating the maximum and minimum horizontal principal stresses for multiple wells in the study area, the maximum and minimum horizontal principal stresses at the same coordinate points of the target strata are determined based on the well trajectory coordinates, serving as the basis for applying stress to simulate the tectonic stress field.

[0066] The application of vertical principal stress in the study area is calculated based on the layer depth data. During the calculation process, the density data at different coordinate points are assigned values ​​according to the simulation requirements.

[0067] The direction of geostress in the study area is determined by accurately measuring the ancient and modern stress directions through experimental means, or by analyzing the current geostress direction using the wellbore collapse method of special logging.

[0068] In this embodiment, in step S13, the construction model is constructed based on stratigraphic information and fault-seam distribution data. When building the mathematical model, the fault-seam distribution area is distinguished from the original strata in the target layer by refining the grid.

[0069] In this embodiment, S2 includes:

[0070] S21: Data format processing and standardization of structural stress field simulation results;

[0071] S22: Geological parameters used to characterize fracture-fracture systems include: expansion coefficient, sliding stability coefficient, and penetration index; engineering parameters used to characterize fracture-fracture systems include: horizontal stress difference and formation fracture pressure.

[0072] S23: Perform normalized data processing on the geological parameters and the engineering parameters respectively;

[0073] S24: Using the analytic hierarchy process (AHP), determine the weight values ​​of the geological parameters and the engineering parameters, and construct multi-parameter comprehensive evaluation parameters for the effectiveness of the fracture-joint system.

[0074] In this embodiment, in step S21, the calculation of the characterization parameters of the fracture-seam system is based on the node data in the structural stress field simulation. After the simulation is completed, the node data output by the simulation is formatted by deleting the node number, blank cells, blank lines and a large number of header lines of the node data, and then performing standardization processing. The nodes containing outliers and zero values ​​of various types of parameters in the node data are deleted.

[0075] In step S22, the processed stress field simulation node data is used to obtain geological parameters such as expansion coefficient, sliding stability coefficient, and fracture penetration capacity coefficient, which characterize the effectiveness of the fracture-fracture system, as well as engineering parameters such as horizontal stress difference and formation fracture pressure, which characterize the effectiveness of the fracture-fracture system.

[0076] In this embodiment, in step S23, the geological parameters such as expansion coefficient, sliding stability coefficient, fracture penetration capacity coefficient, and the engineering parameters used to characterize the effectiveness of the fracture-fracture system, horizontal stress difference and formation fracture pressure, are normalized.

[0077] In step S24, the weight values ​​of each parameter are determined using the analytic hierarchy process (AHP), and then the effectiveness evaluation parameters of the fracture-seam system are constructed by combining multiple parameters based on the magnitude of the weight values.

[0078] In this embodiment, S3 includes:

[0079] S31: Compare the effectiveness evaluation parameters of the fracture-fracture system with the effectiveness parameters of a single well, calibrate the distribution range of the effectiveness data of the fracture-fracture system, and establish the effectiveness evaluation standard of the fracture-fracture system;

[0080] S32: Based on the established evaluation criteria for the effectiveness of the fracture-crack system, accurately determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0081] In this embodiment, in step S31, the effectiveness evaluation parameters of the fracture-fracture system are calibrated based on the actual single-well fracture effectiveness parameters, and the evaluation range of the effectiveness evaluation parameters of the fracture-fracture system is determined. When comparing the single-well fracture effectiveness parameters, the effectiveness evaluation parameters of the fracture-fracture system are calibrated using the fracture effectiveness parameters from imaging logging and production dynamic data. After calibration, the effectiveness evaluation criteria of the fracture-fracture system are defined.

[0082] In step S32, after establishing the evaluation criteria for the effectiveness of the fracture-crack system, the spatial distribution area of ​​effective cracks in the fracture-crack system of the study area is determined through the evaluation criteria for the effectiveness of the fracture-crack system.

[0083] In one embodiment, a method for evaluating the effectiveness of strike-slip fracture-fracture systems based on tectonic stress field simulation is proposed. To address the current challenges in predicting fracture effectiveness in deep-ultra-deep carbonate reservoirs, such as inaccurate core observations and outcrop measurements leading to higher-than-expected results, the tendency for well logging calculations to yield limited insights, and the difficulty in predicting fracture effectiveness in un-well-drilled areas after evaluating fracture networks using production dynamics data, this method proposes a solution. This involves determining the target stratigraphic level and fracture-fracture distribution characteristics of the study area, clarifying the boundary conditions for tectonic stress field simulation, constructing a reasonable structural and mathematical model, conducting tectonic stress field simulation, determining five geological and engineering parameters characterizing the fracture-fracture system based on the simulation results, assigning weights using the analytic hierarchy process (AHP), constructing multi-parameter evaluation parameters for the fracture-fracture system effectiveness, calibrating these parameters, establishing evaluation standards, and accurately defining the spatial distribution of effective fractures within the fracture-fracture system. This method, based on rock fracture criteria and using geostress as the applied condition, predicts the development and distribution of effective fractures in reservoirs, providing a practical solution for professionals studying the effective distribution characteristics of fracture-fracture systems in strike-slip fault zones and well location deployment.

[0084] This embodiment provides a method for evaluating the effectiveness of a strike-slip fracture-fracture system based on tectonic stress field simulation. The method includes:

[0085] S1: Determine the target stratigraphic level and fault-fault distribution characteristics of the study area, clarify the boundary conditions for tectonic stress field simulation, construct a reasonable tectonic model and mathematical model, and carry out tectonic stress field simulation.

[0086] S2: Based on the simulation results, five geological and engineering parameters for characterizing the fracture-fracture system are obtained. Then, the weights are assigned using the analytic hierarchy process (AHP), and the multi-parameter comprehensive evaluation parameters for the effectiveness of the fracture-fracture system are constructed.

[0087] S3: Define the evaluation parameters for the effectiveness of the fracture-crack system, establish evaluation standards for the effectiveness of the fracture-crack system, and accurately determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0088] In this embodiment, step S1 includes:

[0089] S11: Determine the scope of the study area, the distribution of well logging data, the target layer and the characteristics of fault-fracture distribution, extract the target layer and fault-fracture data, and perform data standardization processing.

[0090] S12: Define the boundary conditions for simulating the stress field, calculate the stress magnitude, analyze the stress direction, and set the range of the protective layer.

[0091] S13: Prioritize constructing a model using fracture-seam data from the target layer, then mesh the model and build a mathematical model.

[0092] S14: Based on the magnitude and direction of stress, construct a stress field simulation for the mathematical model.

[0093] In this embodiment, step S11, determining the study area and the distribution of well logging data, are preparatory steps to provide basic data for setting the boundary conditions for tectonic stress field simulation. The study area is determined, and based on its actual distribution, the distribution of well locations and the distribution of well logging data at each well location are investigated to determine whether conventional or special well logging data can be used to calculate the magnitude and direction of in-situ stress.

[0094] In this embodiment, during step S11, when extracting the target strata and fault-fault distribution characteristics of the study area, depth data must be extracted to facilitate the calculation of the vertical principal stress. During stress field simulation, the accuracy of the target strata data and even minor changes in the fault-fault distribution characteristics can significantly impact the simulation results. Therefore, after determining the target strata and fault-fault distribution characteristics of the study area, it is essential to perform normalization processing on the extracted target strata and fault-fault data, including correcting outliers and adding blank values.

[0095] In this embodiment, outliers in the target layer layer data are abnormally high or low depth values; outliers in the fracture-seam data are abnormal offset values ​​of fracture-seam coordinates and abnormally high or low depth values. Correction of outliers and supplementation of blank values ​​are both performed by calculating the difference between the coordinate and depth data of four points surrounding the outlier data point as the center. The calculation result replaces the outlier point or supplements the blank value point. If the outlier point or blank value point is located at the boundary of the target layer and there are only three or two points around it, the difference is also calculated based on the coordinate and depth data of the three or two points, and the calculation result replaces the outlier point or supplements the blank value point.

[0096] In this embodiment, the determination of the magnitude of the geostress in the study area in step S12 needs to be carried out in three aspects: calculating the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical principal stress.

[0097] If the study area contains conventional or special logging data, the calculation of the maximum and minimum horizontal principal stresses is mainly completed using the single-well geostress calculation formula:

[0098]

[0099]

[0100]

[0101] Where, in the formula It represents the vertical stress, in MPa; H is the burial depth (m). The density of the rock at a burial depth z can be obtained from density logging data; It is the maximum horizontal principal stress, MPa; ω1 and ω2 are the minimum horizontal principal stresses, MPa; α is the Biot coefficient; μ is the static Poisson's ratio. It is pore pressure, MPa.

[0102] If the study area does not contain conventional or special logging, the stress magnitude obtained from previous studies within the structural units outside the study area needs to be obtained through literature review. This is approximately the geostress magnitude of the study area, but the accuracy will be reduced.

[0103] The vertical principal stress is calculated based on the depth value of each data point in the stratigraphic information, using the following formula:

[0104]

[0105] Where, in the formula It represents the vertical stress, in MPa; H is the burial depth (m). Let z be the rock density at a burial depth of z.

[0106] In this embodiment, after the calculation of the maximum and minimum horizontal principal stresses of multiple wells in the study area is completed, it is necessary to determine the maximum and minimum horizontal principal stress values ​​at the same coordinate points of the target stratum based on the coordinate point information of their well trajectory. This information is used as the basis for applying the stress magnitude in the tectonic stress field simulation.

[0107] In this embodiment, the application of vertical principal stress in the study area is calculated based on the layer depth data. In the actual calculation process, the density data at different coordinate points needs to be assigned according to the simulation requirements. There are various placement methods, and the method suitable for the study area can be selected.

[0108] In this embodiment, in step S12,

[0109] The determination of the geostress direction in the study area currently relies mainly on two methods: one is to accurately determine the ancient and modern stress directions through experimental means, and the other is to analyze and determine the current geostress direction using the wellbore collapse method of special logging. If the study area has experimental conditions or special logging is available, directional information can be obtained. If neither of these is available, it is necessary to obtain the stress direction obtained by previous studies within the tectonic units outside the study area through literature review, which is approximately the geostress direction of the study area.

[0110] In this embodiment, in step S12, the range of the protective layer determines the accuracy of the structural stress field simulation. Its range needs to be adjusted according to the size and shape of the study area. Usually, setting it to 6-10 times can offset the boundary effect and achieve the best effect.

[0111] In this embodiment, in step S13, the construction model needs to be built based on stratigraphic information and fault-fracture distribution data. When building the mathematical model, the fault-fracture distribution area needs to be distinguished from the undisturbed strata in the target layer by refining the mesh. This allows for a more precise determination of the stress distribution and fracture development around and within the fault-fracture system using a finer mesh.

[0112] In this embodiment, step S14 completes the construction of the mathematical model of the study area. Under reasonable boundary conditions, tectonic stress field simulation can then be carried out. The quality of the tectonic stress field simulation results directly affects the accuracy of crack prediction. Therefore, it is necessary to continuously adjust and optimize the boundary conditions based on the tectonic stress field simulation results to find the optimal simulation results for subsequent crack prediction work.

[0113] In this embodiment, step S2 includes:

[0114] S21: Data format processing and standardization of structural stress field simulation results.

[0115] S22: Determine the five types of parameters used to characterize the fracture-fracture system: expansion coefficient, sliding stability coefficient, penetration index, and engineering parameters used to characterize the fracture-fracture system: horizontal stress difference and formation fracture pressure.

[0116] S23: Normalize the data for each of the five parameters to facilitate comprehensive calculation of multiple parameters within a unified value range.

[0117] S24: The analytic hierarchy process (AHP) is used to determine the weight values ​​of each parameter, and the parameters for evaluating the effectiveness of the fracture-seam system are constructed by combining multiple parameters.

[0118] In this embodiment, in step S21, the calculation of the characterization parameters of the fracture-seam system is based on the node data in the structural stress field simulation. After the simulation is completed, the node data output by the simulation needs to be formatted first, and the node number, blank cells, blank lines and a large number of header lines are deleted. Then, the standardization process is performed, and the nodes containing outliers and zero values ​​of various types of parameters in the node data are deleted.

[0119] In this embodiment, in step S22, by applying the processed stress field simulation node data, the geological parameter expansion coefficient (which characterizes the effectiveness of the fracture-fracture system) can be obtained. The calculation formula is as follows:

[0120]

[0121] in, The maximum horizontal principal stress is expressed in MPa. The cross-sectional normal stress is expressed in MPa. The minimum principal stress is σ, MPa.

[0122] Sliding stability coefficient ( The calculation formula is as follows: .

[0123] in, For shear stress, It is normal stress. ρ represents the formation pore pressure, in MPa.

[0124] Crack penetration coefficient ( The calculation formula is as follows:

[0125]

[0126] in, The normal stress on the surface of a natural crack, in MPa; The minimum principal stress is , MPa.

[0127] The horizontal stress difference is an engineering parameter used to characterize the effectiveness of a fracture-joint system. The calculation formula is as follows: .

[0128] in, The maximum principal stress is in MPa. The minimum principal stress is σ_min, MPa.

[0129] Formation fracture pressure ( The calculation formula is as follows:

[0130]

[0131] in, It is a non-equilibrium factor for the horizontal framework stress of the formation, and is dimensionless. This indicates a non-fractured formation or a porous reservoir; otherwise... , This indicates the formation fracturing pressure used in hydraulic fracturing operations. This indicates that it is used in drilling to prevent excessively heavy drilling mud from fracturing the formation and causing well leakage. Formation pore pressure, MPa; The pressure of the overlying strata is MPa; The Poisson's ratio of the rock is dimensionless. for Coefficient, geological structural stress coefficient, dimensionless; denoted as the tensile strength of the rock, in MPa.

[0132] In this embodiment, in step S23, since the value ranges of the five types of parameters differ significantly and there are multiple-level differences between different data, in order to facilitate the subsequent construction of the effectiveness evaluation parameters of the fracture-seam system, it is necessary to first normalize each parameter. The calculation formula is as follows:

[0133]

[0134] Where Y is the normalized value, which is dimensionless; X is a data point with arbitrary parameters; The minimum value among any parameter data points; It represents the maximum value among any parameter data points.

[0135] In this embodiment, when determining the weight values ​​of each parameter using the analytic hierarchy process (AHP) in step S24, it is important to note that the weight values ​​of the two major categories of parameters (geological factors and engineering factors) should be calculated first, and then the weight values ​​of the subcategories of parameters within each major category should be calculated. After calculating the weight values ​​of the five subcategories (expansion coefficient, sliding stability coefficient, penetration capacity index, horizontal stress difference, and formation fracture pressure) of the two major categories of parameters (geological factors and engineering factors), the effectiveness evaluation parameters of the fracture-fracture system can be constructed based on the magnitude of the weight values. The calculation formula is as follows:

[0136]

[0137] Where Z is the dimensionless parameter for evaluating the effectiveness of the fracture-fracture system; A is the dimensionless weight value of the geological factors; D is the dimensionless geological factor; B is the dimensionless weight value of the engineering factors; and G is the dimensionless engineering factor. This is the normalized expansion coefficient value, which is dimensionless. This is the normalized sliding stability coefficient, which is dimensionless. This is the normalized crack penetration capability coefficient, dimensionless. The normalized horizontal stress difference is dimensionless. 1. Normalized formation fracture pressure value, dimensionless; 2. Normalized expansion coefficient weight value, dimensionless; 3. Normalized slip stability coefficient weight value, dimensionless; 4. Normalized fracture penetration capacity coefficient weight value, dimensionless; 5. Normalized horizontal stress difference weight value, dimensionless; 6. Normalized formation fracture pressure weight value, dimensionless.

[0138] In this embodiment, step S3 includes:

[0139] S31: Compare the effectiveness evaluation parameters of the fracture-fracture system with the effectiveness parameters of a single well, calibrate the distribution range of the effectiveness data of the fracture-fracture system, and establish the effectiveness evaluation standard of the fracture-fracture system.

[0140] S32: Based on the established evaluation criteria for the effectiveness of fracture-crack systems, accurately determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

[0141] In this embodiment, in step S31, since the fracture-fracture system effectiveness evaluation parameters are the results of structural stress field simulation, they need to be calibrated based on actual single-well fracture effectiveness parameters to determine a reasonable evaluation range for the fracture-fracture system effectiveness evaluation parameters. When comparing single-well fracture effectiveness parameters, the fracture effectiveness parameters from imaging logging and production dynamic data are more accurate and have strong continuity. Therefore, calibrating the fracture-fracture system effectiveness evaluation parameters using these two methods yields better results. After calibration, the fracture-fracture system effectiveness evaluation criteria can be defined.

[0142] In this embodiment, in step S32, after establishing the evaluation criteria for the effectiveness of the fracture-crack system, the spatial distribution area of ​​effective cracks in the fracture-crack system of the study area can be accurately determined through this criterion.

[0143] In one specific embodiment, this example studies the effective crack distribution area of ​​the strike-slip fracture zone fracture-crack system using an effectiveness evaluation method based on tectonic stress field simulation.

[0144] First, the study area for this stress field simulation was determined based on the actual situation. The distribution of well locations within the study area was then investigated. A total of 5 wells were identified in the study area. After determining the number of conventional logging and imaging logging wells, it was found that the study area included 5 conventional logging wells and 1 imaging logging well. The logging data can be used to calculate the magnitude and direction of in-situ stress. Next, the target stratum was identified based on actual production needs, and the fault-fracture distribution characteristics at this stratum were determined based on actual seismic data. Then, the coordinates and depth data of each point on the target stratum and fault-fractures were extracted. After data extraction, the extracted target stratum and fault-fracture data needed to be standardized in Excel or any data processing software, correcting outliers and adding blank values. Since no core wells were found in the study area, the magnitude of the in-situ stress needed to be determined through well logging calculations. Using five conventional well logs in the study area, the maximum horizontal principal stress was calculated to be 139.21 MPa, and the minimum horizontal principal stress was 118.26 MPa. The stress direction was determined using the wellbore collapse direction from the only special well log; the current maximum horizontal principal stress direction is 35.82°, and the minimum horizontal principal stress direction is 125.82°. To ensure the accuracy of the tectonic stress field simulation, the protective boundary was set to be 10 times the size of the study area. A tectonic model of the study area was constructed based on the target layer stratigraphic data and fault-fracture system distribution data. The fault-fracture distribution area was then meshed to create a reasonable mathematical model. Finally, after applying the stress magnitude and direction, and after the protective boundary offset the boundary effects, the tectonic stress field simulation could be performed. During the tectonic stress field simulation, the boundary conditions were continuously adjusted to obtain the final simulation results.

[0145] After exporting the nodal data from the stress field simulation, and after formatting and standardization, five types of parameters can be obtained to characterize the fracture-fracture system: the expansion coefficient, the sliding stability coefficient, and the penetration index; and five types of engineering parameters to characterize the fracture-fracture system: horizontal stress difference and formation fracture pressure (e.g., Figure 2-6 (As shown). It should be noted that due to the significant differences in the value ranges of the five types of parameters, with multiple-level differences between different data points, each parameter needs to be normalized first to facilitate the subsequent construction of parameters for evaluating the effectiveness of the fracture-fracture system. Then, the weight values ​​for each parameter are determined using the analytic hierarchy process (AHP). The weight values ​​for each parameter are as follows: Geological parameter weight: 0.33; Engineering parameter weight: 0.67; Expansion coefficient weight: 0.15; Sliding stability coefficient weight: 0.07; Penetration capacity index weight: 0.11; Horizontal stress difference weight: 0.17; Formation fracture pressure weight: 0.50. Based on the weight values ​​of each parameter, a comprehensive multi-parameter evaluation parameter for the effectiveness of the fracture-fracture system is constructed.

[0146] By comparing the fracture effectiveness evaluation parameters with imaging logging data from one well and production dynamic data from multiple wells within the study area, it is concluded that for the fracture effectiveness evaluation parameters in this study area, when the parameter value > 0, the fracture system can be considered effective, and the fracture effectiveness evaluation parameter value is directly proportional to the fracture effectiveness; the higher the fracture effectiveness evaluation parameter value, the stronger the fracture effectiveness. Conversely, when the parameter value < 0, the fracture system can be considered ineffective, representing undisturbed formation (e.g., Figure 7 (As shown). Based on this standard, the spatial distribution area of ​​effective fractures in the fracture-fracture system of the study area can be accurately determined, which is of great significance for well location deployment.

[0147] This embodiment provides a method for evaluating the effectiveness of fracture-fracture systems in strike-slip fault zones. It involves determining the target stratigraphic level and fracture distribution characteristics of the study area, clarifying the boundary conditions for tectonic stress field simulation, constructing a reasonable structural and mathematical model, conducting tectonic stress field simulation, and then, based on the simulation results, determining five geological and engineering parameters to characterize the fracture-fracture system. The analytic hierarchy process (AHP) is then used to assign weights, and multiple parameters are comprehensively constructed to evaluate the effectiveness of the fracture-fracture system. Finally, these parameters are calibrated, and a standard for evaluating the effectiveness of the fracture-fracture system is established to accurately determine the spatial distribution area of ​​effective fractures within the system. This method solves the current problems encountered in predicting the effectiveness of fractures in deep to ultra-deep carbonate reservoirs, such as the inconsistency between measured results from core observation and field outcrops, the tendency for well logging methods to lead to one-well views when calculating fracture effectiveness parameters, and the difficulty in predicting the effectiveness of fractures in un-well-drilled areas after evaluating the fracture network using production dynamic data.

[0148] This study provides a method for evaluating the effectiveness of strike-slip fault zone fracture systems. This method involves determining the target stratigraphic level and fracture distribution characteristics of the study area, clarifying the boundary conditions for tectonic stress field simulation, constructing a reasonable tectonic and mathematical model, conducting tectonic stress field simulation, and then, based on the simulation results, determining five geological and engineering parameters to characterize the fracture system. The analytic hierarchy process (AHP) is then used to assign weights, and multiple parameters are integrated to construct the fracture system effectiveness evaluation parameters. Finally, the fracture system effectiveness evaluation parameters are calibrated, and a fracture system effectiveness evaluation standard is established to accurately determine the spatial distribution area of ​​effective fractures within the fracture system. This method can predict the development and distribution of fractures in reservoirs based on rock fracture criteria and applied by geostress. It effectively avoids the problems currently faced in predicting the effectiveness of fractures in deep-ultra-deep carbonate reservoirs, such as the results of core observation and field outcrop testing being greater than the actual situation, the tendency to form a one-hole view when using well logging methods to calculate fracture effectiveness parameters, and the difficulty in predicting the effectiveness of fractures in un-well-placed areas after evaluating the effectiveness of the fracture network using production dynamic data. This provides a set of effective solutions for professionals studying the effectiveness distribution characteristics of the fracture-fracture system in strike-slip fault zones and well placement.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for evaluating effective strike-slip fractures based on tectonic stress field simulation, characterized in that, The method includes: S1: Obtain the target stratigraphic level and fault-fracture distribution characteristics of the study area, clarify the simulation boundary conditions of the tectonic stress field, construct a reasonable tectonic model and mathematical model based on the simulation boundary conditions, carry out tectonic stress field simulation, and obtain simulation results; S2: Based on the simulation results, obtain the geological and engineering parameters of the fault-fracture system determined by the target stratum and fault-fracture distribution characteristics, assign weights to the geological and engineering parameters using the analytic hierarchy process, and construct the fault-fracture system effectiveness evaluation parameters through multi-parameter synthesis. S3: Calibrate the effectiveness evaluation parameters of the fracture-crack system, establish the effectiveness evaluation criteria of the fracture-crack system, and define the spatial distribution area of ​​effective cracks in the fracture-crack system; S1 includes: S11: Determine the scope of the study area, the distribution of well logging data, the target strata and the characteristics of fault-fracture distribution, extract the target strata and fault-fracture data, and perform data standardization processing; S12: Define the boundary conditions for simulating the stress field, calculate the stress magnitude, analyze the stress direction, and set the range of the protective layer; S13: Construct the structural model using the fracture-seam data of the target layer, and mesh it to build the mathematical model; S14: Based on the stress magnitude and stress direction, construct a stress field simulation for the mathematical model; In S12, the determination of the magnitude of the geostress in the study area is carried out in three aspects: calculating the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical principal stress. If the study area contains conventional or special logging data, the calculation of the maximum and minimum horizontal principal stresses is mainly completed using the single-well geostress calculation formula. If the study area does not contain conventional or special logging, the stress magnitude obtained from previous studies in the tectonic units outside the study area will be used as the geostress magnitude value of the study area through a pre-set literature survey. The vertical principal stress is calculated based on the depth value of each data point in the stratigraphic information; After calculating the maximum and minimum horizontal principal stresses for multiple wells in the study area, the maximum and minimum horizontal principal stresses at the same coordinate points of the target strata are determined based on the well trajectory coordinates, serving as the basis for applying stress to simulate the tectonic stress field. The application of vertical principal stress in the study area is calculated based on the layer depth data. During the calculation process, the density data at different coordinate points are assigned values ​​according to the simulation requirements. The direction of geostress in the study area is determined by accurately measuring the ancient and modern stress directions through experimental means, or by analyzing the current geostress direction using the wellbore collapse method of special logging.

2. The method according to claim 1, characterized in that, In S11, The study area is defined, and based on the actual distribution of the study area, the distribution of well locations and logging data of each well location within the study area is investigated to determine whether there is conventional or special logging data that can be used to calculate the magnitude and direction of geostress. When extracting the target layer horizon and fault-fault data, depth data must be extracted for calculating the magnitude of the vertical principal stress. After determining the target layer horizon and fault-fault distribution characteristics of the study area, the extracted target layer horizon and fault-fault data are normalized. Specifically, the normalization process includes two aspects: correcting outliers and supplementing blank values. Outliers in the target layer horizon data are abnormally high or low depth values. Outliers in the fault-fault data are abnormal offset values ​​of fault-fault coordinates and abnormally high or low depth values. Correcting outliers and supplementing blank values ​​involves calculating the difference between the coordinate data and depth data of four points around the outlier data point. The calculation result replaces the outlier point or supplements the blank value point. If the outlier point or blank value point is located at the boundary of the target layer and there are only three or two points around it, the difference is calculated based on the coordinate data and depth data of the three or two points. The calculation result replaces the outlier point or supplements the blank value point.

3. The method according to claim 1, characterized in that, In step S13, the construction model is built based on stratigraphic information and fault-seam distribution data. When building the mathematical model, the fault-seam distribution area is distinguished from the original strata in the target layer by refining the grid.

4. The method according to claim 1, characterized in that, S2 includes: S21: Data format processing and standardization of structural stress field simulation results; S22: Geological parameters used to characterize fracture-fracture systems include: expansion coefficient, sliding stability coefficient, and penetration index; engineering parameters used to characterize fracture-fracture systems include: horizontal stress difference and formation fracture pressure. S23: Perform normalized data processing on the geological parameters and the engineering parameters respectively; S24: Using the analytic hierarchy process (AHP), determine the weight values ​​of the geological parameters and the engineering parameters, and construct multi-parameter comprehensive evaluation parameters for the effectiveness of the fracture-joint system.

5. The method according to claim 4, characterized in that, In step S21, the calculation of the characterization parameters of the fracture-seam system is based on the node data in the structural stress field simulation. After the simulation is completed, the node data output by the simulation is formatted by deleting the node number, blank cells, blank lines and a large number of header lines of the node data, and then performing standardization processing. The nodes containing outliers and zero values ​​of various types of parameters in the node data are deleted. In step S22, the processed stress field simulation node data is used to obtain geological parameters such as expansion coefficient, sliding stability coefficient, and fracture penetration capacity coefficient, which characterize the effectiveness of the fracture-fracture system, as well as engineering parameters such as horizontal stress difference and formation fracture pressure, which characterize the effectiveness of the fracture-fracture system.

6. The method according to claim 4, characterized in that, In S23, the geological parameters such as expansion coefficient, sliding stability coefficient, fracture penetration capacity coefficient, and engineering parameters such as horizontal stress difference and formation fracture pressure used to characterize the effectiveness of the fracture-fracture system are normalized. In step S24, the weight values ​​of each parameter are determined using the analytic hierarchy process (AHP), and then the effectiveness evaluation parameters of the fracture-seam system are constructed by combining multiple parameters based on the magnitude of the weight values.

7. The method according to claim 1, characterized in that, S3 includes: S31: Compare the effectiveness evaluation parameters of the fracture-fracture system with the effectiveness parameters of a single well, calibrate the distribution range of the effectiveness data of the fracture-fracture system, and establish the effectiveness evaluation standard of the fracture-fracture system; S32: Based on the established evaluation criteria for the effectiveness of the fracture-crack system, accurately determine the spatial distribution area of ​​effective cracks in the fracture-crack system.

8. The method according to claim 7, characterized in that, In step S31, the effectiveness evaluation parameters of the fracture-fracture system are calibrated based on the actual single-well fracture effectiveness parameters, and the evaluation range of the effectiveness evaluation parameters of the fracture-fracture system is determined. When comparing the single-well fracture effectiveness parameters, the effectiveness evaluation parameters of the fracture-fracture system are calibrated using the fracture effectiveness parameters from imaging logging and production dynamic data. After calibration, the effectiveness evaluation criteria of the fracture-fracture system are defined. In step S32, after establishing the evaluation criteria for the effectiveness of the fracture-crack system, the spatial distribution area of ​​effective cracks in the fracture-crack system of the study area is determined through the evaluation criteria for the effectiveness of the fracture-crack system.

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