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

Abstract

The invention discloses a crack condition determination method and device, a storage medium, an electronic device and a computer program product. The method comprises the following steps: acquiring geological profile data of a to-be-detected area; determining a preliminary crack distribution condition of the to-be-measured area according to the geological profile data; according to the initial fracture distribution condition, core sampling data of a target area is collected, and the target area is a fracture distribution area indicated by the initial fracture distribution condition; according to the core sampling data, the crack position and the feature information of the target area are determined, and the crack position and the feature information serve as the target crack condition. Preliminary geological profile data analysis provides a wide view angle of crack distribution, rock core sampling data supplements detailed information of specific positions and characteristics of cracks, and reliability and precision of crack evaluation are remarkably improved through combination of the two.

Description

Technical Field

[0001] The present application relates to the field of geology, and more specifically, to a method and device for determining fracture conditions, 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, the distribution of fractures is of great significance to the development of oil and gas reservoirs. 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 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 a crack condition, 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 a fracture condition is provided, the method comprising: acquiring geological profile data of a region to be tested; determining preliminary fracture distribution conditions of the region to be tested based on the geological profile data; collecting core sampling data of a target region based on the preliminary fracture distribution conditions, wherein the target region is a fracture distribution region indicated by the preliminary fracture distribution conditions; determining fracture positions and characteristic information of the target region based on the core sampling data, the fracture positions and characteristic information being used as target fracture conditions.

[0007] In an exemplary embodiment, the preliminary crack distribution of the area to be tested is determined based on geological profile data, including: determining abnormal signal conditions corresponding to each sub-area of ​​the area to be tested, wherein the abnormal signal is a signal related to cracks; determining the probability of cracks occurring in each sub-area; determining sub-areas in each sub-area where the probability of cracks occurring is higher than a preset threshold as target areas; and determining preliminary crack distribution conditions based on the distribution of target areas in the area to be tested.

[0008] In an exemplary embodiment, after determining the fracture location and characteristic information of the target area based on the core sampling data, the method further includes: supplementing the preliminary fracture distribution of the target area based on the fracture location and characteristic information of the target area to determine the target fracture distribution of the target area.

[0009] In an exemplary embodiment, the method further includes: constructing a sample set based on crack locations, feature information, and target crack distribution conditions in a plurality of different target areas; using the sample set to train a preset neural network model to construct a crack distribution prediction model, wherein the crack distribution prediction model is used to determine the crack distribution conditions based on the crack locations and feature information.

[0010] In an exemplary embodiment, the method further includes: acquiring core sampling data of the target area at different time points; determining the crack distribution of the target area at different time points based on the core sampling data of the target area at different time points and a crack distribution prediction model; and determining the crack distribution change trend of the target area based on the crack distribution of the target area at different time points.

[0011] In an exemplary embodiment, the method further includes: acquiring geological characteristics of the target area; determining the relationship between the crack distribution change trend and the geological characteristics of the target area based on the geological characteristics and the crack distribution change trend of the target area; and determining the target address characteristics that affect the crack distribution change trend based on the relationship between the crack distribution change trend and the geological characteristics of the target area.

[0012] Another aspect of the present application provides a device for determining a fracture condition, comprising: a data acquisition module for acquiring geological profile data of a region to be tested; a preliminary distribution determination module for determining a preliminary fracture distribution condition of the region to be tested based on the geological profile data; a sampling module for collecting core sampling data of a target region based on the preliminary fracture distribution condition, wherein the target region is a fracture distribution region indicated by the preliminary fracture distribution condition; and a fracture condition determination module for determining fracture positions and characteristic information of the target region based on the core sampling data, wherein the fracture positions and characteristic information are used as target fracture conditions.

[0013] According to another aspect of the embodiment 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 crack conditions 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 condition 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 fracture situation can more accurately determine the distribution and characteristic information of fractures by combining geological profile data and core sampling data, thereby improving the accuracy and efficiency of fracture detection. In particular, after the fracture distribution area is preliminarily determined, targeted core sampling can reduce unnecessary sampling work and save time and cost. The preliminary geological profile data analysis provides a broad perspective on the distribution of fractures, while the core sampling data supplements the detailed information on the specific location and characteristics of the fractures. The combination of the two significantly improves the reliability and accuracy of fracture assessment. 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 conditions according to an embodiment of the present application;

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

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

[0022] Figure 4 is a flowchart of a method for determining crack conditions according to an embodiment of the present application;

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

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

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

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

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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 the present 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 crack conditions according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 The computer terminal includes a processor 102 (only one is shown in the figure) (the processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, MPU for short) or a programmable logic device (PLD for short)) 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 condition 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 conditions is provided. Figure 2 is a flow chart of an optional method for determining crack conditions according to an embodiment of the present application, the process comprising the following steps S200-S230:

[0033] Step S200, obtaining geological profile data of the area to be measured.

[0034] Specifically, geological profile data is key information for studying stratigraphic structure, rock properties and geological processes. It can provide continuous cross-sectional images of underground rock layers and help identify possible fracture formation areas.

[0035] Exemplarily, geophysical exploration techniques such as seismic exploration, electrical exploration, gravity exploration, and magnetic exploration are used to obtain underground structural information of the area to be tested. Drilling technology is used to directly obtain stratum samples, and the distribution of stratum fractures is inferred by analyzing the characteristics of rock cuttings and borehole walls. The collected geological profile data is input into the geological modeling software for data processing and fracture identification to form a preliminary fracture distribution map. A drone equipped with a high-resolution camera can be used to take aerial photos of the area to be tested to obtain image data of the fracture area. Ensure that the drone flight path covers all areas of interest, and take multiple shots at different time periods to capture fracture changes. De-noise, correct, and crop the acquired images to improve the accuracy of subsequent analysis. Convert the image data into a format suitable for processing by machine learning algorithms, such as converting the image into a grayscale image or extracting feature vectors.

[0036] Step S210, determining preliminary crack distribution of the area to be tested based on geological profile data.

[0037] Specifically, based on geological profile data, image processing algorithms and geostatistical methods are used to identify the possible locations and characteristics of fractures, providing guidance for subsequent core sampling.

[0038] For example, image processing techniques are used to extract crack features, such as crack length, width, direction, etc., and edge detection algorithms (such as Canny edge detection) can be used to process geological profile images to identify possible crack edges. Pattern recognition techniques, such as support vector machines or deep learning methods, such as convolutional neural networks, are used to train models to classify and predict the presence of cracks. Combined with formation thickness, rock type, and stress analysis, the identified cracks are preliminarily spatially located and characterized to form a preliminary model of crack distribution.

[0039] Step S220, collecting core sampling data of the target area according to the preliminary fracture distribution.

[0040] Among them, the target area is the crack distribution area indicated by the preliminary crack distribution situation.

[0041] Specifically, core sampling is an important means to obtain direct physical and chemical properties of underground rocks. By comparing the preliminary fracture distribution model, cores are collected from suspected fracture-dense or critical areas to verify and refine the fracture characteristics.

[0042] For example, according to the preliminary fracture distribution model, the location of the core sampling point is determined to ensure that the hot spot area predicted by the fracture is covered. Use professional core drilling equipment, such as a core drill, to perform directional drilling to obtain core samples, which should include fracture areas. Perform non-destructive analysis on the core, such as tomography, X-ray imaging, ultrasonic testing, and physical and chemical tests, such as porosity and permeability measurements, mineralogy analysis, etc.

[0043] Step S230, determining the fracture location and characteristic information of the target area according to the core sampling data.

[0044] Among them, the crack location and characteristic information are used as the target crack situation.

[0045] Specifically, through detailed analysis of the core, the specific location, size, direction, and filling characteristics of the fractures can be obtained. This information is used as the target fracture situation to correct and optimize the preliminary fracture distribution model.

[0046] For example, manual recording and image capture of fracture characteristics are performed on the surface and inside the core. Core analysis data, including porosity and permeability, are used to infer fracture connectivity and the impact on fluid transport. Fracture information in core sampling data is integrated and compared with the preliminary fracture distribution model to correct the uncertainty of model prediction and form a more accurate target fracture situation.

[0047] In this embodiment, by combining geological profile data and core sampling data, the distribution and characteristic information of cracks can be determined more accurately, improving the accuracy and efficiency of crack detection. In particular, after the crack distribution area is preliminarily determined, targeted core sampling can reduce unnecessary sampling work and save time and cost. The preliminary geological profile data analysis provides a broad perspective of crack distribution, while the core sampling data supplements the detailed information of the specific location and characteristics of the cracks. The combination of the two significantly improves the reliability and accuracy of crack assessment.

[0048] In one embodiment, Figure 3 As shown, step S210, determining the preliminary crack distribution of the area to be tested according to the geological profile data. It includes: steps S300-S330:

[0049] Step S300: determining the abnormal signal conditions corresponding to each sub-area of ​​the area to be tested according to the geological profile data.

[0050] Among them, the abnormal signal is a signal related to the crack.

[0051] Specifically, geological profile data contains a variety of physical characteristics, such as seismic wave velocity, rock density, electromagnetic response, etc. These characteristics will show abnormalities when cracks exist, that is, they are significantly different from the surrounding unfractured areas. Signal anomaly detection is based on these differences to perform preliminary screening for crack prediction.

[0052] Exemplarily, the area to be tested is divided into multiple sub-areas, and the size of each sub-area depends on the data resolution and analysis requirements. Anomaly detection analysis is performed on the geological profile data of each sub-area, for example, by comparing the physical characteristics of the sub-area with the adjacent area, identifying signal anomalies that may be related to cracks. Statistical methods (such as standard deviation analysis, Z value detection) or machine learning algorithms (such as cluster analysis, anomaly detection models) are used to quantify the abnormal signal conditions of the sub-area to provide a basis for subsequent probability evaluation.

[0053] Step S310, determining the probability of cracks occurring in each sub-region according to the abnormal signal conditions corresponding to each sub-region.

[0054] Specifically, after the abnormal signals are identified, the probability of these signals being converted into the existence of cracks needs to be further evaluated.

[0055] For example, a relationship model between physical characteristics and the probability of crack existence is established, which can be trained by physical characteristic data of known crack areas. Using Bayesian probability theory, combined with the abnormal signal strength and other geological information of the sub-area, the conditional probability of cracks in each sub-area is calculated. The calculated probability distribution map is superimposed with the geological profile data to form a crack probability distribution model to intuitively display the possibility of cracks in different sub-areas.

[0056] Step S320: According to the probability of cracks appearing in each sub-region, a sub-region in which the probability of cracks appearing is higher than a preset threshold is determined as a target region.

[0057] Specifically, target areas refer to geological sub-areas where the probability of fractures is significantly higher than that of surrounding areas. These areas are the focus of subsequent core sampling and detailed fracture analysis.

[0058] For example, a fracture probability threshold is set as the boundary to distinguish the target area from the non-target area. The fracture probability of each sub-area is compared with the threshold, and the sub-area with a probability higher than the threshold is marked as a potential fracture-intensive area, i.e., the target area. The target areas are prioritized, and the priority of core sampling is determined according to the fracture probability and the potential impact on resource development and disaster prevention.

[0059] Step S330, determining preliminary crack distribution according to the distribution of the target area in the area to be tested.

[0060] Specifically, the distribution of the target area can be used to preliminarily outline the distribution pattern of cracks in the area to be tested, which serves as a starting point for further detailed analysis.

[0061] For example, the fracture probability distribution model of the target area is visualized to form a fracture probability distribution map. Based on the fracture probability distribution map, the hot spots and distribution trends of fracture distribution are identified, and the connectivity and distribution range of fractures are preliminarily determined. Combined with geological background knowledge, the causes of fracture distribution patterns are explained to provide direction for subsequent core sampling and fracture feature analysis.

[0062] In this embodiment, through abnormal signal detection and probability evaluation, the existence of cracks can be predicted more accurately, misjudgment and missed judgment can be reduced, and the accuracy of resource exploration and geological disaster prediction can be improved. Clear target areas guide core sampling, which can not only ensure sampling efficiency, but also ensure the representativeness of sampling data, and provide high-quality samples for detailed crack analysis. The preliminary crack distribution provides an important reference for engineering design and construction, such as optimizing drilling paths, avoiding or reducing geological disaster risks, and improving resource extraction efficiency. Based on the analysis of crack probability distribution, we can deepen our understanding of geological processes and crack formation mechanisms, and promote the development of geological science and technological progress. Combining qualitative analysis and quantitative prediction of geological data, it effectively improves the scientificity and practicality of crack distribution assessment.

[0063] In one embodiment, Figure 4 As shown, the method further includes: step S240, supplementing the preliminary crack distribution of the target area according to the crack position and characteristic information of the target area, and determining the target crack distribution of the target area.

[0064] Specifically, the preliminary fracture distribution is a fracture prediction based on geological profile data, while the core sampling data provides direct evidence of fractures, including the specific location, size, shape, filling material, and connectivity of fractures. The supplementation process involves combining the core data with the preliminary model to correct the uncertainty of the model prediction. The target fracture distribution is based on the fracture distribution model supplemented with the core sampling data, which is closer to the actual situation and reflects the precise distribution and characteristics of fractures in the target area.

[0065] Exemplarily, the location and characteristic information of the cracks identified in the core are spatially registered with the geological profile data to ensure the consistency of the two in spatial coordinates. Data fusion techniques, such as Bayesian data fusion or multi-source information fusion algorithms, are used to integrate the crack information in the core sampling data into the preliminary crack distribution model to improve the accuracy of the model. The location and size of the cracks in the preliminary model are corrected. If the core data show that the connectivity or filling of the cracks does not match the model prediction, corresponding adjustments should be made in the model. The supplemented crack distribution model is further analyzed, including the density of cracks, the connectivity of the crack network, the direction and tendency of the cracks, etc. This information helps to understand the behavior of the fracture system. The crack characteristic information in the core sampling data, such as the width, length, filling and connectivity of the cracks, is used to refine the crack distribution model and improve the resolution and accuracy of the model. Based on the core data, the trunk and branch structure of the cracks are identified, which is crucial for evaluating the effectiveness of the cracks and their impact on fluid transmission.

[0066] In this embodiment, the core sampling data provides direct evidence of the distribution of fractures. Through data fusion technology, the deviation of model prediction can be corrected, and the reliability and accuracy of the fracture distribution model can be improved. Accurate target fracture distribution helps to formulate more effective resource development plans, such as optimizing drilling paths, improving the extraction efficiency of oil and gas reservoirs, and reducing exploration costs. The fracture distribution model supplemented by core data can more accurately predict the probability of occurrence and potential impact areas of geological disasters such as earthquakes and landslides, providing a scientific basis for disaster prevention and emergency response. The determination of the target fracture distribution provides a more accurate data basis for geological research, helps to deeply understand the formation mechanism and evolution process of fractures, and promotes the development of geological science theory and technology. Through the supplementation and fusion of core data, the accuracy and practicality of fracture distribution assessment have been significantly improved, which has had a profound impact on the fields of geological science, resource development and disaster warning, and is an important innovation in geological exploration technology.

[0067] In one embodiment, Figure 5 As shown, the method further includes: steps S500-S510:

[0068] Step S500: constructing a sample set based on the crack positions, feature information and target crack distribution conditions of a plurality of different target areas.

[0069] Specifically, the sample set is the basis for training the neural network model. It contains crack data of multiple target areas, including characteristic information such as the specific location, size, direction, filling type, and the correspondence between these characteristics and the crack distribution.

[0070] Exemplarily, fracture characteristics are extracted from core sampling data, including fracture length, width, strike, dip, filling type, and connectivity, and the characteristics of each fracture constitute a feature vector of the sample. The fracture distribution in the target area is converted into quantitative or qualitative labels as the output target of the sample. For example, the fracture distribution can be divided into three levels of high, medium, and low density, or directly represented by a fracture network diagram. When constructing a sample set, ensure the diversity and representativeness of the data, including samples of different lithologies, different geological environments, and different fracture characteristics, to cover various fracture distribution scenarios and improve the generalization ability of the model.

[0071] Step S510: Use the sample set to train a preset neural network model to construct a crack distribution prediction model.

[0072] Among them, the crack distribution prediction model is used to determine the crack distribution according to the crack location and characteristic information.

[0073] Specifically, the neural network model is a powerful machine learning tool that can automatically learn complex patterns from input features for predicting crack distribution.

[0074] Exemplarily, the selection of a suitable neural network architecture, such as a convolutional neural network, a long short-term memory network, or a fully connected network, depends on the type of crack feature data and the prediction requirements. The constructed sample set is divided into a training set, a validation set, and a test set, which are used for model training, parameter adjustment, and model performance evaluation, respectively. The neural network model is trained using the training set, and the model parameters are optimized by the back propagation algorithm so that the model can accurately predict the crack distribution based on the crack feature information. The hyperparameters of the model, such as the learning rate, regularization coefficient, etc., are adjusted on the validation set to prevent overfitting and improve the generalization ability of the model. The prediction performance of the model is evaluated on the test set, including indicators such as prediction accuracy and recall rate, to ensure the reliability and practicality of the model.

[0075] In this embodiment, the neural network model can automatically learn the complex relationship between crack characteristics and crack distribution, realize rapid prediction of crack distribution, and greatly improve the efficiency of geological exploration and resource assessment. Based on the training of diverse and representative sample sets, and the optimization and adjustment of the model, the crack distribution prediction model can provide high-precision prediction results, reduce prediction errors, and improve the decision-making quality of resource development and geological disaster prevention. The sample set covers data of different geological environments and crack characteristics, so that the trained crack distribution prediction model has strong generalization ability and can show good prediction performance on unseen data sets. Combining geological data with advanced machine learning technology promotes the digital transformation in the field of geological science and provides a technical basis for realizing intelligent geological exploration and resource management. By constructing and training the crack distribution prediction model, geological exploration is brought into a new stage of automation and intelligence, significantly improving the accuracy and efficiency of crack assessment, and having a positive impact on the fields of geological science, resource development and disaster warning, representing an important innovation in the field of geological data processing and analysis.

[0076] In one embodiment, Figure 6 As shown, the method further includes: steps S600-S620:

[0077] Step S600, obtaining core sampling data of the target area at different time points.

[0078] Specifically, in order to monitor the changes of cracks over time, it is necessary to conduct core sampling in the target area at multiple time points to obtain the crack characteristic information at different time points and provide basic data for the analysis of crack change trends.

[0079] For example, a core sampling plan is formulated, including the selection of sampling time points, the layout of sampling points, and the setting of sampling depth, to ensure that the hot spots of fracture distribution in the target area can be covered. Professional core drilling equipment, such as core drilling rigs, is used for directional drilling to ensure the accuracy of sampling points and the quality of core samples. The sampled core samples are subjected to detailed non-destructive analysis, including tomography, X-ray imaging, ultrasonic testing, etc., as well as physical and chemical tests, such as porosity, permeability, and mineralogy analysis, to obtain detailed characteristics of fractures.

[0080] Step S610, determining the crack distribution of the target area at different time points according to the core sampling data of the target area at different time points and the crack distribution prediction model.

[0081] Specifically, by inputting the core sampling data at different time points into the fracture distribution prediction model, the fracture distribution in the target area at each time point can be predicted, providing a basis for monitoring the changes of fractures over time.

[0082] Exemplarily, the core sampling data is preprocessed to convert the fracture feature information into a format recognizable by the model, such as a feature vector or image data. The preprocessed core sampling data is used as input, and the fracture distribution prediction model is used for prediction to obtain the fracture distribution of the target area at each time point. The model prediction results are post-processed, such as data correction and result visualization, to more intuitively display the fracture distribution.

[0083] Step S620, determining a crack distribution change trend in the target area according to crack distribution conditions in the target area at different time points.

[0084] Specifically, by comparing the fracture distribution prediction results at different time points, the changing trend of fracture distribution can be analyzed, including fracture expansion, contraction, the emergence of new fractures, and changes in the fracture network structure.

[0085] For example, the fracture distribution prediction results at different time points are compared and analyzed, for example, by calculating the changes in indicators such as fracture density and fracture connectivity. Time series analysis methods are used to fit the changing trend of fracture distribution and predict future change patterns. Data visualization techniques, such as dynamic heat maps and animation displays, are used to intuitively present the changing process of fracture distribution, providing intuitive reference for decision makers.

[0086] In this embodiment, by acquiring core sampling data at different time points and combining with the fracture distribution prediction model, the changing trend of fracture distribution in the target area can be accurately monitored, and core sampling data at different time points can be obtained, so that the dynamic changes of fractures can be monitored, including the expansion and contraction of fractures and the emergence of new fractures, which is crucial for resource development and geological disaster early warning. Through the fracture distribution prediction model, combined with time series analysis, the changing trend of fracture distribution can be predicted, providing forward-looking guidance for geological exploration, resource management and disaster prevention. The determination of the changing trend of fracture distribution provides a scientific basis for engineering design, drilling path selection, resource exploitation planning and geological disaster risk assessment, effectively reducing engineering risks and costs.

[0087] In one embodiment, Figure 7 As shown, the method further includes: steps S700-S720:

[0088] Step S700, obtaining geological characteristics of the target area.

[0089] Specifically, geological characteristics include rock type, stratum structure, tectonic stress, temperature, pressure, etc., which have an important influence on the formation, evolution and distribution of fractures. Obtaining this information is the premise for analyzing the relationship between fracture change trends and geological characteristics.

[0090] For example, geological exploration methods such as seismic exploration, electrical exploration, gravity exploration, magnetic exploration, etc. are used to obtain underground structural information of the target area. Core samples are obtained through drilling and detailed geological analysis is carried out, including rock mineralogy, rock physical properties, stratigraphic chronology, etc. The geological exploration data and core analysis results are integrated to construct a three-dimensional geological model of the target area, recording key geological features such as rock type, stratigraphic structure, and tectonic stress.

[0091] Step S710, determining the relationship between the crack distribution change trend and the geological characteristics of the target area according to the geological characteristics and the crack distribution change trend of the target area.

[0092] Specifically, through statistical analysis or machine learning methods, the influence of geological characteristics on the changing trend of fracture distribution is explored to provide a basis for geological engineering decision-making.

[0093] For example, multivariate statistical analysis, such as correlation analysis and principal component analysis, is used to explore the quantitative relationship between rock type, tectonic stress, etc. and crack change trends. Machine learning models, such as regression models, decision trees, random forests, or neural networks, are constructed, with geological characteristics as input and crack change trends as output, and the model is trained to identify the impact pattern of geological characteristics on crack changes. By comparing the model predictions with the actual crack change trends, the accuracy and reliability of the model are verified, and the model parameters are continuously adjusted and optimized.

[0094] Step S720, determining the target geological features that affect the fracture distribution change trend according to the relationship between the fracture distribution change trend and the geological features in the target area.

[0095] Specifically, based on the aforementioned analysis results, the geological features that have the greatest impact on the trend of fracture distribution are identified, and these features will become the focus of subsequent geological research and engineering decision-making.

[0096] For example, the importance ranking of features output by the analysis model is used to determine which geological features have the greatest impact on the trend of fracture changes. A sensitivity analysis is performed to study the fracture distribution response of geological features with high impact under different changes to quantify their impact. Combining geological theory and engineering experience, the model results are interpreted and verified to ensure that the identified target geological features are scientific and practical.

[0097] In this embodiment, by analyzing the relationship between the geological characteristics of the target area and the trend of crack distribution changes, the key geological characteristics that affect the crack distribution changes are determined. This analysis method helps to deeply understand how geological characteristics affect the formation and evolution of cracks, provides new evidence for geological science theory, and promotes the development of geological science. The identified key geological characteristics provide a scientific basis for geological engineering decisions, such as the optimization of drilling paths, the formulation of resource exploitation plans, etc., which helps to improve engineering efficiency and reduce risks. The combination of in-depth knowledge of geology and advanced analytical methods of data science has brought revolutionary changes to the field of geological science and engineering, not only improving the understanding of underground fracture systems, but also providing strong support for practical engineering applications, demonstrating the important value of integrating multidisciplinary knowledge into geological data processing and analysis.

[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 conditions is also provided, and the device for determining crack conditions is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. 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 crack condition determination device according to an embodiment of the present application. Figure 8 As shown, including:

[0101] The data acquisition module 801 is used to acquire geological profile data of the area to be measured.

[0102] The preliminary distribution determination module 802 is used to determine the preliminary fracture distribution of the area to be tested based on the geological profile data.

[0103] The sampling module 803 is used to collect core sampling data of a target area according to the preliminary fracture distribution situation, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution situation.

[0104] The fracture condition determination module 804 is used to determine the fracture position and characteristic information of the target area according to the core sampling data, wherein the fracture position and characteristic information are used as the target fracture condition.

[0105] Through the above device, by combining geological profile data and core sampling data, the distribution and characteristic information of cracks can be determined more accurately, improving the accuracy and efficiency of crack detection. In particular, after the crack distribution area is preliminarily determined, targeted core sampling can reduce unnecessary sampling work and save time and cost. The preliminary geological profile data analysis provides a broad perspective on the distribution of cracks, while the core sampling data supplements the detailed information on the specific location and characteristics of the cracks. The combination of the two significantly improves the reliability and accuracy of crack assessment.

[0106] In an exemplary embodiment, the preliminary distribution determination module 802 is further used to: determine the abnormal signal conditions corresponding to each sub-region of the area to be tested according to the geological profile data, wherein the abnormal signal is a signal related to cracks. Determine the probability of cracks appearing in each sub-region according to the abnormal signal conditions corresponding to each sub-region. Determine the sub-regions in which the probability of cracks appearing in each sub-region is higher than a preset threshold as target regions according to the probability of cracks appearing in each sub-region. Determine the preliminary crack distribution conditions according to the distribution of the target regions in the area to be tested.

[0107] In an exemplary embodiment, the above device further includes:

[0108] The distribution determination module is used to supplement the preliminary crack distribution of the target area according to the crack position and characteristic information of the target area, and determine the target crack distribution of the target area.

[0109] In an exemplary embodiment, the above device further includes:

[0110] The sample construction module is used to construct a sample set based on the crack positions, feature information and target crack distribution of multiple different target areas.

[0111] The model building module is used to train a preset neural network model using a sample set to build a crack distribution prediction model, wherein the crack distribution prediction model is used to determine the crack distribution according to the crack location and characteristic information.

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

[0113] The sampling module is used to obtain core sampling data of the target area at different time points.

[0114] The distribution determination module is used to determine the crack distribution of the target area at different time points based on the core sampling data of the target area at different time points and the crack distribution prediction model.

[0115] The trend determination module is used to determine the crack distribution change trend of the target area according to the crack distribution conditions of the target area at different time points.

[0116] In an exemplary embodiment, the above device further includes:

[0117] The geological acquisition module is used to obtain the geological characteristics of the target area.

[0118] The correlation analysis module is used to determine the relationship between the crack distribution change trend and the geological characteristics of the target area based on the geological characteristics and the crack distribution change trend of the target area.

[0119] The cause determination module is used to determine the target geological features that affect the crack distribution change trend based on the relationship between the crack distribution change trend and the geological features in the target area.

[0120] 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.

[0121] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0122] S1, obtaining geological profile data of the area to be measured.

[0123] S2, determining the preliminary fracture distribution of the area to be tested based on the geological profile data.

[0124] S3, collecting core sampling data of a target area according to the preliminary fracture distribution, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution.

[0125] S4, determining the fracture position and characteristic information of the target area according to the core sampling data, wherein the fracture position and characteristic information are used as the target fracture condition.

[0126] 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.

[0127] 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.

[0128] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0129] S1, obtaining geological profile data of the area to be measured.

[0130] S2, determining the preliminary fracture distribution of the area to be tested based on the geological profile data.

[0131] S3, collecting core sampling data of a target area according to the preliminary fracture distribution, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution.

[0132] S4, determining the fracture position and characteristic information of the target area according to the core sampling data, wherein the fracture position and characteristic information are used as the target fracture condition.

[0133] 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.

[0134] 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.

[0135] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0136] S1, obtaining geological profile data of the area to be measured.

[0137] S2, determining the preliminary fracture distribution of the area to be tested based on the geological profile data.

[0138] S3, collecting core sampling data of a target area according to the preliminary fracture distribution, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution.

[0139] S4, determining the fracture position and characteristic information of the target area according to the core sampling data, wherein the fracture position and characteristic information are used as the target fracture condition.

[0140] 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.

[0141] 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.

[0142] 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 conditions, characterized in that: The method comprises: Obtain geological profile data of the area to be tested; Determining preliminary crack distribution of the area to be tested according to the geological profile data; According to the preliminary fracture distribution, core sampling data of a target area is collected, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution; The fracture positions and characteristic information of the target area are determined according to the core sampling data, wherein the fracture positions and characteristic information are used as target fracture conditions.

2. The method for determining crack conditions according to claim 1, characterized in that: Determining the preliminary crack distribution of the area to be tested according to the geological profile data includes: Determine, according to the geological profile data, abnormal signal conditions corresponding to each sub-area of ​​the area to be tested, wherein the abnormal signal is a signal related to a crack; Determine the probability of cracks occurring in each sub-region according to the abnormal signal conditions corresponding to each sub-region; According to the probability of cracks appearing in each sub-region, determining a sub-region in which the probability of cracks appearing in each sub-region is higher than a preset threshold as a target region; The preliminary crack distribution is determined according to the distribution of the target area in the area to be tested.

3. The method for determining crack conditions according to claim 2, characterized in that: After determining the fracture location and characteristic information of the target area according to the core sampling data, the method further includes: The preliminary crack distribution of the target area is supplemented according to the crack positions and characteristic information of the target area to determine the target crack distribution of the target area.

4. The method for determining crack conditions according to claim 3, characterized in that: The method further comprises: Construct a sample set based on the crack locations, characteristic information, and target crack distribution of multiple different target areas; The sample set is used to train a preset neural network model to construct a crack distribution prediction model, wherein the crack distribution prediction model is used to determine the crack distribution according to the crack position and characteristic information.

5. The method for determining crack conditions according to claim 4, characterized in that: The method further comprises: Obtain core sampling data of the target area at different time points; Determine the distribution of cracks in the target area at different time points according to the core sampling data of the target area at different time points and the crack distribution prediction model; According to the crack distribution conditions in the target area at different time points, the crack distribution change trend in the target area is determined.

6. The method for determining crack conditions according to claim 5, characterized in that: The method further comprises: Obtaining geological characteristics of the target area; Determining the relationship between the crack distribution change trend in the target area and the geological characteristics according to the geological characteristics and crack distribution change trend in the target area; According to the relationship between the fracture distribution change trend and the geological characteristics in the target area, the target geological characteristics that affect the fracture distribution change trend are determined.

7. A device for determining crack conditions, characterized in that: The device comprises: A data acquisition module is used to acquire geological profile data of the area to be tested; A preliminary distribution determination module, used to determine the preliminary crack distribution of the area to be tested according to the geological profile data; A sampling module, used for collecting core sampling data of a target area according to the preliminary fracture distribution, wherein the target area is a fracture distribution area indicated by the preliminary fracture distribution; The fracture condition determination module is used to determine the fracture position and characteristic information of the target area according to the core sampling data, wherein the fracture position and characteristic information are used as the target fracture condition.

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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