Real-time monitoring and 3d imaging method for true tri-axial hydraulic fracture

Abstract

The present application relates to the technical field of hydraulic fracturing monitoring, and particularly relates to a real-time monitoring and three-dimensional imaging method for true triaxial hydraulic fracturing cracks, which comprises the following steps: screening signals with crack development characteristics and marking them as monitoring crack signals; screening crack continuous states and crack types in the monitoring crack signals, based on the distribution characteristics of the crack continuous states among different monitoring signals, segmenting and splicing the crack types across signals to generate continuous imaging processing signals; extracting crack morphology information and crack spatial positions in the imaging processing signals, and based on the crack spatial positions and the number of crack types, judging whether there are features of mutual influence of multiple cracks in the imaging processing signals. The present application solves the problems of signal distortion and imaging distortion caused by the superposition or intersection of multiple cracks, differentiates the interference-free and multiple-crack interference scenes through differential processing, introduces distortion prediction and reverse correction, and realizes accurate three-dimensional real-time imaging of crack morphology, spatial position and interaction.

Description

Technical Field

[0001] This invention relates to the field of hydraulic fracturing monitoring technology, and in particular to a real-time monitoring and three-dimensional imaging method for true triaxial hydraulic fracturing fractures. Background Technology

[0002] In the field of unconventional oil and gas resource development, true triaxial hydraulic fracturing experiments are a core means of simulating the three-dimensional stress environment underground and studying the laws governing fracture initiation, propagation, and spatial distribution. The results directly impact fracturing process optimization and reservoir stimulation effectiveness. Currently, the industry's demand for fracture monitoring has shifted from traditional qualitative observation to dynamic, quantitative, and full-scale three-dimensional characterization.

[0003] However, existing true triaxial real-time monitoring and 3D imaging methods for hydraulic fracturing fractures suffer from severe signal distortion when multiple fractures overlap or intersect. This leads to inaccurate fracture morphology and location information, making it difficult to distinguish the signal characteristics of independent fractures from those that interfere with each other. Furthermore, no distortion correction model has been established for overlapping and intersecting patterns. The imaging results are prone to problems such as blurred fracture morphology, positional shifts, and distorted aperture, failing to accurately reflect the spatial relationships and interactions between fractures. In addition, existing imaging methods lack differentiated processing logic for interference-free fractures and multi-fracture interference scenarios, and lack systematic repair and correction schemes for missing imaging dimensions and data distortion. They struggle to simultaneously capture detailed fracture characterization and restore overall morphology, failing to meet the accuracy and efficiency requirements of real-time 3D imaging. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a real-time monitoring and three-dimensional imaging method for true triaxial hydraulic fracturing fractures, which solves the problem of signal distortion and imaging distortion caused by the superposition or intersection of multiple fractures in real-time monitoring of true triaxial hydraulic fracturing fractures, and achieves accurate three-dimensional real-time imaging of fracture morphology, spatial location and interaction.

[0005] To achieve the above objectives, this invention discloses a method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures, comprising:

[0006] Step S1: Collect real-time signals of true triaxial hydraulic fracturing fractures from different monitoring dimensions to form a target monitoring signal set. Select signals with fracture development characteristics from the target monitoring signal set and mark them as monitoring fracture signals.

[0007] Step S2: Identify the continuity state and type of cracks in the monitored crack signals. Based on the distribution characteristics of the continuity state of cracks among different monitoring signals, segment and stitch the crack types across signals to generate continuous signals to be imaged and processed.

[0008] Step S3: Extract the crack morphology information and crack spatial location from the signal to be imaged and processed. Based on the crack spatial location and the number of crack types, determine whether there are features of multiple cracks influencing each other in the signal to be imaged and processed.

[0009] If there are no features of multiple cracks in the signal to be imaged and processed, then the influence of crack features on the signal to be imaged and processed is determined based on crack type and crack morphology information, and the first imaging influence situation is generated.

[0010] If there are multiple cracks in the signal to be imaged and processed that affect each other, then based on the crack type and crack morphology information, the influence of the crack features that affect each other on the signal to be imaged and processed is determined, and a second imaging influence situation is generated.

[0011] By integrating the effects of the first and second imaging, a real-time three-dimensional image of the hydraulic fracturing fracture is formed.

[0012] Further, step S2 includes:

[0013] S21 identifies the crack type and crack continuity status in the monitored crack signal;

[0014] S22 When the continuous state of the same crack type spans at least two sets of monitored crack signals, the monitored crack signals are segmented to form a segmented signal region to be imaged.

[0015] S23 extracts crack features at the edges of each segmented imaging signal region, and performs different stitching logics based on the different states of the edge features:

[0016] If the edge features do not overlap but can be paired and continuous, then the segmented signal regions to be imaged are matched and stitched together to form the signal to be imaged and processed.

[0017] If the features of each crack overlap, the overlapping area of ​​each crack feature is segmented to form a signal region to be processed.

[0018] Based on the crack type of the signal region to be processed, crack features at the edges of each signal region to be processed are selected. After processing the crack edges of each signal region to be processed according to the crack features, they are stitched together to form the signal to be imaged and processed.

[0019] Further, step S3 includes:

[0020] If there is only one type of crack feature in the signal to be imaged and processed, or if there are multiple crack types that do not overlap or intersect in spatial location, then it is determined that there are crack features that do not affect each other.

[0021] If there are at least two types of cracks in the signal to be imaged and processed, and the spatial positions of each type of crack overlap or intersect, then it is determined that there are at least two mutually influential crack features.

[0022] Furthermore, if the signal to be imaged does not contain features of mutual influence among multiple cracks, then based on crack type and crack morphology information, the influence of crack features on the signal to be imaged is determined to generate a first imaging influence situation, including:

[0023] By comparing the morphological information of each crack type with the preset type crack morphology imaging table, the corresponding first imaging dimension and first imaging completeness are obtained.

[0024] Determine whether there is a complete imaging dimension with overlapping dimensions and a first missing imaging dimension:

[0025] If there is no overlap, the complete imaging dimension of each crack type is combined with the first missing imaging dimension to form the first imaging effect.

[0026] If there is overlap, the integrity data of the overlapping dimensions are calculated to obtain the imaging integrity, and the imaging integrity is combined with the integrity of the other dimensions to form the first imaging impact situation.

[0027] Furthermore, if the signal to be imaged contains features of multiple cracks influencing each other, then based on the crack type and morphology information, the influence of the mutually influencing crack features on the signal to be imaged is determined to generate a second imaging influence situation, including:

[0028] Identify the crack superposition and crack intersection patterns of mutually influencing cracks;

[0029] Determine the distortion effect of the crack superposition mode on the imaging geometry. If distortion occurs, obtain the distortion type and state information and map it to the distortion type and state imaging table to obtain the first missing imaging dimension and completeness. Integrate the first missing imaging dimension and the first imaging completeness to form the first imaging effect result.

[0030] Based on the crack intersection pattern, the crack type and morphology information of each crack involved in the intersection are mapped to the type crack morphology imaging table to obtain the second missing imaging dimension and the second imaging completeness. The second missing imaging dimension and the second imaging completeness are integrated to form the second imaging influence result.

[0031] The second imaging effect is formed by combining the first imaging effect result and the second imaging effect result.

[0032] Furthermore, the distortion effect of the crack stacking mode on the imaging geometry is determined, including:

[0033] Extract the stress field distribution characteristics and crack surface topological characteristics of the superimposed region in the crack superposition mode;

[0034] The stress field distribution characteristics and crack surface topological characteristics are input into a pre-trained distortion prediction model to obtain distortion feature type information and distortion feature state information;

[0035] By comparing the distortion feature type information, distortion feature state information, and distortion type state imaging table, the second missing imaging dimension caused by distortion and its corresponding imaging integrity quantification value are determined.

[0036] Furthermore, by integrating the effects of the first and second imaging methods, a real-time three-dimensional image of the true triaxial hydraulic fracturing fracture is formed, including:

[0037] Construct a three-dimensional mesh model and map the first or second imaging influence to the attribute parameters of the mesh nodes in the three-dimensional mesh model;

[0038] Based on the missing dimension information in the first imaging impact situation, the imaging impact repair data is obtained by locally refining or interpolating the three-dimensional mesh model.

[0039] Based on the distortion information in the second imaging influence, the geometry of the three-dimensional mesh model is reversed to obtain imaging influence correction data.

[0040] The imaging restoration data and imaging impact data are integrated to form a true triaxial real-time three-dimensional imaging result of hydraulic fracturing fractures.

[0041] Furthermore, a three-dimensional mesh model is constructed, including:

[0042] Based on the initial basic grid structure of the spatial coordinate extrema in the imaging signal, the material size and topological connection relationship of the basic grid cell are determined;

[0043] Complete data and spatial location information of crack signals are mapped to basic grid cells with corresponding topological connections to form a three-dimensional grid model.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects:

[0045] By identifying crack superposition and intersection patterns, a distortion prediction model driven by stress field and topological features is constructed, achieving accurate identification of distortion type and state. Inverse distortion correction eliminates errors such as crack morphological blurring, positional offset, and aperture distortion, realistically restoring the spatial relationships and interactions between multiple cracks and significantly improving imaging accuracy in complex crack scenarios. A dual-path processing mechanism is constructed, considering both first and second imaging influence scenarios. Local mesh refinement and interpolation repair are performed for missing imaging dimensions in interference-free crack scenarios, while inverse correction is applied to geometric distortions in multi-crack interference scenarios. This balances detailed crack characterization with overall morphological restoration, achieving accurate imaging in different scenarios and effectively solving the problem that traditional methods cannot adapt to multi-scenario imaging needs. By constructing a three-dimensional mesh model and mapping imaging influence parameters, a closed-loop processing from signal acquisition to three-dimensional imaging is achieved. This ensures the integrity and accuracy of imaging data and enables dynamic imaging through real-time mesh correction, providing reliable quantitative data support for the study of the dynamic evolution mechanism of hydraulic fracturing cracks and their engineering applications. Attached Figure Description

[0046] Figure 1 A schematic diagram illustrating the steps of the real-time monitoring and three-dimensional imaging method for true triaxial hydraulic fracturing fractures provided in an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram illustrating the steps of forming the signal to be imaged in the real-time monitoring and three-dimensional imaging method for true triaxial hydraulic fracturing provided in an embodiment of the present invention. Detailed Implementation

[0048] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 The principles and features of the present invention are described, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0049] A method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures includes the following steps:

[0050] Step S1: Collect real-time signals of true triaxial hydraulic fracturing fractures from different monitoring dimensions to form a target monitoring signal set. Select signals with fracture development characteristics from the target monitoring signal set and mark them as monitoring fracture signals.

[0051] Step S2: Screen the crack continuity state and crack type in the monitored crack signals. Based on the distribution characteristics of crack continuity state among different monitoring signals, segment and stitch the crack types across signals to generate continuous signals to be imaged and processed.

[0052] Step S3: Extract crack morphology information and crack spatial location from the signal to be imaged and processed. Based on the crack spatial location and the number of crack types, determine whether there are features of multiple cracks influencing each other in the signal to be imaged and processed.

[0053] If there are no features of multiple cracks in the signal to be imaged and processed, then the first imaging influence situation is generated based on the crack type and crack morphology information to determine the influence of crack features on the signal to be imaged and processed.

[0054] If there are multiple cracks in the signal to be imaged and processed that affect each other, then based on the crack type and crack morphology information, the influence of the crack features that affect each other on the signal to be imaged and processed is determined to generate a second imaging influence situation.

[0055] By integrating the effects of the first and second imaging, a real-time three-dimensional image of the hydraulic fracturing fracture is formed.

[0056] Step S2 includes the following specific steps:

[0057] S21: Identify the crack type and crack continuity status in the crack monitoring signal, providing a clear classification basis and continuity judgment standard for the segmentation and splicing of crack information.

[0058] S22: When a continuous state of the same crack type spans at least two sets of monitored crack signals, the monitored crack signals are segmented to form segmented imaging signal regions. For example, a hydraulic crack that penetrates both acoustic emission monitoring signals and distributed fiber optic strain monitoring signals may appear as an acoustic emission event sequence and a strain anomaly curve in the two types of signals, respectively. In this case, the boundary between the two types of signals is used as the segmentation point to split the crack information into two segmented imaging signal regions. After segmentation, crack features at the edges of each segmented imaging signal region are extracted. These edge features contain information about the crack's location, orientation, and morphology, serving as the matching basis for subsequent stitching.

[0059] S23: Extract the crack features at the edges of each segmented imaging signal region, and execute the following different stitching logic according to the different states of the edge features:

[0060] If the edge features do not overlap but can be paired and continuous, then the segmented signal regions to be imaged are directly matched and stitched together to form the signal to be imaged and processed. At this time, the matching and stitching is based on the position coordinates and orientation trend of the crack. By calculating the position difference and orientation difference of the edge features of adjacent regions, when the position difference is less than a preset threshold and the orientation difference is within the allowable range, it is determined that the edge features can be paired and continuous, thus achieving smooth stitching of cracks without overlap.

[0061] If the features of different cracks overlap, the overlapping areas are segmented to form the signal regions to be processed. For example, when multiple cracks intersect and overlap in different monitoring signals, the overlapping areas are segmented according to the actual extension path of the cracks based on the crack type and continuity, eliminating cross-interference at the signal level. Then, based on the crack type of the signal regions to be processed, crack features at the edges of each signal region are selected. After processing the crack edges of each signal region based on the crack features, they are stitched together to form the signal to be imaged and processed.

[0062] The entire process employs a logic of first identifying, then segmenting, and then processing according to different cases. This ensures that the crack information across signals can be completely and without repetition, providing continuous and accurate basic data for subsequent three-dimensional crack imaging. This guarantees that the imaging results can truly reflect the actual continuous state and spatial distribution of the cracks.

[0063] Step S3 includes the following specific steps:

[0064] S31 extracts fracture morphology and spatial location information from the signal to be imaged. Fracture morphology information includes the fracture's orientation, aperture, and length. Fracture spatial location is represented by three-dimensional coordinates, showing the distribution of fractures within the rock sample, such as the coordinates of the fracture's center and boundary points. Based on this, and considering the fracture spatial location and the number of fracture types, it determines whether there are multiple fractures interacting in the signal to be imaged, and executes the following two different judgment logics:

[0065] If the signal to be imaged contains only one type of crack feature, or multiple crack types that do not overlap or intersect spatially, it is determined that there are crack features that do not affect each other. For example, when the signal to be imaged contains only a single-direction main crack, there are no other cracks spatially associated with it, so it is determined to be a crack feature without mutual influence; or when there are two independent cracks in the signal, one distributed on the left side of the rock sample and the other distributed on the right side of the rock sample, and their spatial coordinates do not overlap or intersect, they are also determined to be crack features without mutual influence. To quantify whether spatial locations overlap or intersect, it can be determined by calculating the boundary coordinate range of the two cracks. If the x-axis coordinate range of crack 1 is x... 1min To x 1max The y-axis coordinate range is y 1min to y 1max The z-axis coordinate range is z 1min To z 1max The x-axis coordinate range of crack 2 is x 2min To x 2max The y-axis coordinate range is y 2min to y 2max The z-axis coordinate range is z 2min To z2max When x is satisfied 1max Less than x 2min or x 2max Less than x 1min , and y 1max Less than y 2min or y 2max Less than y 1min , and z 1max Less than z 2min or z 2max Less than z 1min If the two cracks do not overlap or intersect in spatial location, they are considered cracks that do not affect each other.

[0066] If at least two types of cracks exist in the signal to be imaged, and the spatial positions of these crack types overlap or intersect, then it is determined that there are at least two mutually influencing crack features. For example, when a primary crack and a secondary crack intersect in the middle of a rock sample, and their spatial coordinate ranges overlap, it is determined that the two cracks have mutually influencing features. In the quantitative determination, when the x-axis coordinate ranges, y-axis coordinate ranges, and z-axis coordinate ranges of two cracks overlap, it can be determined that they have spatially overlapping or intersecting features, belonging to mutually influencing crack features. This determination logic can accurately distinguish between independent cracks and interacting cracks, providing a clear classification basis for subsequent imaging influence correction for different scenarios, ensuring that the 3D imaging results can truly reflect the actual interaction relationship between cracks, and improving the accuracy and reliability of imaging.

[0067] Based on the different determination results, S32 generates two different imaging influence situations: if there are no features of multiple cracks in the signal to be imaged and processed, then the influence of crack features on the signal to be imaged and processed is determined according to crack type and crack morphology information, and a first imaging influence situation is generated; if there are features of multiple cracks in the signal to be imaged and processed, then the influence of crack features that have mutual influence on the signal to be imaged and processed is determined according to crack type and crack morphology information, and a second imaging influence situation is generated.

[0068] The process of generating the first imaging effect is as follows:

[0069] First, the morphological information of each crack type is compared with a pre-defined crack morphology imaging table to obtain the corresponding first imaging dimension and first imaging completeness. The pre-defined crack morphology imaging table pre-defines the correspondence between different crack types and imaging dimensions and imaging completeness. For example, the main crack type corresponds to three imaging dimensions: spatial length, orientation, and aperture, with completeness values ​​of 0.95, 0.92, and 0.90, respectively; the microcrack type corresponds to two imaging dimensions: crack density and distribution range, with completeness values ​​of 0.85 and 0.80, respectively. Table 1 shows a schematic table of crack morphology imaging for one type. Through standardized mapping, the morphological information of different crack types is transformed into unified imaging parameters, eliminating parameter differences between different crack types and providing a foundation for subsequent processing.

[0070] Table 1. Crack Morphology Imaging Table

[0071]

[0072] Second, determine whether there are overlapping complete imaging dimensions and a first missing imaging dimension. A complete imaging dimension refers to the imaging dimension that can be directly obtained from the crack morphology information, while the first missing imaging dimension refers to the imaging dimension that is not covered in the crack morphology information. For example, the morphology information of a main crack can provide complete imaging dimensions of length, direction, and aperture, but does not provide the crack roughness dimension. In this case, the crack roughness is the first missing imaging dimension.

[0073] If there is no overlap, the complete imaging dimensions of each crack type are combined with the first missing imaging dimension to form the first imaging effect. For example, the complete imaging dimension of the main crack is the length-direction aperture, and the complete imaging dimension of the microcrack is the density distribution range. The imaging dimensions of the two do not overlap, and the missing imaging dimension of the main crack is roughness, while the missing imaging dimension of the microcrack is connectivity. In this case, the length-direction aperture density distribution range is combined with roughness and connectivity to form the first imaging effect covering all relevant dimensions, ensuring that no imaging parameters are omitted.

[0074] If overlap exists, the completeness of the overlapping dimensions is calculated to obtain the imaging completeness. This completeness is then combined with the completeness of other dimensions to form the first imaging impact. Overlapping dimensions refer to data from different crack types on the same imaging dimension. For example, both the primary crack and the secondary crack include the orientation dimension, resulting in overlapping orientation data. The completeness of overlapping dimensions can be calculated using a weighted average method. The formula is: Imaging Completeness = Primary Crack Orientation Completeness × Primary Crack Weight + Secondary Crack Orientation Completeness × Secondary Crack Weight. The weights can be determined based on the crack's energy or scale; for example, the primary crack weight is 0.7, and the secondary crack weight is 0.3. After calculating the imaging completeness of the overlapping dimensions, it is then combined with the completeness of the other non-overlapping dimensions. For example, combining the imaging completeness of the orientation dimension with the completeness of the length, aperture, and density distribution range forms the complete first imaging impact. This eliminates duplicate data caused by dimensional overlap and ensures the comprehensiveness of imaging parameters, providing accurate correction data for subsequent 3D imaging.

[0075] The process of generating the second imaging effect is as follows:

[0076] First, we need to identify the overlapping and intersection patterns of fractures that influence each other. Overlapping fractures refer to the partial or complete spatial overlap of multiple fractures, such as two hydraulic fractures extending parallel to each other on the same plane with overlapping apertures. This can lead to blurred fracture edges in the signal, affecting the geometric accuracy of the imaging. Intersection fractures refer to the spatial convergence of multiple fractures, such as a primary fracture and a secondary fracture intersecting in a T-shape in the middle of the rock sample. This can cause signal feature superposition in the intersection area, affecting the accurate extraction of fracture orientation and aperture.

[0077] For crack stacking patterns, the distortion effect of the crack stacking pattern on the imaging geometry is determined. If distortion occurs, the distortion type and state information are obtained, such as aperture distortion caused by crack edge blurring, or coordinate distortion caused by crack position offset, and this information is mapped to a distortion type state imaging table. This table predefines the correction dimension and integrity parameters corresponding to different distortion types. For example, aperture distortion corresponds to the aperture correction dimension with an integrity of 0.85, and coordinate distortion corresponds to the position correction dimension with an integrity of 0.90. As shown in Table 2, a schematic table of distortion type state imaging is given. Through mapping, the first missing imaging dimension and integrity are obtained, and the first missing imaging dimension and the first imaging integrity are integrated to form the first imaging effect result. For example, when the apertures of two cracks overlap and cause distortion, the state information of the aperture distortion is obtained, and the aperture correction dimension and integrity of 0.85 are mapped to form the first imaging effect result used to correct the overlap distortion. In subsequent imaging, the aperture parameter will be corrected based on this result. The correction formula is: corrected aperture = original aperture × distortion correction coefficient, where the distortion correction coefficient is determined by the imaging integrity. For example, when the integrity is 0.85, the correction coefficient = 0.85.

[0078] Table 2 Distortion Type Status Imaging Table

[0079]

[0080] For crack intersection patterns, the type and morphological information of each crack involved in the intersection are mapped to a type crack morphology imaging table. This table predefines the correction dimension and integrity parameters corresponding to different crack type intersections. For example, the correction dimension for the intersection region orientation of the main crack and secondary crack intersection has an integrity of 0.88, and the correction dimension for the intersection point aperture has an integrity of 0.82. Through mapping, a second missing imaging dimension and a second imaging integrity are obtained. Integrating the second missing imaging dimension and the second imaging integrity forms the second imaging influence result. For example, when a primary crack intersects with a secondary crack, the crack type and intersection morphology information of the two are mapped to obtain the intersection area orientation correction dimension and integrity of 0.88, and the intersection point aperture correction dimension and integrity of 0.82. After integration, a second imaging effect result is formed to correct the intersection distortion. In subsequent imaging, the orientation and aperture parameters of the intersection area will be corrected based on this result. The correction formula is: corrected orientation = original orientation + orientation correction offset, where the orientation correction offset is determined by the imaging integrity and the intersection angle. For example, when the integrity is 0.88 and the intersection angle is 90 degrees, the offset = (1-0.88) × intersection angle / 2.

[0081] Second, by combining the results of the first and second imaging effects, a second imaging effect scenario is formed. This scenario includes correction parameters for both crack superposition distortion and crack intersection distortion, which can comprehensively eliminate imaging errors caused by the mutual influence of multiple cracks. This ensures that the 3D imaging results in complex crack scenes can truly reflect the actual geometric structure and spatial distribution of cracks, thereby improving the accuracy and reliability of imaging.

[0082] Determining the distortion effect of crack stacking modes on imaging geometry includes the following steps:

[0083] Extract the stress field distribution characteristics and crack surface topological characteristics of the superimposed region in the crack superposition mode;

[0084] The stress field distribution characteristics and crack surface topological characteristics are input into a pre-trained distortion prediction model to obtain distortion feature type information and distortion feature state information;

[0085] By comparing the distortion feature type information, distortion feature state information, and distortion type state imaging table, the second missing imaging dimension caused by distortion and its corresponding imaging integrity quantification value are determined.

[0086] Specifically,

[0087] First, the stress field distribution characteristics and crack surface topological characteristics of the superimposed region in the crack superposition mode are extracted. Stress field distribution characteristics refer to parameters showing the gradient change in stress magnitude and direction within the superimposed region. For example, when two parallel cracks are superimposed, stress in the superimposed region will exhibit localized concentration, with stress values ​​higher than the average stress in the surrounding area. Crack surface topological characteristics refer to parameters showing the deviation in the aperture and roughness orientation of the superimposed crack region. For instance, the crack aperture in the superimposed region will increase due to stress concentration, and the surface roughness will also be higher than in the non-superimposed region. These characteristics are the basis for judging distortion effects and can reflect the actual impact of crack superposition on the signal acquisition and imaging process.

[0088] Second, the stress field distribution characteristics and crack surface topological features are input into a pre-trained distortion prediction model to obtain distortion feature type information and distortion feature state information. This model, trained with a large amount of sample data, can establish a mapping relationship between stress field topological features and imaging distortion. For example, when the stress gradient in the superimposed region is greater than a preset threshold and the crack surface roughness is higher than normal, the model will output aperture distortion type information and distortion state information, such as a moderate distortion degree affecting 30% of the superimposed region. Internally, the model can predict the distortion type by calculating the distance between the feature vector and the center of the distortion sample. The formula is: distance = weighted sum of stress field feature vector and topological feature vector; the smaller the distance, the higher the matching degree with the distortion type.

[0089] Finally, the distortion feature type information and distortion feature state information are compared with the distortion type and state imaging table to determine the second missing imaging dimension caused by distortion and its corresponding quantified value of imaging integrity. The distortion type and state imaging table predefines the missing imaging dimensions and integrity corresponding to different distortion types and states. For example, a moderate aperture distortion state corresponds to an aperture correction dimension with an imaging integrity of 0.85; a mild position distortion state corresponds to a position correction dimension with an imaging integrity of 0.92. Through comparison, the specific dimensions affected during the imaging process are identified, and the imaging quality of that dimension is quantified. For example, an imaging integrity of 0.85 for the aperture dimension means that the reliability of the aperture data in the original imaging result is 85%. Subsequent correction needs to be based on this value. The corrected aperture data can be obtained by multiplying the original aperture by the integrity coefficient, i.e., corrected aperture = original aperture × 0.85. This achieves accurate quantification of the superimposed distortion effect of cracks, provides reliable parameter basis for imaging correction in complex crack scenarios, and ensures that the final imaging result can truly reflect the actual geometric structure of the crack.

[0090] S33 integrates the influence of the first and second imaging methods to form a real-time three-dimensional image of the hydraulic fracturing fracture, which is true triaxial. Specifically, it includes the following steps:

[0091] First, a three-dimensional mesh model is constructed, mapping either the first or second imaging influence to the attribute parameters of the mesh nodes in the 3D mesh model. The 3D mesh model serves as the carrier for fracture imaging, with its mesh nodes covering the entire rock sample space. Each node's attribute parameters include location coordinates, fracture aperture, fracture orientation, and fracture density. When there are no multi-fracture interaction features in the signal to be processed, the first imaging influence is mapped to the mesh nodes; for example, the dimensional information of the main fracture's length, orientation, and aperture, along with its corresponding integrity, is assigned to the mesh nodes in the fracture region. When multi-fracture interaction features exist, the second imaging influence is mapped to the nodes, including correction parameters for superposition and cross-distortion. This transforms abstract imaging influence parameters into operable node attributes in three-dimensional space, laying the foundation for subsequent correction processing.

[0092] Second, based on the missing dimensional information in the first imaging impact analysis, the 3D mesh model is locally refined or interpolated for repair, resulting in imaging impact repair data. Missing dimensional information represents parameters in the crack morphology information that were not directly collected, such as crack roughness and crack connectivity. For missing dimensions, local mesh refinement is used to improve data resolution, increasing the number of mesh nodes at crack edges or in areas with drastic detail changes, enabling the model to capture more subtle crack features. Simultaneously, interpolation algorithms are used to supplement the missing dimensional data. For example, based on the aperture values ​​of adjacent nodes, linear interpolation is used to calculate the aperture of intermediate nodes, with the formula: Node aperture = Previous node aperture × Weight + Next node aperture × 1 - Weight, where the weight is determined by the distance ratio between nodes. Through refinement and interpolation, the integrity and detail of the model are improved, forming imaging impact repair data.

[0093] Third, based on the distortion information in the second imaging influence scenario, reverse distortion correction is performed on the geometry of the 3D mesh model to obtain imaging influence correction data. Distortion information represents geometric deviations caused by the superposition or intersection of multiple cracks, such as crack position offset, aperture distortion, and orientation deviation. The core of reverse distortion correction is to restore the true shape of the cracks based on distortion parameters. For example, for aperture distortion, the correction formula is: corrected aperture = original aperture × aperture correction coefficient, where the aperture correction coefficient is determined by the imaging integrity in the second imaging influence scenario; for position distortion, the correction formula is: corrected coordinates = original coordinates + position correction offset, where the offset is calculated based on the distortion state information. Through reverse correction, the geometric errors caused by the mutual influence of multiple cracks are eliminated, forming imaging influence correction data.

[0094] Finally, the imaging restoration data and imaging impact data were integrated to form a true triaxial real-time 3D imaging result of hydraulic fracturing fractures. The imaging restoration data supplemented the missing dimensional information in the interference-free fracture scene, while the imaging impact correction data corrected the geometric distortion in the multi-fracture interaction scene. After the two were combined, each node of the 3D mesh model had complete and accurate attribute parameters, which could realistically reflect the spatial distribution morphology details and interaction relationships of the fractures. Based on the corrected 3D mesh model, real-time rendering was performed to output the 3D visualization results of the fractures, realizing a complete process from signal acquisition to 3D imaging, and providing intuitive and reliable data support for the dynamic monitoring and analysis of hydraulic fracturing fractures.

[0095] The following methods are used to construct a 3D mesh model, specifically including:

[0096] First, based on the initial basic grid structure of the spatial coordinate extrema in the imaging signal, the material size and topological connectivity of the basic grid cells are determined. Spatial coordinate extrema refer to the maximum and minimum coordinate values ​​of the fracture distribution in three-dimensional space. For example, the x-axis coordinate range of the fracture is 0 to 100 mm, the y-axis coordinate range is 0 to 100 mm, and the z-axis coordinate range is 0 to 100 mm. Using these extrema as boundaries, an initial grid structure covering the entire rock sample space is constructed. The material size of the basic grid cells is set according to the accuracy requirements of fracture imaging; for example, the cell side length is 1 mm to ensure that subtle features of the fracture can be captured. The topological connectivity defines the spatial association between grid cells. For example, each cell is connected to its six adjacent cells, forming a continuous three-dimensional grid skeleton, providing a spatial positioning basis for subsequent data mapping.

[0097] Then, the complete dimensional data and spatial location information of the fracture signal are mapped to the corresponding basic grid cells with corresponding topological connections to form a three-dimensional grid model. The complete dimensional data of the fracture signal includes the fracture aperture, direction, and density, while the spatial location information corresponds to the three-dimensional coordinates of the fracture in the rock sample. During the mapping process, the corresponding basic grid cell is found according to the spatial location of the fracture, and the fracture parameters are assigned to the attributes of that cell. For example, when a fracture with an aperture of 0.5 mm passes through grid cells with coordinates of 20, 30, and 40 mm, the aperture attribute of that cell is set to 0.5 mm, and the cell is marked as a fracture cell. For multiple consecutive fracture cells, the fracture data of adjacent cells are associated according to the topological connections to form a complete fracture morphology. In this way, the originally scattered fracture signal data is transformed into continuous cell attributes in a three-dimensional grid, constructing a three-dimensional grid model that can realistically reflect the spatial distribution and morphological characteristics of fractures. This provides a stable foundation for subsequent local grid densification interpolation repair and inverse distortion correction, ensuring the accuracy and continuity of the final imaging results.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures, characterized in that, include: Step S1: Collect real-time signals of true triaxial hydraulic fracturing fractures from different monitoring dimensions to form a target monitoring signal set. Select signals with fracture development characteristics from the target monitoring signal set and mark them as monitoring fracture signals. Step S2: Identify the continuity state and type of cracks in the monitored crack signals. Based on the distribution characteristics of the continuity state of cracks among different monitoring signals, segment and stitch the crack types across signals to generate continuous signals to be imaged and processed. Step S3: Extract the crack morphology information and crack spatial location from the signal to be imaged and processed. Based on the crack spatial location and the number of crack types, determine whether there are features of multiple cracks influencing each other in the signal to be imaged and processed. If there are no features of multiple cracks in the signal to be imaged and processed, then the influence of crack features on the signal to be imaged and processed is determined based on crack type and crack morphology information, and the first imaging influence situation is generated. If there are multiple cracks in the signal to be imaged and processed that affect each other, then based on the crack type and crack morphology information, the influence of the crack features that affect each other on the signal to be imaged and processed is determined, and a second imaging influence situation is generated. By integrating the effects of the first and second imaging, a real-time three-dimensional image of the hydraulic fracturing fracture is formed.

2. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 1, characterized in that, Step S2 includes: S21 identifies the crack type and crack continuity status in the monitored crack signal; S22 When the continuous state of the same crack type spans at least two sets of monitored crack signals, the monitored crack signals are segmented to form a segmented signal region to be imaged. S23 extracts crack features at the edges of each segmented imaging signal region, and performs different stitching logics based on the different states of the edge features: If the edge features do not overlap but can be paired and continuous, then the segmented signal regions to be imaged are matched and stitched together to form the signal to be imaged and processed. If the features of each crack overlap, the overlapping area of ​​each crack feature is segmented to form a signal region to be processed. Based on the crack type of the signal region to be processed, crack features at the edges of each signal region to be processed are selected. After processing the crack edges of each signal region to be processed according to the crack features, they are stitched together to form the signal to be imaged and processed.

3. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 2, characterized in that, Step S3 includes: If there is only one type of crack feature in the signal to be imaged and processed, or if there are multiple crack types that do not overlap or intersect in spatial location, then it is determined that there are crack features that do not affect each other. If there are at least two types of cracks in the signal to be imaged and processed, and the spatial positions of each type of crack overlap or intersect, then it is determined that there are at least two mutually influential crack features.

4. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 3, characterized in that, If the signal to be imaged does not contain features of multiple cracks influencing each other, then based on the crack type and morphology information, the influence of crack features on the signal to be imaged is determined to generate the first imaging influence situation, including: By comparing the morphological information of each crack type with the preset type crack morphology imaging table, the corresponding first imaging dimension and first imaging completeness are obtained. Determine whether there is a complete imaging dimension with overlapping dimensions and a first missing imaging dimension: If there is no overlap, the complete imaging dimension of each crack type is combined with the first missing imaging dimension to form the first imaging effect. If there is overlap, the integrity data of the overlapping dimensions are calculated to obtain the imaging integrity, and the imaging integrity is combined with the integrity of the other dimensions to form the first imaging impact situation.

5. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 4, characterized in that, If the signal to be imaged contains features of multiple cracks influencing each other, then based on the crack type and morphology information, the influence of the mutually influencing crack features on the signal to be imaged is determined to generate a second imaging influence situation, including: Identify the crack superposition and crack intersection patterns of mutually influencing cracks; Determine the distortion effect of the crack superposition mode on the imaging geometry. If distortion occurs, obtain the distortion type and state information and map it to the distortion type and state imaging table to obtain the first missing imaging dimension and completeness. Integrate the first missing imaging dimension and the first imaging completeness to form the first imaging effect result. Based on the crack intersection pattern, the crack type and morphology information of each crack involved in the intersection are mapped to the type crack morphology imaging table to obtain the second missing imaging dimension and the second imaging completeness. The second missing imaging dimension and the second imaging completeness are integrated to form the second imaging influence result. The second imaging effect is formed by combining the first imaging effect result and the second imaging effect result.

6. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 5, characterized in that, Determining the distortion effect of crack stacking modes on imaging geometry includes: Extract the stress field distribution characteristics and crack surface topological characteristics of the superimposed region in the crack superposition mode; The stress field distribution characteristics and crack surface topological characteristics are input into a pre-trained distortion prediction model to obtain distortion feature type information and distortion feature state information; By comparing the distortion feature type information, distortion feature state information, and distortion type state imaging table, the second missing imaging dimension caused by distortion and its corresponding imaging integrity quantification value are determined.

7. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 6, characterized in that, Integrating the effects of the first and second imaging methods, a real-time three-dimensional image of the hydraulic fracturing fracture is formed, including: Construct a three-dimensional mesh model and map the first or second imaging influence to the attribute parameters of the mesh nodes in the three-dimensional mesh model; Based on the missing dimension information in the first imaging impact situation, the imaging impact repair data is obtained by locally refining or interpolating the three-dimensional mesh model. Based on the distortion information in the second imaging influence, the geometry of the three-dimensional mesh model is reversed to obtain imaging influence correction data. The imaging restoration data and imaging impact data are integrated to form a true triaxial real-time three-dimensional imaging result of hydraulic fracturing fractures.

8. The method for real-time monitoring and three-dimensional imaging of true triaxial hydraulic fracturing fractures according to claim 7, characterized in that, Constructing a 3D mesh model includes: Based on the initial basic grid structure of the spatial coordinate extrema in the imaging signal, the material size and topological connection relationship of the basic grid cell are determined; Complete data and spatial location information of crack signals are mapped to basic grid cells with corresponding topological connections to form a three-dimensional grid model.

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