Crack distribution condition determination method and device, storage medium and electronic device

Abstract

The invention discloses a crack distribution condition determination method and device, a storage medium, an electronic device and a computer program product. The method comprises the following steps: acquiring logging data of a target area; according to the logging data, a mechanical model is constructed, and the mechanical model is used for indicating the geomechanical condition of the target area; and simulating the geological change condition of the target area under the preset environment condition according to the mechanical model to determine the crack distribution condition of the target area. The geological change conditions of the target area under different environment conditions are simulated, and then the crack distribution condition of the target area is accurately determined through the established mechanical model. According to the method, the accuracy of crack prediction is improved, and the change trend of the crack distribution condition can be dynamically analyzed.

Description

Technical Field

[0001] The present application relates to the field of geology, and more specifically, to a method and device for determining fracture distribution, a storage medium, an electronic device, and a computer program product. Background Art

[0002] In the field of geological exploration and oil and gas production, accurately evaluating the distribution of underground fractures is crucial to understanding reservoir characteristics, predicting oil and gas flow paths, and optimizing production operations. Usually, natural resources such as oil, natural gas or water can be produced through formation fractures, and formation fractures are the storage space of natural resources. Therefore, the distribution of fractures is crucial to evaluating reservoir characteristics and improving production efficiency.

[0003] In the related technology, the fracture situation is mainly determined by relying on geological data and empirical judgment, such as seismic data, core data, etc.

[0004] However, traditional fracture distribution assessment methods have limitations and uncertainties, which limit the accuracy and reliability of fracture assessment, thus affecting the scientific nature and economic benefits of geological decision-making. Summary of the invention

[0005] Embodiments of the present application provide a method and device for determining crack distribution, a storage medium, an electronic device, and a computer program product.

[0006] According to one aspect of an embodiment of the present application, a method for determining the distribution of fractures is provided, comprising: acquiring logging data of a target area; constructing a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area; and simulating the geological changes of the target area under preset environmental conditions based on the mechanical model to determine the distribution of fractures in the target area.

[0007] In an exemplary embodiment, a mechanical model is constructed based on logging data, including: determining characteristic parameters related to the mechanical properties of rocks in the logging data; determining a mechanical model framework based on the types of characteristic parameters; and setting parameters of the mechanical model framework to construct a mechanical model based on the geological conditions of the target area, the relationship between the characteristic parameters and the mechanical properties of rocks.

[0008] In an exemplary embodiment, the characteristic parameters include acoustic wave time difference and resistivity. According to the geological conditions of the target area, the characteristic parameters and the relationship between the mechanical properties of the rock, the parameters of the mechanical model framework are set to construct a mechanical model, including: determining the elastic parameters of the rock in the target area according to the acoustic wave time difference; determining the conductivity of the rock in the target area according to the resistivity; inputting the elastic parameters, conductivity and geological conditions into the mechanical model framework as the basic parameters of the mechanical model framework to construct the mechanical model.

[0009] In an exemplary embodiment, geological changes in a target area under preset environmental conditions are simulated according to a mechanical model to determine the distribution of cracks in the target area, including: inputting the preset environmental conditions into the mechanical model to simulate the geological changes in the target area under the preset environmental conditions; analyzing the geological changes to determine the location, size, and density of cracks generated in the target area; and determining the distribution of cracks in the target area according to the location, size, and density of cracks generated in the target area.

[0010] In an exemplary embodiment, after simulating the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the crack distribution in the target area, the method also includes: obtaining the actual crack distribution in the target area; determining whether the accuracy of the mechanical model meets the standards based on the actual crack distribution and the determined crack distribution; if it is determined that the accuracy of the mechanical model does not meet the standards, adjusting the parameters of the mechanical model until it is determined that the accuracy of the mechanical model meets the standards.

[0011] In an exemplary embodiment, the method further includes: determining the crack distribution in the target area according to the adjusted mechanical model; and determining the change trend of the crack distribution according to the crack distribution in the target area within a preset time period.

[0012] Another aspect of the present application provides a device for determining the distribution of fractures, including: a data acquisition module for acquiring logging data of a target area; a model construction module for constructing a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geological mechanical conditions of the target area; and a fracture condition determination module for simulating the geological changes of the target area under preset environmental conditions based on the mechanical model to determine the distribution of fractures in the target area.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the crack distribution when running.

[0014] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the crack distribution through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0016] The above-mentioned method for determining the distribution of fractures can construct an accurate mechanical model of the target area based on logging data. By constructing a mechanical model, the mechanical properties of the rock and the fracture state can be linked to the geological mechanical conditions, providing a theoretical framework for simulating geological changes and improving the scientificity and accuracy of fracture distribution prediction. Then, the geological changes in the target area under different environmental conditions can be simulated to accurately determine the fracture distribution in the target area. This method not only improves the accuracy of fracture prediction, but also can dynamically analyze the changing trend of fracture distribution, providing a scientific basis for decision-making in the fields of geological exploration, oil and gas extraction, and effectively improving the efficiency and safety of resource extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 is a hardware structure block diagram of a method for determining crack distribution conditions according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0021] Figure 3 This is a second flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0022] Figure 4 is a third flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0023] Figure 5 This is a fourth flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0024] Figure 6 is a fifth flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0025] Figure 7 is a sixth flow chart of a method for determining crack distribution according to an embodiment of the present application;

[0026] Figure 8It is a structural block diagram of a device for determining crack distribution according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0029] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal for determining the crack distribution of an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmable logic device, referred to as PLD)) and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0030] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the crack distribution in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] In this embodiment, a method for determining crack distribution is provided. Figure 2 is a flow chart of an optional method for determining crack distribution according to an embodiment of the present application, the process comprising the following steps S200-S220:

[0033] Step S200, obtaining well logging data of the target area.

[0034] Specifically, obtaining logging data in the target area is the basis for evaluating fracture distribution and building mechanical models. Logging technology can obtain a series of physical property information of underground formations during drilling, including acoustic wave time difference, resistivity, density, neutron porosity, etc. These data are crucial for analyzing the mechanical properties and fracture status of rocks.

[0035] For example, logging instruments are deployed in the wells of the target area to record the formation response at different depths. Instruments include but are not limited to sonic logging instruments, resistivity logging instruments, density logging instruments, and neutron logging instruments, which can measure the physical properties of the formation in real time or after the fact. After the data is collected, it is pre-processed to eliminate outliers and measurement errors to ensure the accuracy and reliability of the data.

[0036] Step S210, constructing a mechanical model based on the logging data.

[0037] Among them, the mechanical model is used to indicate the geomechanical conditions of the target area.

[0038] Specifically, building a mechanical model is a key step in converting logging data into geomechanical understanding and fracture prediction. The model not only reflects the physical and mechanical properties of rocks, but also considers the impact of fractures on the stress-strain behavior of rocks, providing a scientific basis for predicting geological changes.

[0039] For example, first, the key mechanical properties of the rock, such as elastic modulus, Poisson's ratio and electrical conductivity, are calculated based on the logging data. Then, a suitable mechanical model, such as an elastic-electric coupling model or a fracture mechanics model, is selected or developed, and the model parameters are set. On this basis, a three-dimensional geological model is created using numerical simulation software (such as COMSOL, ABAQUS, etc.), and the rock properties and fracture states are mapped into the model to construct a mechanical model that can reflect the geomechanical conditions of the target area.

[0040] Step S220, simulating geological changes in the target area under preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area.

[0041] Specifically, simulating geological changes is the core of predicting fracture distribution based on mechanical models. Through simulation, we can observe the generation, expansion and redistribution of fractures under different environmental conditions (such as tectonic stress changes, fluid injection / production, etc.), as well as the impact on reservoir connectivity.

[0042] For example, based on the mechanical model constructed in step 2, preset environmental conditions are set, such as formation pressure, temperature, tectonic stress, etc. Dynamic simulation is performed using numerical simulation software to simulate rock stress, strain, displacement and fluid flow under these conditions. From the simulation results, the generation and expansion behavior of cracks, as well as the impact of cracks on rock porosity, permeability and connectivity are analyzed to determine the distribution of cracks.

[0043] In this embodiment, an accurate mechanical model of the target area can be constructed based on the logging data. By constructing the mechanical model, the mechanical properties and crack state of the rock can be linked to the geomechanical conditions, providing a theoretical framework for simulating geological changes and improving the scientificity and accuracy of fracture distribution prediction. Then, the geological changes in the target area under different environmental conditions are simulated, so as to accurately determine the fracture distribution in the target area. This method not only improves the accuracy of fracture prediction, but also can dynamically analyze the changing trend of fracture distribution, providing a scientific basis for decision-making in the fields of geological exploration, oil and gas extraction, and effectively improving the efficiency and safety of resource extraction.

[0044] In one embodiment, Figure 3 As shown, step S210, constructing a mechanical model based on well logging data. It includes: steps S300-S320:

[0045] Step S300, determining characteristic parameters related to the mechanical properties of rocks in the logging data.

[0046] Specifically, the mechanical properties of rocks, such as elastic modulus, Poisson's ratio, compressive strength, etc., are key to evaluating fracture distribution and dynamic behavior. Well logging data contains information related to these mechanical properties, such as acoustic wave delay, resistivity, density, etc. These parameters can be converted into mechanical properties through specific rock physics models.

[0047] Exemplarily, by analyzing the logging data, characteristic parameters directly or indirectly related to the mechanical properties of the rock are identified. For example, acoustic time difference data can be used to calculate the elastic modulus of the rock; resistivity data can reflect the porosity and fluid saturation of the rock, indirectly affecting the mechanical strength of the rock. Use data processing software such as Matlab or Python libraries (such as pandas and numpy) to perform data cleaning and feature extraction. Then, apply rock physics models such as Dvorkin and Mavko, convert the acoustic time difference into Young's modulus and Poisson's ratio, convert the resistivity into porosity and fluid saturation, and finally obtain characteristic parameters related to the mechanical properties of the rock.

[0048] Step S310, determining the mechanical model framework according to the type of characteristic parameters.

[0049] Specifically, different characteristic parameters are suitable for describing different types of dynamic behaviors. Therefore, it is crucial to select an appropriate mechanical model framework based on the type of characteristic parameters. For example, acoustic time difference and resistivity data may point to the elastic behavior and fluid dynamic behavior of the rock, respectively, and a model framework that can comprehensively consider these behaviors is required.

[0050] Exemplarily, based on the type of characteristic parameters extracted in step one, select an appropriate mechanical model framework. For example, if the characteristic parameters include the elastic modulus and Poisson's ratio of the rock, an elastoplastic model framework can be selected. If resistivity and fluid saturation are also included, an elastoplastic coupling model framework can be further considered. Consult relevant literature to understand the applicable conditions and parameter setting methods of the model framework. Use professional software (such as COMSOL or ABAQUS) or self-written programs (such as using Python or Fortran) to build a model framework to ensure that the model can accurately reflect the rock mechanical behavior and fluid dynamics process in the target area.

[0051] Step S320, setting parameters of the mechanical model framework and constructing a mechanical model according to the geological conditions of the target area, the relationship between the characteristic parameters and the mechanical properties of the rock.

[0052] Specifically, the parameters of the mechanical model need to be set according to the geological conditions and rock mechanical characteristics of the target area to ensure that the model can accurately reflect the actual geomechanical conditions.

[0053] Exemplarily, geological conditions of the target area, such as stratigraphic structure, tectonic stress, fluid pressure, etc., are determined with reference to geological research reports and logging data. Combined with the characteristic parameters of rock mechanical properties obtained in step 1, each parameter in the mechanical model framework (such as elastic modulus, crack density, porosity, fluid saturation) is assigned a value. If the model framework needs to consider multiphase fluid flow, fluid property data (such as viscosity, density) must also be collected. Use numerical simulation software, such as OpenGeoS or self-written C++ or Fortran programs, input the set parameters, and construct a mechanical model.

[0054] In this embodiment, by determining the characteristic parameters, the original logging data is effectively converted into information directly related to the mechanical properties of the rock, providing the necessary input parameters for the construction of the subsequent mechanical model, and improving the accuracy of the model prediction. By selecting the mechanical model framework in a targeted manner, a model that matches the rock mechanical properties and geological conditions of the target area can be constructed, thereby improving the accuracy and reliability of the fracture distribution prediction. Through the precise setting of parameters, the constructed mechanical model can more realistically simulate the rock mechanical behavior and fluid dynamics process of the target area, thereby improving the scientificity and practicality of the fracture distribution prediction and providing strong technical support for geological decision-making. This process can also reveal the complex relationship between rock mechanical properties and fracture generation and expansion, and promote theoretical research and technological progress in the field of geology.

[0055] In one embodiment, Figure 4 As shown, step S320 sets the parameters of the mechanical model framework and constructs the mechanical model according to the geological conditions of the target area, the relationship between the characteristic parameters and the mechanical properties of the rock. It includes: steps S400-S420:

[0056] Step S400, determining the elastic parameters of the rock in the target area according to the acoustic wave time difference.

[0057] Specifically, the acoustic time difference is an important parameter in rock physics logging. It reflects the speed of sound wave propagation in the rock and is related to the elasticity and porosity of the rock. By analyzing the acoustic time difference, the elastic parameters of the rock can be derived, including Young's modulus (E) and Poisson's ratio (μ), which are the basis for building a mechanical model.

[0058] Exemplarily, the acoustic time difference data obtained from logging is input into a rock physics model, such as the Dvorkin and Mavko models. Through mathematical transformation, the acoustic time difference is converted into Young's modulus and Poisson's ratio of the rock. This process may require iterative calculations and error correction to ensure the accuracy and reliability of the results. Data processing and model calculations are performed using Matlab, Python or specialized rock physics software (such as GeoFrame).

[0059] Step S410, determining the conductivity of the rock in the target area according to the resistivity.

[0060] Among them, the characteristic parameters include acoustic wave time difference and resistivity.

[0061] Specifically, resistivity is another important parameter in logging data, which is related to the porosity, fluid saturation and electrical conductivity of the rock. Electrical conductivity is a physical parameter that describes the ability of rocks to conduct electricity. In porous media, electrical conductivity is closely related to the pore structure of the rock and the properties of the fluid.

[0062] For example, the resistivity data is converted into the conductivity of the rock using the Archie formula or its variant. This conversion needs to take into account the porosity, fluid saturation, and dielectric constant and resistivity index factors of the formation. Data preprocessing is performed using professional software (such as LasViewer or WellExplorer), and then the Archie formula is applied for calculation to obtain the conductivity value of the rock.

[0063] Step S420, inputting elastic parameters, electrical conductivity, and geological conditions into the mechanical model framework as basic parameters of the mechanical model framework to construct a mechanical model.

[0064] Specifically, the construction of mechanical models requires combining the physical and mechanical parameters of rocks with geological conditions to reflect the actual geomechanical environment of the target area. The selection of the model framework and the setting of parameters directly determine the accuracy and applicability of the model.

[0065] For example, a mechanical model framework that can comprehensively consider rock elastic parameters and electrical conductivity is selected, such as an elastic-electric coupling model. The Young's modulus, Poisson's ratio, and electrical conductivity data obtained in steps one and two, together with the geological conditions of the target area (such as tectonic stress, fluid pressure, temperature, etc.) are used as basic inputs to construct a mechanical model using professional numerical simulation software (such as COMSOL Multiphysics or ABAQUS) or a self-written program (such as using Python or MATLAB). During the model construction process, it is necessary to ensure that the physical meaning and dimensions of the parameters are consistent to facilitate the operation of the model and the interpretation of the results.

[0066] In this embodiment, the elastic parameters of the rock in the target area are accurately determined, which provides a solid physical basis for the mechanical model constructed subsequently and improves the accuracy of the model in predicting the distribution of cracks and the mechanical behavior of rocks. The electrical conductivity of the rock is determined by resistivity, which provides key parameters for constructing a mechanical model including the conductivity effect, and helps to more comprehensively evaluate the geological conditions and the fluid dynamic behavior of the rock. By integrating the elastic parameters, electrical conductivity and geological conditions of the rock, the mechanical model constructed can more accurately simulate the mechanical behavior and fluid dynamic process of the rock. This model can not only predict the distribution and dynamic changes of cracks, but also evaluate the impact of cracks on the physical properties of the rock and the connectivity of the reservoir, providing a scientific decision-making basis for geological exploration, oil and gas production and underground engineering design. Through more comprehensive parameter input, the credibility and practicality of the model prediction results have been significantly improved, promoting technological progress in the field of geoscience and the optimization of resource development.

[0067] In one embodiment, Figure 5 As shown, step S220 simulates the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area. It includes: steps S500-S520:

[0068] Step S500: input the preset environmental conditions into the mechanical model to simulate the geological changes in the target area under the preset environmental conditions.

[0069] Specifically, the preset environmental conditions usually include formation pressure, temperature, tectonic stress, and fluid injection / extraction. Changes in these conditions will directly affect the stress state of the rock and fluid flow, thereby affecting the generation and distribution of cracks. By inputting these environmental conditions into the mechanical model, the impact of geological changes on cracks can be simulated.

[0070] Exemplarily, the preset environmental conditions are input into the mechanical model constructed in step three in the form of parameters. For example, in COMSOL Multiphysics software, the "Solid Mechanics" module and the "Fluid Dynamics" module can be used to set the formation pressure, temperature distribution, tectonic stress field, etc. Through numerical simulation, the response of the model under these conditions is observed, including the strain and displacement of the rock and the flow distribution of the fluid. Special attention is paid to the stress concentration and fluid flow in the fracture area, which is crucial for understanding the generation and expansion of the fracture.

[0071] Step S510, analyzing geological changes to determine the location, size, and density of cracks generated in the target area.

[0072] Specifically, the analysis of simulation results is a key step in determining the distribution of fractures. It is necessary to extract fracture characteristic parameters from the simulation results, including the location, size and density of fractures, which reflect the distribution characteristics of fractures and their impact on reservoir connectivity.

[0073] For example, the stress distribution map, displacement field and fluid flow path in the simulation results are analyzed to identify the location of cracks. Usually, cracks are formed in stress concentration areas or fluid flow paths. By observing the stress and fluid flow data in the crack area, the size (such as the length and width of the crack) and density (the ratio of the number of cracks to the area) of the cracks are estimated. Image processing techniques such as edge detection and threshold segmentation can be used to automatically identify and quantify crack features from the three-dimensional data of stress and fluid flow fields.

[0074] Step S520, determining the crack distribution in the target area according to the position, size and density of the cracks generated in the target area.

[0075] Specifically, the determination of fracture distribution is the basis for evaluating geological conditions and formulating mining strategies. By comprehensively considering the location, size and density of fractures, a fracture distribution map of the target area can be drawn.

[0076] For example, based on the fracture characteristic parameters obtained in step 2, a fracture distribution map is created using GIS (geographic information system) software or a self-written Python script. In the map, different colors or legends are used to represent fractures of different sizes and densities, as well as their spatial distribution in the target area. In addition, the connectivity indicators of the fractures, such as fracture connectivity and fracture network density, can also be calculated to further evaluate the impact of fractures on reservoir performance.

[0077] In this embodiment, by simulating geological changes under preset environmental conditions, the dynamic behavior of cracks can be observed, including the generation of cracks, the direction of expansion and the flow of fluids in the cracks, providing intuitive simulation results for the subsequent evaluation of the distribution of cracks. By analyzing the simulation results, the distribution characteristics of cracks can be quantified, including the location, size and density of cracks, which are of great significance for evaluating reservoir connectivity and oil and gas production potential. The final determined crack distribution can not only intuitively display the crack distribution characteristics of the target area, but also provide a scientific basis for geological decision-making. By accurately describing the location, size and density of crack generation, the oil and gas production strategy can be optimized, the recoverability of resources can be improved, and the risks and uncertainties in the production process can be reduced. By inputting preset environmental conditions for simulation, analyzing the crack characteristic parameters in the simulation results, and finally determining the crack distribution, this series of steps constitutes a complete crack distribution evaluation process. This process uses mechanical models and numerical simulation technology to overcome the limitations and uncertainties of traditional evaluation methods and provide more accurate and reliable crack distribution information for geological exploration and oil and gas production. The effect of this technology is that it improves the scientificity and efficiency of geological decision-making, helps optimize resource development strategies, reduces mining risks, and promotes the advancement of geological science and oil and gas extraction technology.

[0078] In one embodiment, Figure 6 As shown, in step S220, after simulating the geological changes of the target area under the preset environmental conditions according to the mechanical model to determine the crack distribution in the target area, the method further includes steps S600-S620:

[0079] Step S600, obtaining the actual crack distribution in the target area.

[0080] Specifically, the acquisition of actual crack distribution is to verify the prediction results of the mechanical model and ensure the accuracy and effectiveness of the model. This usually involves comprehensive analysis of multi-source data such as ground, drilling and logging.

[0081] For example, ground geological survey data of the target area are collected, including geological maps, surface fracture observation records, etc. These data can provide macroscopic fracture distribution information. By drilling cores, laboratory analysis, such as X-ray tomography, CT scanning and thin section analysis, can be carried out to obtain detailed information on fractures inside the rock. Fracture identification logging tools, such as imaging logging and microseismic monitoring, are used to extract fracture characteristics from logging data and conduct depth detection of fracture distribution. Geological experts interpret the above data and use GIS software or self-compiled programs to integrate various data into a unified fracture distribution map as a benchmark for evaluating the accuracy of the mechanical model.

[0082] Step S610, determining whether the accuracy of the mechanical model meets the requirements based on the actual crack distribution and the determined crack distribution.

[0083] Specifically, the accuracy of the model is verified by comparing the actual fracture distribution with the model prediction results to evaluate the utility and applicability of the model.

[0084] For example, the actual crack distribution map obtained in step 1 is compared with the crack distribution map determined in step 3 to evaluate the consistency of crack location, size and density. Statistical methods, such as root mean square error (RMSE) and correlation coefficient (R^2), are used to quantify the difference between the model prediction results and the actual crack distribution to determine whether the accuracy of the model meets the preset standards. Invite geological experts to review the model prediction results to evaluate the model's ability to explain complex geological phenomena, as well as the scientificity and credibility of the prediction results.

[0085] Step S620: When it is determined that the accuracy of the mechanical model does not meet the standard, adjust the parameters of the mechanical model until it is determined that the accuracy of the mechanical model meets the standard.

[0086] Specifically, the adjustment of model parameters is to improve the accuracy of model prediction so that it can more accurately reflect the distribution of fractures under actual geological conditions.

[0087] For example, first, perform a parameter sensitivity analysis to determine which parameters have the greatest impact on the model prediction results, such as the elastic modulus, Poisson's ratio, electrical conductivity, tectonic stress, etc. of the rock. Based on the results of the sensitivity analysis, adjust the values ​​of key parameters. Methods such as orthogonal design, genetic algorithm, or Bayesian optimization can be used to find parameter combinations that can improve the accuracy of the model. After adjusting the parameters, rerun the mechanical model and compare it with the actual crack distribution to evaluate the improvement effect of the model. According to the verification results, it may be necessary to iterate the parameters multiple times until the crack distribution predicted by the model meets the actual requirements and meets the preset standards.

[0088] In this embodiment, by comparing with the actual crack distribution, the model parameters are continuously adjusted and optimized, and finally the accuracy of the model in predicting crack distribution is ensured to meet the standards. This process improves the scientificity and efficiency of geological model construction, provides accurate crack distribution information for geological exploration, oil and gas production and underground engineering, and helps to optimize resource development strategies, reduce geological risks and improve mining efficiency. At the same time, through the continuous optimization of the model, the development of geological science and technology has been promoted, and the overall scientific research capabilities and technical level of the industry have been improved.

[0089] In one embodiment, Figure 7 As shown, the method further includes: steps S700-S710:

[0090] Step S700, determining the crack distribution in the target area according to the adjusted mechanical model.

[0091] Specifically, after the model parameters are optimized and adjusted, the mechanical model can more accurately reflect the geomechanical environment of the target area. At this time, the model needs to be run again to predict the distribution of cracks in order to obtain more accurate prediction results.

[0092] Exemplarily, the parameters adjusted and verified in step 3 are input into the mechanical model framework to ensure that the physical environment and geological conditions of the model are consistent with the actual conditions of the target area. Use professional numerical simulation software (such as COMSOL or ABAQUS) or self-written programs (such as Python, MATLAB, etc.) to run the adjusted model to simulate the geomechanical response of the target area under preset environmental conditions, focusing on the generation, expansion and redistribution of cracks. After the model runs, the predicted results of the fracture distribution are output, including characteristic parameters such as the location, size and density of the fractures, as well as the effects of the fractures on the mechanical properties of the rock and the flow of the fluid.

[0093] Step S710, determining a change trend of crack distribution according to crack distribution in a target area within a preset time period.

[0094] Specifically, over time, geological conditions and mining activities can lead to the generation, expansion, closure or redistribution of fractures, a dynamic process that reflects the changing trend of fracture distribution. Determining the changing trend of fracture distribution is of great significance for evaluating reservoir performance and optimizing mining strategies.

[0095] For example, a series of preset time periods are set, for example, every 3 months is a time period, and the optimized mechanical model is used to predict the crack distribution in each time period. The crack distribution prediction results of each time period are compared and analyzed to identify the changes in cracks in different time periods, including the increase or decrease in the number of cracks, changes in size, and expansion or contraction of the distribution range. Time series analysis methods such as trend line fitting and autoregressive model (AR model) are used to evaluate the trend of crack distribution over time and predict the possibility of future crack distribution.

[0096] For example, logging operations can be performed in the target area to collect acoustic transit time and resistivity data. Based on the acoustic transit time and resistivity data, a mechanical model that takes into account changes in rock elasticity and conductivity can be established. Multi-parameter logging operations can also be performed in the target area to collect data such as acoustic transit time, resistivity, gamma rays, density, and neutron porosity. Based on the multi-parameter data, a mechanical model that comprehensively considers the physical properties of the rock can be established.

[0097] In this embodiment, the optimized mechanical model is combined to predict the distribution of cracks, and the trend of crack distribution changes within a preset time period is analyzed, which constitutes a systematic method for dynamically evaluating the impact of geological changes on cracks. This method improves the accuracy of crack distribution prediction, enhances the scientificity and foresight of crack change trend assessment, and provides strong technical support for decision-making in geological exploration, oil and gas production, and underground engineering. It not only optimizes resource development strategies and reduces geological risks, but also promotes in-depth understanding and research of crack dynamics in the field of geological science, and promotes the advancement and application of industry technology.

[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0099] In this embodiment, a device for determining crack distribution is also provided, and the device for determining crack distribution is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0100] Figure 8 is a structural block diagram of an optional device for determining crack distribution according to an embodiment of the present application. Figure 8 As shown, including:

[0101] The data acquisition module 801 is used to acquire the well logging data of the target area.

[0102] The model building module 802 is used to build a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area.

[0103] The fracture condition determination module 803 is used to simulate the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the fracture distribution of the target area.

[0104] Through the above device, an accurate mechanical model of the target area can be constructed based on the logging data. By constructing the mechanical model, the mechanical properties and crack state of the rock can be linked to the geomechanical conditions, providing a theoretical framework for simulating geological changes and improving the scientificity and accuracy of fracture distribution prediction. Then, the geological changes of the target area under different environmental conditions can be simulated to accurately determine the fracture distribution in the target area. This method not only improves the accuracy of fracture prediction, but also can dynamically analyze the changing trend of fracture distribution, providing a scientific basis for decision-making in the fields of geological exploration, oil and gas extraction, and effectively improving the efficiency and safety of resource extraction.

[0105] In an exemplary embodiment, the model building module 802 is also used to determine characteristic parameters related to the mechanical properties of rocks in the logging data. According to the type of characteristic parameters, the mechanical model framework is determined. According to the geological conditions of the target area, the relationship between the characteristic parameters and the mechanical properties of the rocks, the parameters of the mechanical model framework are set to build the mechanical model.

[0106] In an exemplary embodiment, the model building module 802 is also used to determine the elastic parameters of the rock in the target area according to the acoustic wave time difference. The electrical conductivity of the rock in the target area is determined according to the resistivity. The elastic parameters, electrical conductivity, and geological conditions are input into the mechanical model framework as basic parameters of the mechanical model framework to build the mechanical model.

[0107] In an exemplary embodiment, the crack condition determination module 803 is also used to input the preset environmental conditions into the mechanical model to simulate the geological changes in the target area under the preset environmental conditions. The geological changes are analyzed to determine the location, size, and density of the cracks generated in the target area. According to the location, size, and density of the cracks generated in the target area, the crack distribution in the target area is determined.

[0108] In an exemplary embodiment, the above device further includes:

[0109] The crack condition acquisition module is used to obtain the actual crack distribution in the target area.

[0110] The model verification module is used to determine whether the accuracy of the mechanical model meets the requirements based on the actual crack distribution and the determined crack distribution.

[0111] The model optimization module is used to adjust the parameters of the mechanical model when it is determined that the accuracy of the mechanical model does not meet the standard, until it is determined that the accuracy of the mechanical model meets the standard.

[0112] In an exemplary embodiment, the above device further includes:

[0113] The crack condition determination module is used to determine the crack distribution condition of the target area according to the adjusted mechanical model.

[0114] The crack trend acquisition module is used to determine the change trend of the crack distribution according to the crack distribution of the target area within a preset time period.

[0115] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0116] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0117] S1, obtain the logging data of the target area.

[0118] S2, constructing a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area.

[0119] S3, simulating the geological changes of the target area under the preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area.

[0120] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0121] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0122] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0123] S1, obtain the logging data of the target area.

[0124] S2, constructing a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area.

[0125] S3, simulating the geological changes of the target area under the preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area.

[0126] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0127] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores the computer program product, and when the computer program is executed by a processor, the steps of the method in each embodiment of the present application are implemented.

[0128] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0129] S1, obtain the logging data of the target area.

[0130] S2, constructing a mechanical model based on the logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area.

[0131] S3, simulating the geological changes of the target area under the preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area.

[0132] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0133] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0134] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining crack distribution, characterized in that: The method comprises: Obtain well logging data for the target area; Constructing a mechanical model based on the well logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area; The geological changes of the target area under preset environmental conditions are simulated according to the mechanical model to determine the distribution of cracks in the target area.

2. The method for determining crack distribution according to claim 1, characterized in that: The step of constructing a mechanical model according to the well logging data comprises: Determining characteristic parameters related to mechanical properties of rock in the well logging data; Determining a mechanical model framework according to the type of the characteristic parameter; According to the geological conditions of the target area and the relationship between the characteristic parameters and the mechanical properties of the rock, the parameters of the mechanical model framework are set to construct the mechanical model.

3. The method for determining crack distribution according to claim 2, characterized in that: The characteristic parameters include acoustic wave time difference and resistivity. The parameters of the mechanical model framework are set according to the geological conditions of the target area, the relationship between the characteristic parameters and the mechanical properties of the rock, and the mechanical model is constructed, including: Determining elastic parameters of rocks in the target area according to the acoustic wave time difference; Determining the electrical conductivity of the rock in the target area according to the resistivity; The elastic parameters, the electrical conductivity, and the geological conditions are input into the mechanical model framework as basic parameters of the mechanical model framework to construct the mechanical model.

4. The method for determining crack distribution according to claim 1, characterized in that: The simulating the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area includes: Inputting the preset environmental conditions into the mechanical model to simulate the geological changes of the target area under the preset environmental conditions; Analyze the geological changes to determine the location, size, and density of cracks generated in the target area; The crack distribution of the target area is determined according to the position, size and density of the cracks generated in the target area.

5. The method for determining crack distribution according to claim 1, characterized in that: After simulating the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the distribution of cracks in the target area, the method further includes: Obtaining actual crack distribution in the target area; Determining whether the accuracy of the mechanical model meets the standard according to the actual crack distribution and the determined crack distribution; When it is determined that the accuracy of the mechanical model does not meet the standard, the parameters of the mechanical model are adjusted until it is determined that the accuracy of the mechanical model meets the standard.

6. The method for determining crack distribution according to claim 5, characterized in that: The method further comprises: Determining the crack distribution in the target area according to the adjusted mechanical model; According to the crack distribution in the target area within a preset time period, a change trend of the crack distribution is determined.

7. A device for determining crack distribution, characterized in that: The device comprises: A data acquisition module, used to acquire well logging data of a target area; A model building module, used to build a mechanical model based on the well logging data, wherein the mechanical model is used to indicate the geomechanical conditions of the target area; The crack condition determination module is used to simulate the geological changes of the target area under preset environmental conditions according to the mechanical model to determine the crack distribution of the target area.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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