Method and device for predicting artificial fractures in oil reservoir development

Abstract

The invention discloses a method and device for predicting artificial fractures in oil reservoir development, and belongs to the technical field of oil reservoir development fracturing. The method comprises the following steps: constructing an artificial fracture theoretical model according to well drilling and oil testing data of a target block; obtaining an earthquake elastic parameter body and a stress field parameter body according to the earthquake data of the target block in combination with logging data, logging data and rock physics related test parameter data of the target block; and correcting the artificial fracture theoretical model based on the earthquake elastic parameter body and the stress field parameter body, and taking the corrected artificial fracture theoretical model as an earthquake prediction artificial fracture model. The method can realize fine prediction of artificial fractures, and can effectively guide development and production of oil reservoirs.

Description

Technical Field

[0001] This application relates to the field of fracturing technology for reservoir development, and in particular to a method and apparatus for predicting artificial fractures in reservoir development. Background Technology

[0002] Fracturing is a major measure to enhance production in low-permeability reservoirs. By creating artificial fractures through fracturing operations, the initial production and ultimate recovery rate of the oilfield can be improved. In the middle and late stages of reservoir development, artificial fractures serve as the main seepage field for water injection and oil displacement. The overall prediction and evaluation of their expansion scale and distribution direction are key issues that urgently need to be addressed in the dynamic adjustment of production measures, stabilizing oil production and controlling water, and improving recovery rate.

[0003] In related technologies, the prediction of artificial fractures is based on a theoretical model of artificial fractures constructed from data from drilling, fracturing, and logging. However, this theoretical model of artificial fractures assumes that the underground geological conditions are homogeneous and the stress field is stable. Therefore, the predicted artificial fractures differ significantly from the artificial fractures after actual large-scale modification, and cannot effectively guide actual reservoir development and production. Summary of the Invention

[0004] In view of this, this application provides a method and apparatus for predicting artificial fractures in oil reservoir development, which can achieve precise prediction of artificial fractures and effectively guide the development and production of oil reservoirs.

[0005] Specifically, the following technical solutions are included:

[0006] On the one hand, embodiments of this application provide a method for predicting artificial fractures in reservoir development, including:

[0007] Based on drilling and oil testing data of the target block, a theoretical model of artificial fractures was constructed.

[0008] Based on the seismic data of the target block, combined with the well logging, well logging and rock physics related test parameter data of the target block, the seismic elastic parameter volume and stress field parameter volume are obtained;

[0009] Based on the seismic elastic parameter body and the stress field parameter body, the artificial crack theoretical model is corrected, and the corrected artificial crack theoretical model is used as the artificial crack model for earthquake prediction.

[0010] In some embodiments, the drilling and testing data includes drilling core data, logging data, and fracturing data, and the step of constructing an artificial fracture theoretical model based on the drilling and testing data of the target block includes:

[0011] Based on the drilling core data, the logging data, and the fracturing data, the key parameters of the known artificial fractures are obtained, including fracture length, fracture height, fracture aperture, and azimuth.

[0012] Based on the known key parameters of the artificial crack, an artificial crack is simulated and constructed to obtain the theoretical model of the artificial crack.

[0013] In some embodiments, obtaining the seismic elastic parameter volume and stress field parameter volume based on the seismic data of the target block, combined with well logging, well logging and rock physics related test parameter data of the target block, includes:

[0014] Based on the well logging, well logging and rock physics related test parameter data of the target block, obtain the particle aspect ratio and clay aspect ratio data;

[0015] The particle aspect ratio and clay aspect ratio data were sequentially subjected to quantitative analysis and parameter testing to determine the intergranular structure of the target rock.

[0016] Based on the intergranular structure of the target rock, a rock physics model is constructed;

[0017] Based on the rock physics model, elastic parameters are calculated, and the seismic elastic parameter volume is obtained by inversion using seismic data of the target block, wherein the seismic elastic parameter volume is characterized using spatial grid data.

[0018] In some embodiments, obtaining the seismic elastic parameter volume and stress field parameter volume based on the seismic data of the target block, combined with well logging, well logging and rock physics related test parameter data of the target block, further includes:

[0019] Based on the seismic data of the target block and the seismic elastic parameter volume, the fault, stratum thickness, and structural surface are obtained;

[0020] Based on the fault, stratigraphic thickness, structural surface, and seismic data of the target block, the curvature function of the true stratigraphic trend surface is obtained;

[0021] Based on the seismic elastic parameters and the curvature function of the actual stratum trend surface, the geostress field is simulated to obtain the stress field parameter volume.

[0022] In some embodiments, the correction of the artificial crack theoretical model based on the seismic elastic parameter body and the stress field parameter body includes:

[0023] Based on the earthquake elastic parameter body, the artificial crack theoretical model is reconstructed on a scale to obtain an intermediate artificial crack model;

[0024] Based on the stress field parameter volume, the orientation of the intermediate artificial crack model is corrected to obtain the corrected artificial crack theoretical model.

[0025] In some embodiments, the step of reconstructing the artificial fracture theoretical model based on the seismic elastic parameter body to obtain an intermediate artificial fracture model includes:

[0026] Based on the earthquake elastic parameter volume and the known key parameters of artificial cracks, a first nonlinear classification variable multivariate function is constructed between the artificial crack size and the elastic parameters.

[0027] Using the first nonlinear classification variable multivariate function, the artificial crack theoretical model is reconstructed to obtain the intermediate artificial crack model.

[0028] In some embodiments, the multivariate function of the first nonlinear categorical variable is obtained according to the following formula:

[0029] L fr =a*E 2 +b*LM+c

[0030] H fr =a*R bi 2 +b*E+c

[0031] In the formula: L fr This indicates the length of the artificial crack, in meters (m); H fr The value represents the artificial crack height, in meters (m); E represents Young's modulus, in gigabytes of pressure (GPa); and LM represents Lamé's constant, in units of 10⁻⁶. 9 kg 2 / (m 4 ·s 2 ); R bi This represents the rock brittleness index, expressed as a percentage; a, b, and c represent region-related constants.

[0032] In some embodiments, the orientation correction of the intermediate artificial crack model based on the stress field parameter volume to obtain the corrected artificial crack theoretical model includes:

[0033] Based on the stress field parameter volume and the known key parameters of the artificial crack, a second nonlinear classification variable multivariate function is constructed between the orientation of the artificial crack and the stress field parameters.

[0034] Using the second nonlinear classification variable multivariate function, the intermediate artificial crack model is oriented for correction, resulting in the corrected artificial crack theoretical model.

[0035] In some embodiments, the second nonlinear categorical variable multivariate function is obtained according to the following formula:

[0036] A fr =a*PSDmax 2 +b*PSD min +c

[0037] In the formula: A fr This indicates the location of the artificial crack, in degrees; PSD max This indicates the direction of the maximum principal stress, in degrees; PSD min This indicates the direction of the minimum principal stress, in degrees; a, b, and c represent region-specific constants.

[0038] On the other hand, embodiments of this application also provide a device for predicting artificial fractures in reservoir development, comprising:

[0039] The module is used to build a theoretical model of artificial fractures based on drilling and testing data of the target block;

[0040] The parameter body acquisition module is used to obtain the seismic elastic parameter body and stress field parameter body based on the seismic data of the target block, combined with the well logging data, well logging data and rock physics related test parameter data of the target block;

[0041] The correction module is used to correct the artificial crack theoretical model based on the seismic elastic parameter body and the stress field parameter body, and to use the corrected artificial crack theoretical model as the artificial crack model for earthquake prediction.

[0042] The beneficial effects of the technical solutions provided in this application include at least the following:

[0043] The method for predicting artificial fractures in reservoir development provided in this application first constructs a theoretical model of artificial fractures based on drilling and oil testing data of the target block. Then, based on seismic data of the target block, combined with well logging, well logging, and rock physics-related experimental parameters, a seismic elastic parameter volume and a stress field parameter volume are obtained to correct the theoretical model of artificial fractures. The corrected theoretical model of artificial fractures is then used as a seismic prediction model for artificial fractures to predict the distribution characteristics of artificial fractures generated by fracturing. Compared with the theoretical models of artificial fractures in related technologies, this seismic prediction model of artificial fractures, after correction with the seismic elastic parameter volume and stress field data volume, can better characterize the morphology of artificial fractures in actual formations, enabling precise prediction of artificial fractures and effectively guiding reservoir development and production. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating a method for predicting artificial fractures in reservoir development, provided as an embodiment of this application;

[0046] Figure 2 A schematic diagram of a theoretical model of an artificial crack provided for an embodiment of this application;

[0047] Figure 3(a) is a comparison chart of the predicted elastic parameters of different rock and mineral grain sizes provided in the embodiments of this application and the actual measured parameters;

[0048] Figure 3(b) is a comparison chart of the predicted elastic parameters and the measured parameters for different rock minerals and clays with varying aspect ratios provided in the embodiments of this application.

[0049] Figure 4 A schematic diagram of seismic elastic parameters in a method for predicting artificial fractures in reservoir development provided in an embodiment of this application;

[0050] Figure 5 A schematic diagram of stress field parameters in a method for predicting artificial fractures in reservoir development provided in an embodiment of this application;

[0051] Figure 6 A schematic diagram showing the comparison between the artificial crack size and Young's modulus before and after scale reconstruction, provided for embodiments of this application;

[0052] Figure 7 A schematic diagram showing the superimposed comparison of the orientation of the artificial crack and the direction of the maximum principal stress before and after orientation correction, provided for embodiments of this application;

[0053] Figure 8 An overlay diagram of an earthquake prediction artificial crack and natural crack coupled crack network system and Young's modulus distribution provided in an embodiment of this application;

[0054] Figure 9 A comparative diagram showing the overlay of the artificial crack theoretical model, the earthquake prediction artificial crack model, and production data provided in the embodiments of this application;

[0055] Figure 10 This is a schematic diagram of an artificial fracture prediction device for reservoir development provided in an embodiment of this application.

[0056] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] Unless otherwise defined, all technical terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art.

[0059] In the embodiments of this application, discrete equivalent permutation refers to a method that constructs an equivalent model with functions such as analysis, observation, editing, and visualization by comprehensively analyzing discrete observation data and then digitally permuting it.

[0060] Seismic spatial variation constraint refers to the method of using spatial grid variable parameters obtained from three-dimensional seismic analysis for constraint. Unlike conventional mean constant parameter constraints, seismic spatial variation constraint can more accurately reflect the spatial variation characteristics of stress field.

[0061] Iterative coupled regression refers to a mathematical fitting method that combines multiple continuous or discrete independent variables with a single continuous or discrete dependent variable through cyclic feedback to predict their correlation patterns.

[0062] Nonlinear categorical variable multivariate functions refer to nonlinear multivariate high-order mathematical function relationships constructed by analyzing and predicting the correlation patterns between a single dependent variable and multiple independent variables.

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0064] Fracturing is a major measure for enhancing production in low-permeability reservoirs. Through fracturing operations, the expansion of natural fractures is promoted, and the resulting artificial fractures can increase the volume after reconstruction, improving the initial production and ultimate recovery rate of the oilfield. However, as the oilfield development time increases, with the overall densification of the well network and large-scale fracturing measures, the underground fracture network system and the seepage patterns of oil and water become increasingly complex, making injection-production control more difficult and leading to greater decline. Therefore, in the mid-to-late stages of development, the overall prediction and evaluation of the expansion scale and distribution direction of artificial fractures, as the main seepage field for water injection and oil displacement, is a critical issue that urgently needs to be addressed for dynamic production measures adjustment, oil and water stabilization, and improved recovery rate.

[0065] Currently, the prediction of artificial fractures is generally based on data from drilling cores, imaging logging, and fracturing measures. The known artificial fracture length, height, and orientation are statistically analyzed to simulate a theoretical model of artificial fractures that corresponds one-to-one with the perforation location. However, this model assumes that the underground geological conditions are homogeneous and the stress field is stable. Therefore, the fracture direction in the general artificial fracture theoretical model is unidirectional and the fracture expansion scale is stable, which does not match the complexity of the underground fracture network after actual scale modification and cannot effectively guide actual reservoir development and production.

[0066] To address the problems existing in the prior art, this application provides a method for predicting artificial fractures in oil reservoir development, which can achieve accurate prediction of artificial fractures and effectively guide the development and production of oil reservoirs.

[0067] Figure 1 A flowchart illustrating a method for predicting artificial fractures in reservoir development, provided in this application embodiment, is available. Figure 1 The method includes the following steps.

[0068] Step 101: Construct a theoretical model of artificial fractures based on drilling and oil testing data of the target block.

[0069] By constructing a theoretical model of artificial cracks, the spatial distribution morphology of artificial cracks can be preliminarily clarified.

[0070] In this embodiment of the application, the theoretical model of the artificial crack can be constructed based on the Petrel software. For example, Figure 2 This is a schematic diagram of a theoretical model of an artificial crack provided in an embodiment of this application. Figure 2 The spatial distribution pattern of the artificial cracks can be observed.

[0071] In some embodiments, drilling and testing data include drilling core data, logging data, and fracturing data. Based on this, this step specifically includes: obtaining known key parameters of the artificial fracture based on the drilling core data, logging data, and fracturing data, wherein the key parameters include fracture length, fracture height, fracture aperture, and azimuth; and, based on the known key parameters of the artificial fracture, applying a discrete equivalent substitution method to simulate and construct a theoretical model of the artificial fracture with mean fracture length, fracture height, and azimuth corresponding one-to-one with the perforation location.

[0072] Step 102: Based on the seismic data of the target block, combined with the well logging, well logging and rock physics related test parameter data of the target block, obtain the seismic elastic parameter volume and stress field parameter volume.

[0073] By obtaining the seismic elastic parameter volume and stress field parameter volume, the theoretical model of artificial cracks can be subsequently corrected.

[0074] In this embodiment, the seismic elastic parameters in this step are obtained based on the optimization of the pore structure of the rock physical matrix. The specific acquisition process includes: obtaining particle aspect ratio and clay aspect ratio data based on well logging, drilling, and rock physical related test parameter data of the target block; quantifying the particle aspect ratio and clay aspect ratio using the equivalent medium theory; and then testing the particle aspect ratio and clay aspect ratio using the controlled variable method to determine the intergranular structure of the target rock. Figure 3(a) is a comparison chart of the predicted elastic parameters of different rock and mineral particle sizes provided in this embodiment with the measured parameters. Figure 3(b) is a comparison diagram of the predicted elastic parameters and the measured parameters of different rock minerals and clay aspect ratios provided in the embodiments of this application. According to Figure 3(a) and Figure 3(b), the curve with a particle aspect ratio of 0.35 fits the measured curve well, and the curve with a clay aspect ratio of 0.2 fits the measured curve best. Based on the intergranular structure of the target rock, a rock physics model is constructed to calculate elastic parameters that are more relevant to stratigraphic lithology and grain size. The seismic elastic parameter volume is obtained by pre-stack geostatistical inversion and is characterized using spatial grid data.

[0075] The seismic elastic parameter volume conforms to the porosity and elastic deformation characteristics of the actual subsurface matrix. These seismic elastic parameters include the P-wave / S-wave velocity ratio, density, Lamé coefficient, shear modulus, bulk modulus, Young's modulus, and Poisson's ratio, such as... Figure 4 As shown, where Figure 4 This is a schematic diagram of seismic elastic parameters in a method for predicting artificial fractures in reservoir development, provided in an embodiment of this application.

[0076] In this embodiment of the application, the stress field parameter volume in this step is obtained through seismic spatially constrained stress field simulation. The specific acquisition process includes: obtaining faults, stratigraphic thickness, and structural surfaces based on high-precision structural interpretation using the seismic data of the target block and the spatial grid data corresponding to the seismic elastic parameter volume; obtaining the true stratigraphic trend surface curvature function by applying the least squares method and trend surface function based on the high-precision seismic structural surfaces and curvature attributes obtained from the faults, stratigraphic thickness, structural surfaces, and seismic data of the target block; and simulating the seismic spatially constrained stress field using the three-dimensional finite difference numerical simulation method based on the seismic elastic parameters and the true stratigraphic trend surface curvature function, according to the generalized Hooke's law, to obtain the stress field parameter volume.

[0077] The stress field parameters include the directions of maximum and minimum principal stresses, maximum and minimum principal strains, maximum and minimum principal stresses, principal curvatures, crack direction, and crack density index, such as... Figure 5 As shown, where Figure 5 This is a schematic diagram of stress field parameters in a method for predicting artificial fractures in reservoir development, provided in an embodiment of this application.

[0078] Step 103: Based on the seismic elastic parameter volume and stress field parameter volume, the artificial crack theoretical model is corrected, and the corrected artificial crack theoretical model is used as the artificial crack model for earthquake prediction.

[0079] By correcting the theoretical model of artificial fractures, an artificial fracture prediction model for earthquakes is obtained. This model can be used to predict artificial fractures. Compared with the theoretical model of artificial fractures in related technologies, this artificial fracture prediction model, after being corrected by the seismic elastic parameter volume and stress field related data volume, can better characterize the morphology of artificial fractures in actual strata.

[0080] In some embodiments, this step includes: reconstructing the scale of the artificial crack theoretical model based on the seismic elastic parameter volume to obtain an intermediate artificial crack model; and correcting the orientation of the intermediate artificial crack model based on the stress field parameter volume to obtain a corrected artificial crack theoretical model. In other words, this step includes two sub-steps: scale reconstruction and orientation correction of the artificial crack theoretical model.

[0081] In some embodiments, the process of reconstructing the scale of an artificial crack theoretical model based on a seismic elastic parameter volume to obtain an intermediate artificial crack model includes: constructing a first nonlinear categorical variable multivariate function relating the artificial crack scale to the elastic parameters using an iterative coupled regression method based on the seismic elastic parameter volume and known key parameters of the artificial crack, wherein the key parameters of the artificial crack also include the lengths of the cracks on both flanks; and reconstructing the scale of the artificial crack theoretical model using the first nonlinear categorical variable multivariate function to obtain the intermediate artificial crack model. For example, see [link to example]. Figure 6 ,in Figure 6 The diagram showing the comparison of the artificial crack size and Young's modulus before and after scale reconstruction in the embodiments of this application illustrates the relationship between the artificial crack size and Young's modulus before and after reconstruction.

[0082] In some embodiments, the multivariate function of the first nonlinear categorical variable is obtained according to the following formula:

[0083] L fr =a*E 2 +b*LM+c

[0084] H fr =a*R bi 2 +b*E+c

[0085] In the formula: L fr This indicates the length of the artificial crack, in meters (m); H fr The value represents the artificial crack height, in meters (m); E represents Young's modulus, in gigabytes of pressure (GPa); and LM represents Lamé's constant, in units of 10⁻⁶. 9 kg 2 / (m 4 ·s 2 ); R bi This represents the rock brittleness index, expressed as a percentage; a, b, and c represent region-related constants.

[0086] In some embodiments, the orientation correction of the intermediate artificial crack model based on the stress field parameter volume to obtain the corrected artificial crack theoretical model includes: constructing a second nonlinear categorical variable multivariate function relating the artificial crack orientation to the stress field parameters using an iterative coupled regression method based on the stress field parameter volume and known key parameters of the artificial crack; and using the second nonlinear categorical variable multivariate function to correct the orientation of the intermediate artificial crack model to obtain the corrected artificial crack theoretical model. For example, see [link to example]. Figure 7 ,in Figure 7 The diagram showing the superimposed comparison of the orientation of the artificial cracks and the direction of the maximum principal stress before and after orientation correction in the embodiments of this application can be seen, illustrating the relationship between the orientation of the artificial cracks and the direction of the maximum principal stress before and after earthquake correction.

[0087] In some embodiments, the multivariate function of the second nonlinear categorical variable is obtained according to the following formula:

[0088] A fr =a*PSD max 2 +b*PSD min +c

[0089] In the formula: A frThis indicates the location of the artificial crack, in degrees; PSD max This indicates the direction of the maximum principal stress, in degrees; PSD min This indicates the direction of the minimum principal stress, in degrees; a, b, and c represent region-specific constants.

[0090] Furthermore, after obtaining the artificial fracture prediction model for earthquakes, its reliability can be verified, and the predicted artificial fractures can be coupled with the natural fractures characterized by earthquake scales to form a complex underground fracture network system, such as... Figure 8 As shown, where Figure 8 The artificial crack prediction results are verified and tested using data such as production dynamics, based on the superimposed diagram of the earthquake prediction artificial crack, natural crack coupled crack network system and Young's modulus distribution provided in the embodiments of this application.

[0091] In practical applications, artificial fracture prediction was conducted in the S6 development area of ​​the Ordos Basin. Previous modeling methods using existing technologies failed to yield predictions due to their unidirectional fracture direction and stable fracture propagation size, which did not align with the complex oil-water seepage patterns observed in actual production. Consequently, these methods were not widely adopted in guiding development strategies. However, after applying the artificial fracture prediction method for reservoir development provided in this application, the accuracy of seismic predictions improved by 27 percentage points compared to the theoretical model, and it showed good matching with dynamic production data. Figure 9 As shown, where Figure 9 This application provides a comparative schematic diagram of the artificial fracture theoretical model, the seismic prediction artificial fracture model, and production data overlaid in its embodiments. Based on the development morphology of the artificial fractures and the distribution of the sand body, the oil and water seepage patterns of each well group are analyzed, and development measures adjustment suggestions are provided for three low-yield and inefficient wells: two wells are recommended for deblocking with surfactants, and one well is recommended for perforation repair. After implementing these measures, the average daily oil production of the three wells increased by 0.33 tons (daily production doubled), with a cumulative increase of 197.42 tons over six months. The adjustment was significantly effective, confirming that this invention can provide guiding suggestions for the study of seepage patterns in the mid-to-late stages of development and the effective adjustment of development measures.

[0092] Therefore, the method for predicting artificial fractures in reservoir development provided in this application first constructs a theoretical model of artificial fractures based on drilling and oil testing data of the target block. Then, based on seismic data of the target block, combined with well logging, well logging, and rock physics-related experimental parameters, a seismic elastic parameter volume and a stress field parameter volume are obtained to correct the theoretical model of artificial fractures. The corrected theoretical model of artificial fractures is then used as a seismic prediction model for artificial fractures to predict the distribution characteristics of artificial fractures generated by fracturing. Compared with the theoretical models of artificial fractures in related technologies, this seismic prediction model of artificial fractures, after correction by the seismic elastic parameter volume and stress field data volume, can better characterize the morphology of artificial fractures in actual formations, enabling precise prediction of artificial fractures and effectively guiding reservoir development and production.

[0093] On the other hand, see Figure 10 This application also provides a device 100 for predicting artificial fractures in reservoir development, comprising:

[0094] Module 1001 is used to construct a theoretical model of artificial fractures based on drilling and oil testing data of the target block.

[0095] The parameter body acquisition module 1002 is used to obtain the seismic elastic parameter body and stress field parameter body based on the seismic data of the target block and the well logging, well logging and rock physics related test parameter data of the target block;

[0096] The correction module 1003 is used to correct the artificial crack theoretical model based on the seismic elastic parameter volume and stress field parameter volume, and to use the corrected artificial crack theoretical model as the artificial crack model for earthquake prediction.

[0097] In some embodiments, drilling and well testing data include drilling core data, logging data, and fracturing data, and the construction module 1001 includes:

[0098] The acquisition unit is used to acquire the key parameters of known artificial fractures based on drilling core data, logging data and fracturing data. The key parameters include fracture length, fracture height, fracture aperture and orientation.

[0099] The simulation building unit is used to simulate and construct artificial cracks based on known key parameters of artificial cracks, thereby obtaining a theoretical model of artificial cracks.

[0100] In some embodiments, the parameter body obtaining module 1002 includes:

[0101] The acquisition unit is used to acquire particle aspect ratio and clay aspect ratio data based on well logging, well logging and rock physics related test parameter data of the target block;

[0102] The first determining unit is used to perform quantitative analysis and parameter testing on the particle aspect ratio and clay aspect ratio data in sequence to determine the intergranular structure of the target rock.

[0103] Building units are used to construct rock physics models based on the intergranular structure of the target rock.

[0104] The inversion unit is used to calculate elastic parameters based on a rock physics model and to invert the seismic elastic parameter volume using seismic data from the target block. The seismic elastic parameter volume is characterized using spatial grid data.

[0105] In some embodiments, the parameter body obtaining module 1002 further includes:

[0106] The obtained elements are used to obtain faults, stratigraphic thicknesses, and structural surfaces based on the seismic data and seismic elastic parameters of the target block;

[0107] The second determining unit is used to obtain the true stratigraphic trend surface curvature function based on seismic data of faults, stratigraphic thickness, structural surfaces, and target blocks;

[0108] The simulation unit is used to simulate the geostress field based on seismic elastic parameters and the curvature function of the actual stratum trend surface, and obtain the stress field parameter volume.

[0109] In some embodiments, the correction module 1003 includes:

[0110] The scale reconstruction unit is used to reconstruct the scale of the artificial crack theoretical model based on the seismic elastic parameter body to obtain the intermediate artificial crack model.

[0111] The orientation correction unit is used to correct the orientation of the intermediate artificial crack model based on the stress field parameter volume, so as to obtain the corrected artificial crack theoretical model.

[0112] In some embodiments, the scale reconfiguration unit includes:

[0113] The function constructs a sub-unit to build a multivariate function of the first nonlinear classification variable between the artificial crack size and the elastic parameters, based on the seismic elastic parameter volume and the known key parameters of the artificial crack.

[0114] The scale reconstruction subunit is used to reconstruct the scale of the artificial crack theoretical model using the multivariate function of the first nonlinear classification variable, so as to obtain the intermediate artificial crack model.

[0115] In some embodiments, the multivariate function of the first nonlinear categorical variable is obtained according to the following formula:

[0116] L fr =a*E 2 +b*LM+c

[0117] H fr =a*R bi 2 +b*E+c

[0118] In the formula: L fr This indicates the length of the artificial crack, in meters (m); H fr The value represents the artificial crack height, in meters (m); E represents Young's modulus, in gigabytes of pressure (GPa); and LM represents Lamé's constant, in units of 10⁻⁶. 9 kg 2 / (m 4 ·s 2 ); R bi This represents the rock brittleness index, expressed as a percentage; a, b, and c represent region-related constants.

[0119] In some embodiments, the orientation correction unit includes:

[0120] The function constructs a sub-unit to build a second nonlinear classification variable multivariate function between the orientation of artificial cracks and stress field parameters, based on the stress field parameter volume and the known key parameters of artificial cracks.

[0121] The orientation reconstruction sub-unit is used to perform orientation correction on the intermediate artificial crack model using the second nonlinear classification variable multivariate function, so as to obtain the corrected artificial crack theoretical model.

[0122] In some embodiments, the multivariate function of the second nonlinear categorical variable is obtained according to the following formula:

[0123] A fr =a*PSD max 2 +b*PSD min +c

[0124] In the formula: A fr This indicates the location of the artificial crack, in degrees; PSD max This indicates the direction of the maximum principal stress, in degrees; PSD min This indicates the direction of the minimum principal stress, in degrees; a, b, and c represent region-specific constants.

[0125] Therefore, the reservoir development artificial fracture prediction device provided in this application first constructs an artificial fracture theoretical model based on drilling and oil testing data of the target block. Then, based on seismic data of the target block, combined with well logging, well logging, and rock physics-related experimental parameter data, it obtains seismic elastic parameter volume and stress field parameter volume to correct the artificial fracture theoretical model. The corrected artificial fracture theoretical model is then used as a seismic prediction artificial fracture model to predict the distribution characteristics of artificial fractures generated by fracturing. Compared with artificial fracture theoretical models in related technologies, this seismic prediction artificial fracture model, after correction with seismic elastic parameter volume and stress field data volume, can better characterize the morphology of artificial fractures in actual formations, enabling precise prediction of artificial fractures and effectively guiding reservoir development and production.

[0126] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory including program code that can be executed by a processor in an electronic device to perform the method for predicting artificial fractures in reservoir development as described in the above embodiments. For example, the non-volatile computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0127] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0128] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "multiple" refers to two or more unless otherwise expressly defined.

[0129] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only.

[0130] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for predicting artificial fractures in oil reservoir development, characterized in that, include: Based on drilling and oil testing data of the target block, a theoretical model of artificial fractures was constructed. Based on the seismic data of the target block, combined with the well logging, well logging and rock physics related test parameter data of the target block, the seismic elastic parameter volume and stress field parameter volume are obtained; Based on the seismic elastic parameter body and the stress field parameter body, the artificial crack theoretical model is corrected, and the corrected artificial crack theoretical model is used as the artificial crack model for earthquake prediction.

2. The method for predicting artificial fractures in reservoir development according to claim 1, characterized in that, The drilling and testing data include drilling core data, logging data, and fracturing data. The construction of the artificial fracture theoretical model based on the drilling and testing data of the target block includes: Based on the drilling core data, the logging data, and the fracturing data, the key parameters of the known artificial fractures are obtained, including fracture length, fracture height, fracture aperture, and azimuth. Based on the known key parameters of the artificial crack, an artificial crack is simulated and constructed to obtain the theoretical model of the artificial crack.

3. The method for predicting artificial fractures in reservoir development according to claim 1, characterized in that, The process of obtaining the seismic elastic parameter volume and stress field parameter volume based on the seismic data of the target block, combined with the well logging, well logging, and rock physics-related test parameter data of the target block, includes: Based on the well logging, well logging and rock physics related test parameter data of the target block, obtain the particle aspect ratio and clay aspect ratio data; The particle aspect ratio and clay aspect ratio data were sequentially subjected to quantitative analysis and parameter testing to determine the intergranular structure of the target rock. Based on the intergranular structure of the target rock, a rock physics model is constructed; Based on the rock physics model, elastic parameters are calculated, and the seismic elastic parameter volume is obtained by inversion using seismic data of the target block, wherein the seismic elastic parameter volume is characterized using spatial grid data.

4. The method for predicting artificial fractures in reservoir development according to claim 3, characterized in that, The step of obtaining the seismic elastic parameter volume and stress field parameter volume based on the seismic data of the target block, combined with the well logging, well logging and rock physics related test parameter data of the target block, further includes: Based on the seismic data of the target block and the seismic elastic parameter volume, the fault, stratum thickness, and structural surface are obtained; Based on the fault, stratigraphic thickness, structural surface, and seismic data of the target block, the curvature function of the true stratigraphic trend surface is obtained; Based on the seismic elastic parameters and the curvature function of the actual stratum trend surface, the geostress field is simulated to obtain the stress field parameter volume.

5. The method for predicting artificial fractures in reservoir development according to claim 2, characterized in that, The correction of the artificial crack theoretical model based on the seismic elastic parameter body and the stress field parameter body includes: Based on the earthquake elastic parameter body, the artificial crack theoretical model is reconstructed on a scale to obtain an intermediate artificial crack model; Based on the stress field parameter volume, the orientation of the intermediate artificial crack model is corrected to obtain the corrected artificial crack theoretical model.

6. The method for predicting artificial fractures in reservoir development according to claim 5, characterized in that, The process of reconstructing the artificial crack theoretical model based on the seismic elastic parameter body to obtain an intermediate artificial crack model includes: Based on the earthquake elastic parameter volume and the known key parameters of artificial cracks, a first nonlinear classification variable multivariate function is constructed between the artificial crack size and the elastic parameters. Using the first nonlinear classification variable multivariate function, the artificial crack theoretical model is reconstructed to obtain the intermediate artificial crack model.

7. The method for predicting artificial fractures in reservoir development according to claim 6, characterized in that, The multivariate function of the first nonlinear categorical variable is obtained according to the following formula: L fr =a*E 2 +b*LM+c H fr =a*R bi 2 +b*E+c In the formula: L fr This indicates the length of the artificial crack, in meters (m); H fr The value represents the artificial crack height, in meters (m); E represents Young's modulus, in gigabytes of pressure (GPa); and LM represents Lamé's constant, in units of 10⁻⁶. 9 kg 2 / (m 4 ·s 2 ); R bi This represents the rock brittleness index, expressed as a percentage; a, b, and c represent region-related constants.

8. The method for predicting artificial fractures in reservoir development according to claim 5, characterized in that, The orientation correction of the intermediate artificial crack model based on the stress field parameter volume, to obtain the corrected artificial crack theoretical model, includes: Based on the stress field parameter volume and the known key parameters of the artificial crack, a second nonlinear classification variable multivariate function is constructed between the orientation of the artificial crack and the stress field parameters. Using the second nonlinear classification variable multivariate function, the intermediate artificial crack model is oriented for correction, resulting in the corrected artificial crack theoretical model.

9. The method for predicting artificial fractures in reservoir development according to claim 8, characterized in that, The second nonlinear categorical variable multivariate function is obtained according to the following formula: A fr =a*PSD max 2 +b*PSD min +c In the formula: A fr This indicates the location of the artificial crack, in degrees; PSD max This indicates the direction of the maximum principal stress, in degrees; PSD min This indicates the direction of the minimum principal stress, in degrees; a, b, and c represent region-specific constants.

10. A device for predicting artificial fractures in oil reservoir development, characterized in that, include: The module is used to build a theoretical model of artificial fractures based on drilling and testing data of the target block; The parameter body acquisition module is used to obtain the seismic elastic parameter body and stress field parameter body based on the seismic data of the target block, combined with the well logging data, well logging data and rock physics related test parameter data of the target block; The correction module is used to correct the artificial crack theoretical model based on the seismic elastic parameter body and the stress field parameter body, and to use the corrected artificial crack theoretical model as the artificial crack model for earthquake prediction.

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