A method, apparatus, device and medium for identifying connected fractures

Abstract

This application discloses a method, apparatus, device, and medium for identifying connected fractures, relating to the field of geological exploration. The method includes: determining a candidate analysis space in a target area based on the target analysis object and the target reservoir range; performing density clustering on candidate fractures corresponding to the candidate analysis space in a three-dimensional discrete fracture network to obtain target fracture clusters; abstracting the fractures in the target fracture clusters as graph nodes to construct a candidate connectivity graph, and determining the connection evaluation index corresponding to the candidate edges between each graph node in the candidate connectivity graph; and performing path search in the candidate connectivity graph based on the connection evaluation index to construct the target connectivity graph and complete the connected fracture identification process corresponding to the target area. By determining the candidate analysis space based on the target analysis object and the reservoir range, and performing density clustering on the candidate fractures within that space, the problem of unclear identification of key fracture clusters and susceptibility to interference from weakly connected external fractures is solved.

Description

Technical Field

[0001] This invention relates to the field of geological exploration, and in particular to a method, apparatus, equipment and medium for identifying interconnected fractures. Background Technology

[0002] Unconventional reservoirs such as shale gas and tight oil and gas typically require hydraulic fracturing to create complex fracture networks, thereby improving reservoir permeability and increasing single-well productivity. The effectiveness of the fracturing fracture system depends not only on the number, size, and spatial distribution of fractures, but also, and perhaps more importantly, on whether continuous channels can be formed between the fractures to effectively connect with the wellbore. Fracture connectivity directly relates to the reservoir exploitation range, the identification of dominant permeation channels, and the evaluation of effective stimulation volume, making it a key indicator in interpreting fracturing effects.

[0003] Current methods for identifying connected cracks rely on simple connectivity analysis based on crack geometry and topology. This approach suffers from problems such as unclear identification of key crack clusters in complex discrete crack networks and strong interference from weakly connected cracks in the periphery. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for identifying connected fractures, which can determine a candidate analysis space based on the target analysis object and reservoir range, and perform density clustering processing on the candidate fractures within that space, thus solving the problems of unclear identification of key fracture clusters and susceptibility to interference from weakly connected fractures in the surrounding area. The specific solution is as follows: In a first aspect, this application provides a method for identifying connected cracks, including: The three-dimensional discrete fracture network, target analysis object, target reservoir range, and local stress direction information corresponding to the target region are obtained, and the candidate analysis space in the target region is determined based on the target analysis object and the target reservoir range. Density clustering is performed on the candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space to obtain several crack clusters, and the target crack cluster is selected from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object. Each target crack in the target crack cluster is abstracted as a graph node. A candidate connected graph is constructed based on the spatial relationship between the graph nodes and each target crack. The connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph is determined. The connection evaluation index characterizes the connectivity potential between the corresponding target cracks. In the candidate connected graph, several rounds of path search are performed based on the target path search algorithm and each of the connection evaluation indicators. Based on the results of the several rounds of path search, the target edge is determined from each of the candidate edges. The target connected graph is then constructed based on each target edge and the corresponding graph nodes to complete the process of identifying the connected cracks corresponding to the target region.

[0005] Optionally, the density clustering processing of candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space includes: The number of other cracks contained in the neighborhood space corresponding to any candidate crack is counted. If the number of cracks in the neighborhood space corresponding to any candidate crack is not less than a preset number threshold, then any candidate crack is determined as a core point. Cracks located in the neighborhood space corresponding to any candidate crack that do not meet the core point condition are identified as boundary points corresponding to any candidate crack, and any candidate crack and the corresponding boundary point are assigned to the same crack cluster to complete the density clustering operation corresponding to each candidate crack.

[0006] Optionally, constructing a candidate connected graph based on the spatial relationships between the graph nodes and each of the target cracks includes: Determine the center-to-center distance between each target crack and the angle between the crack surface normals, and determine the crack size, local stress direction, and crack location corresponding to each target crack; Based on the crack center distance, the crack surface normal angle, the crack size, the local stress direction, and the crack location, node pairs with connectivity potential are determined from each of the graph nodes, and candidate edges are constructed between each node pair to generate the candidate connected graph.

[0007] Optionally, the nodes in the candidate connected graph include three types of connectivity relationships: direct connectivity, nearest neighbor connectivity, and associated connectivity. The direct connectivity relationship indicates that the cracks directly intersect or are tangent to each other. The nearest neighbor connectivity relationship indicates that the two cracks do not intersect and the spatial distance is less than a preset distance threshold. The associated connectivity relationship indicates that the two cracks are spaced apart and have a bridging relationship under the common constraints of spatial position, geometric shape, and local stress direction.

[0008] Optionally, determining the connection evaluation index corresponding to each candidate edge between the graph nodes in the candidate connected graph includes: Each candidate connection edge is assigned a corresponding sub-index for crack activity, a sub-index for distance attenuation, a sub-index for geometric compatibility, a sub-index for consistency of extension trend, and a sub-index for consistency of local stress direction. Specifically, the crack activity sub-index characterizes the ability of a crack to participate in connectivity; the distance attenuation sub-index characterizes the influence of the crack center distance on connectivity probability; the geometric compatibility sub-index characterizes the degree of matching between two cracks in scale and orientation; the consistency of extension trend sub-index characterizes the connection relationship between two cracks in the spatial extension direction; and the consistency of local stress direction sub-index characterizes the degree of coordination between the direction of the connection between two cracks and the crack propagation direction controlled by the principal stress. The corresponding crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index are weighted and fused to obtain the connection evaluation index corresponding to the candidate edges between each graph node.

[0009] Optionally, the step of performing several rounds of path search in the candidate connected graph based on the target path search algorithm and each of the connection evaluation metrics includes: The pheromone intensity on each of the candidate edges is initialized, and several rounds of path search are performed in the candidate connected graph according to the target path search algorithm, the pheromone intensity corresponding to each candidate edge, and each connection evaluation index; wherein, the target path search algorithm is an improved ant colony algorithm.

[0010] Optionally, determining the target edge from the candidate edges based on several rounds of path search results includes: The selection frequency, path contribution, and final pheromone intensity of each candidate edge are determined based on several rounds of path search results. The target edge is then determined from the candidate edges based on the selection frequency, path contribution, and final pheromone intensity.

[0011] Secondly, this application provides a connectivity crack identification device, comprising: The spatial determination module is used to acquire the three-dimensional discrete fracture network, target analysis object, target reservoir range and local stress direction information corresponding to the target region, and determine the candidate analysis space in the target region based on the target analysis object and the target reservoir range; The crack cluster screening module is used to perform density clustering on the candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space to obtain several crack clusters, and to screen out the target crack cluster from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object. A connected graph construction module is used to abstract each target crack in the target crack cluster as a graph node, construct a candidate connected graph based on the spatial relationship between the graph nodes and each target crack, and determine the connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph; the connection evaluation index characterizes the connectivity potential between the corresponding target cracks. The connectivity analysis module is used to perform several rounds of path search in the candidate connected graph according to the target path search algorithm and each of the connection evaluation indicators, determine the target edge from each of the candidate edges according to the path search results of several rounds, and construct the target connected graph according to each target edge and the corresponding graph node to complete the process of identifying the connectivity cracks corresponding to the target region.

[0012] Thirdly, this application provides an electronic device, comprising: Memory is used to store computer programs; A processor for executing the computer program to implement the aforementioned method for identifying connectivity gaps.

[0013] Fourthly, this application provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the aforementioned method for identifying connectivity gaps.

[0014] This application first obtains the three-dimensional discrete fracture network, target analysis object, target reservoir range, and local stress direction information corresponding to the target region. Based on the target analysis object and the target reservoir range, candidate analysis spaces are determined within the target region. Then, density clustering is performed on the candidate fractures in the three-dimensional discrete fracture network corresponding to the candidate analysis spaces to obtain several fracture clusters. Target fracture clusters are selected from these clusters based on the spatial relationship between each fracture cluster and the target analysis object. Next, each target fracture in the target fracture cluster is abstracted as a graph node. A candidate connected graph is constructed based on the spatial relationship between the graph nodes and the target fractures. Connection evaluation indices are determined for each candidate edge in the candidate connected graph. These connection evaluation indices characterize the connectivity potential between the corresponding target fractures. Finally, several rounds of path search are performed in the candidate connected graph based on the target path search algorithm and the connection evaluation indices. Target edges are determined from the candidate edges based on the search results from these rounds of path search. A target connected graph is constructed based on the target edges and the corresponding graph nodes to complete the connected fracture identification process corresponding to the target region. Therefore, this application solves the problems of unclear identification of key fracture clusters and susceptibility to interference from weakly connected fractures in the prior art by determining the candidate analysis space based on the target analysis object and the reservoir range, performing density clustering on the candidate fractures in the space to obtain fracture clusters, and then screening out the target fracture clusters based on the spatial relationship between each fracture cluster and the target analysis object. Furthermore, by determining the connectivity evaluation index that characterizes the connectivity potential between fractures and using the target path search algorithm to extract target edges from the candidate connectivity graph to construct the target connectivity graph, this application solves the problem of the prior art's difficulty in outputting the specific fracture units and spatial skeleton structure involved in the connectivity. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a method for identifying connected cracks disclosed in this application; Figure 2 This application discloses a three-dimensional discrete fracture network and a spatial distribution map of well trajectories. Figure 3 This is a diagram showing the results of fracture cluster identification and wellbore screening disclosed in this application; wherein, Figure 3 (a) is a partial planar layout diagram. Figure 3 (b) is a diagram showing the results of local screening in the wellbore; Figure 4 This application discloses a flowchart for constructing a connected graph. Figure 5 This is a three-dimensional structural diagram of a stable connected network disclosed in this application; Figure 6 This is a diagram showing the independent component partitioning result of a connected network disclosed in this application; Figure 7 This is a diagram illustrating the composition of a superior connected network connectivity type as disclosed in this application; Figure 8 This is a schematic diagram of the structure of a connecting crack identification device disclosed in this application; Figure 9 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1 As shown, an embodiment of the present invention discloses a method for identifying connected cracks, including: Step S11: Obtain the three-dimensional discrete fracture network, target analysis object, target reservoir range, and local stress direction information corresponding to the target region, and determine the candidate analysis space in the target region based on the target analysis object and the target reservoir range.

[0019] The connectivity crack identification method in this embodiment mainly includes the following steps: crack network input and target analysis range determination, crack cluster identification and key cluster screening, candidate connectivity graph construction, edge heuristic information calculation, improved ant colony path search, stable dominant subgraph extraction and connectivity index output.

[0020] The process for inputting the crack network and determining the target analysis range is as follows: The process involves acquiring a three-dimensional discrete fracture network model of the area to be analyzed, the target analysis object, the target reservoir range, and local stress direction information. In the discrete fracture network model, each fracture includes at least the following attributes: fracture center location, strike, dip angle, radius, aperture, normal vector, and the fracture group to which it belongs. Local stress direction is used to determine the degree of coordination between the fracture connection direction and the fracture propagation background.

[0021] Let the first The center point of the crack is: ; The sample set consists of all the center points of the cracks: ; In the formula, This represents the number of fractures in the discrete fracture network. The candidate analysis space is determined based on the target analysis object and the target reservoir range. For peripheral fractures whose distance from the fracture center to the well trajectory exceeds the preset influence distance and lack spatial connection with the target dominant fracture zone, their participation weight can be reduced or they can be temporarily excluded from the path search during the initial screening stage to minimize the interference of weakly connected fractures at the periphery on the dominant connected structure within the target range.

[0022] Step S12: Perform density clustering on the candidate cracks in the three-dimensional discrete crack network that correspond to the candidate analysis space to obtain several crack clusters, and select the target crack cluster from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object.

[0023] In this embodiment, density clustering is performed on candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space. This includes: counting the number of other cracks contained in the neighborhood space corresponding to any candidate crack; if the number of cracks in the neighborhood space corresponding to any candidate crack is not less than a preset threshold, then the candidate crack is determined as a core point; cracks located in the neighborhood space corresponding to any candidate crack that do not meet the core point condition are determined as boundary points corresponding to any candidate crack, and the candidate crack and the corresponding boundary point are assigned to the same crack cluster to complete the density clustering operation corresponding to each candidate crack.

[0024] Specifically, the crack cluster identification and key cluster screening process is as follows: A spatial density-based clustering method is used to identify crack clusters at crack centers. For the... Crack center point Its neighborhood can be represented as: ; In the formula, Indicated by The set of cracks in the neighborhood centered on the center, The neighborhood scale is indicated. The neighborhood scale can be adaptively determined based on the nearest neighbor distance of the crack, the local crack density, or the crack development intensity in the target area, making the cluster identification results more stable under different crack densities.

[0025] The distance between the centers of the cracks is represented by three-dimensional Euclidean distance: ; When the center point of a crack satisfies: ; Cracks located within the neighborhood of a core point but not satisfying the core point condition can be considered boundary points; cracks that are neither core points nor within the neighborhood of any core point can be considered noise cracks. The above... That is, a preset quantity threshold.

[0026] In complex fracture networks, core points typically correspond to dominant fractures within densely developed local fracture zones, boundary points are often transitional fractures surrounding fracture clusters, and noise points are mostly spatially discrete, isolated fractures with weak connections to the dominant fracture zone. After initial clustering, isolated noise fractures and local fragmentation clusters can be corrected by considering spatial proximity and geometric continuity. Isolated fractures close to the well and exhibiting continuity with neighboring clusters can be incorporated into adjacent fracture clusters. For target fracture zones that are excessively fragmented, they can be merged based on continuity and orientation coordination along the wellbore direction.

[0027] Based on the crack cluster identification results, key crack clusters are screened by considering the proximity relationships of the target object. Crack clusters with a certain degree of continuity in direction, a sufficient number of cracks within the cluster for analysis, and spatial connection with the target dominant crack zone are given priority. Adjacent key clusters with clear directional connection and local bridging significance can be combined into the same connectivity analysis unit.

[0028] Step S13: Abstract each target crack in the target crack cluster into a graph node, construct a candidate connected graph based on the spatial relationship between the graph nodes and each target crack, and determine the connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph; the connection evaluation index characterizes the connectivity potential between the corresponding target cracks.

[0029] In this embodiment, a candidate connected graph is constructed based on the spatial relationships between graph nodes and target cracks. This includes: determining the crack center distance and crack surface normal angle between the target cracks, and determining the crack scale, local stress direction, and crack location corresponding to each target crack; determining node pairs with connectivity potential from the graph nodes based on the crack center distance, crack surface normal angle, crack scale, local stress direction, and crack location, and constructing candidate edges between the node pairs to generate the candidate connected graph.

[0030] It should be noted that the nodes in the candidate connected graph include three types of connectivity relationships: direct connectivity, nearest neighbor connectivity, and associated connectivity. The direct connectivity relationship indicates that the cracks directly intersect or are tangent to each other. The nearest neighbor connectivity relationship indicates that the two cracks do not intersect and the spatial distance is less than a preset distance threshold. The associated connectivity relationship indicates that the two cracks are spaced apart and have a bridging relationship under the common constraints of spatial position, geometric shape, and local stress direction.

[0031] Specifically, the process for constructing candidate connected graphs and classifying connectivity levels is as follows: Within the critical crack cluster and its connectivity analysis unit, each crack is abstracted as a graph node, and crack pairs with potential connectivity are abstracted as candidate edges, thus establishing a candidate connected graph: ; In the formula, Represents a candidate connected graph. Represents the set of crack nodes. This represents the set of candidate edges. The establishment of candidate edges comprehensively considers factors such as crack center distance, crack size, crack surface normal angle, in-plane extension direction, local stress direction, and target location.

[0032] Candidate edges are classified into three categories based on their connectivity: the first category is direct connectivity, which refers to the connection formed by direct intersection or tangency between crack geometries; the second category is nearest neighbor connectivity, which refers to the connection between cracks that are not strictly intersecting, but have a small spacing and a strong in-plane continuity relationship; the third category is associated connectivity, which refers to the connection between cracks that have a certain interval, but have bridging or supplementary significance under the joint constraints of spatial position, geometric shape and local stress direction.

[0033] By filtering the candidate edges as described above, the complete crack network can be transformed into a candidate connected graph constrained by both spatial and mechanical conditions. This retains the potential connections between the dominant cracks while reducing the number of long-distance crack pairs that are obviously not meaningful for connection.

[0034] In addition, the connection evaluation indexes corresponding to the candidate edges between the graph nodes in the candidate connected graph are determined, including: determining the crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index corresponding to each candidate edge; wherein, the crack activity sub-index characterizes the ability of cracks to participate in connectivity, the distance attenuation sub-index characterizes the influence of the crack center distance on the connectivity possibility, the geometric compatibility sub-index characterizes the matching degree of two cracks in scale and attitude, the extension trend consistency sub-index characterizes the connection relationship of two cracks in the spatial extension direction, and the local stress direction consistency sub-index characterizes the degree of coordination between the direction of the connection between two cracks and the crack propagation direction controlled by the principal stress; the corresponding crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index are weighted and fused to obtain the connection evaluation indexes corresponding to the candidate edges between the graph nodes.

[0035] Specifically, the calculation process of candidate edge heuristic information (i.e., connection evaluation index) under multi-factor constraints is as follows: Calculate edge heuristics for each edge in the candidate graph. Let the ant start from node... Transfer to node The heuristic information is Then we have: ; In the formula, (i.e., the fracture activity sub-index) represents the fracture activity or connectivity potential, which is used to reflect the fracture's own ability to participate in connectivity. (i.e., distance decay sub-index) represents the distance decay term, used to characterize the impact of the crack center distance on connectivity probability; (i.e., geometric compatibility sub-index) represents the geometric compatibility term, used to characterize the degree of matching between the distance, scale, and orientation of the cracks; (i.e., the extension trend consistency sub-index) represents the extension trend consistency item, which is used to reflect the connection relationship between adjacent crack segments in the spatial extension direction; (i.e., the local stress direction consistency sub-index) represents the local stress direction consistency item, which is used to describe the degree of coordination between the connection direction and the crack propagation direction under the control of the principal stress; This indicates the influence of the target location, used to increase the analysis weight of cracks in the vicinity of the target object and near the dominant crack zone; to The weights of each factor.

[0036] The distance attenuation term can be expressed as: ; The stress direction consistency term can be expressed as: ; In the formula, For cracks With cracks The center distance, The characteristic length is related to the scale of the two cracks. It is the angle between the crack connection direction and the direction of the local maximum principal stress. Used to reduce the impact of long-distance cracks on connectivity determination Used to enhance candidate connections that extend along favorable stress directions.

[0037] All the above indicators are normalized to a unified numerical range. For direct connectivity and nearest-neighbor connectivity, the weight of the geometric compatibility term can be appropriately increased; for interconnected connectivity across local gaps, the weights of the extension trend consistency term and stress direction consistency term can be increased, so that the preservation of bridging edges is more consistent with the crack distribution pattern in the target area.

[0038] Step S14: Perform several rounds of path search in the candidate connected graph according to the target path search algorithm and each of the connection evaluation indicators. Determine the target edge from each of the candidate edges based on the results of the several rounds of path search. Construct the target connected graph based on each target edge and the corresponding graph node to complete the process of identifying the connected cracks corresponding to the target region.

[0039] In this embodiment, several rounds of path search are performed in the candidate connected graph according to the target path search algorithm and each of the connection evaluation indicators, including: initializing the pheromone intensity on each of the candidate edges, and performing several rounds of path search in the candidate connected graph according to the target path search algorithm, the pheromone intensity corresponding to each of the candidate edges, and each of the connection evaluation indicators; wherein, the target path search algorithm is an improved ant colony algorithm.

[0040] Specifically, the improved ant colony pathfinding process is as follows: An improved ant colony algorithm is used to perform multi-round path search on candidate connected graphs. Only ants at the node Choose to transfer to candidate node The probability can be expressed as: ; In the formula, Indicates the first Only ants are generated by nodes Transfer to node The probability, Representing an edge The intensity of pheromones on the surface This represents the heuristic information for candidate edges. Indicates the first Only ants at the node The set of optional nodes at that location and These represent the importance coefficients of pheromones and heuristic information, respectively.

[0041] After each round of search, the pheromones of candidate edges are updated based on pheromone evaporation and path quality: ; In the formula, Indicates the pheromone evaporation coefficient. This represents the pheromone increment obtained based on path quality, edge level, and multi-factor heuristic information. Through multiple rounds of search, candidate edges that stably participate in advantageous paths gradually acquire higher pheromone levels, while the influence of weakly connected edges that appear occasionally or lack structural significance diminishes.

[0042] In addition, determining the target edge from each of the candidate edges based on several rounds of path search results includes: determining the selection frequency, path contribution, and final pheromone intensity corresponding to each candidate edge based on several rounds of path search results, and determining the target edge from each of the candidate edges based on the selection frequency, the path contribution, and the final pheromone intensity.

[0043] Specifically, stable dominance subgraph extraction and connectivity index output: After multiple rounds of search, stable dominant edges are extracted based on the frequency of candidate edge selection, final pheromone strength, and path contribution. Let... Candidate edges The frequency of occurrence in the valid search path can be expressed as: ; In the formula, Candidate edges The number of times it was selected by a valid path. This represents the total number of valid search paths. The set of stable dominant edges can be represented as: ; In the formula, Represents the set of stable dominant edges. Indicates candidate edges, This indicates the frequency of candidate edges appearing in multiple rounds of search. This represents the minimum stable frequency threshold. This represents the pheromone intensity threshold.

[0044] A stable dominant connected subgraph is constructed from stable dominant edges and crack nodes participating in connectivity: ; In the formula, This represents a stable dominant connected subgraph. This represents the set of crack nodes that participate in stable connectivity. This represents the set of stable dominant edges. Further calculations are performed on this subgraph to determine the number of connected fractures, the number of stable edges, the number of connected components, the size of the largest connected component, the proportion of different connectivity types, the average well spacing of connected fractures, and the contribution of the dominant cluster. Fractures participating in the stable dominant subgraph can be output as valid connected fractures.

[0045] Key fracture clusters are identified through fracture cluster identification and well trajectory proximity screening. Direct connectivity, nearest neighbor connectivity, and associated connectivity are uniformly constructed into a candidate connectivity graph. Further, heuristic information on edge connections is calculated using fracture activity, geometric compatibility, and local stress direction. An improved ant colony path search is then employed to extract stable dominant connectivity subgraphs. This method reduces the interference of peripheral discrete fractures and weakly connected fractures on the analysis results, more stably identifies the dominant connectivity framework within the target area, and outputs connectivity analysis indicators such as effective connected fractures, stable edges, and connected components. This provides a basis for calculating effective stimulation volume, identifying advantageous stimulation zones, and evaluating fracturing effects.

[0046] In one specific embodiment, the fracture connectivity analysis method for complex fracture networks provided in this embodiment mainly includes the following steps, which are illustrated using a well perimeter network as an example: (1) Obtain information on the three-dimensional discrete fracture network, well trajectory, and local stress direction; (2) Determine the well perimeter analysis range based on the well trajectory and perform spatial clustering of fracture units; (3) Based on the distance relationship between the fracture cluster and the well trajectory, key fracture clusters around the well are screened; (4) Construct candidate connectivity graphs within key crack clusters and classify them into direct connectivity, nearest neighbor connectivity, and associated connectivity; (5) Calculate the edge-inspired information based on fracture activity, geometric compatibility, extension trend, stress direction and well perimeter position; (6) An improved ant colony path search is used to extract stable dominant connected subgraphs and output connected cracks, stable edges and connected components.

[0047] The three-dimensional discrete fracture network (DFN) is used to describe the spatial distribution of fractures around the wellbore after fracturing, the well trajectory is used to limit the analysis range effectively related to the wellbore, and the local stress direction is used to constrain the reasonable connection relationship between fractures. Input fracture attributes include spatial location, attitude, scale, aperture, and normal vector, which meet the needs of DFN modeling and subsequent connectivity graph construction.

[0048] In this example, a fracture network from a construction site in Northeast China was selected as the input object. The three-dimensional fracture network involved in the analysis contained a total of 287 fractures. Figure 2The three-dimensional spatial relationship between the fracture surface and the well trajectory is shown. The fractures are not uniformly distributed around the well, with a relatively concentrated fracture zone forming near the well trajectory, while a certain number of discrete fractures remain on the periphery. Among them, 105 fractures showed weak connections with the main fracture zone around the well in spatial clustering, exhibiting more of a scattered distribution at the edges and contributing little to the dominant connectivity around the well. Therefore, they were not the main focus of subsequent stable connectivity analysis.

[0049] like Figure 3 As shown, after spatial clustering of fracture center points, and combining the distance from fractures to the well trajectory and the continuous distribution of fractures within clusters, five key fracture clusters around the well were selected. Based on the spatial connectivity between clusters, these fracture clusters were further divided into two stable connectivity analysis units. Analysis unit 1, consisting of C5, C4, and C6, contains 110 fractures, of which 88 are stable, forming 98 stable connections. Analysis unit 0, consisting of C0 and C1, contains 51 fractures, of which 43 are stable, forming 45 stable connections. The two analysis units together contain 131 connected fractures and 143 stable connections. This indicates that the main connectivity relationships around the well are not evenly distributed across all fractures, but are concentrated in a few fracture zones with well-developed fractures and good spatial continuity.

[0050] After completing the screening of key crack clusters, such as Figure 4 As shown, a candidate connectivity graph is further constructed within the wellbore analysis unit, and an improved ant colony algorithm is used for path search. The establishment of candidate edges is not only based on whether the fractures intersect, but also comprehensively considers factors such as fracture spacing, scale matching, attitude coordination, in-plane extension direction, and stress direction consistency. In this embodiment, a total of 318 candidate edges are formed, of which 232 are nearest neighbor connections, 82 are associated connections, and 4 are direct connections, accounting for approximately 73.0%, 25.8%, and 1.3%, respectively.

[0051] In terms of connectivity, peri-well fractures rarely exhibit direct intersections; rather, adjacent fractures are closer together, have more consistent orientations, or are distributed in segmental succession along similar directions. While individual fractures may not directly intersect, they can still collectively form local connectivity channels within the fracturing zone. Associated connectivity accounts for approximately 25.8%, indicating some bridging relationships between fracture clusters; however, the significance of this connectivity needs to be determined through spatial location, geometric consistency, and stress direction. Direct connectivity accounts for only 1.3%, suggesting that the peri-well fracture network in this area is a complex connectivity structure composed of neighboring fractures, locally bridging fractures, and a small number of directly intersecting fractures.

[0052] like Figure 5As shown, after multiple rounds of path search and stable edge screening, 131 well-circle connected fractures and 143 stable edges were finally obtained. Connected fractures accounted for approximately 45.6% of the total number of input fractures, and stable edges accounted for approximately 49.8 per 100 fractures, with a stable edge to connected fracture ratio of approximately 1.09. The average well spacing of the connected fractures was 32.62 m, indicating that the stable connectivity results were mainly concentrated in the fracture-dense areas near the well trajectory and on both sides.

[0053] like Figure 6 As shown, after further subdividing the stable dominant connectivity results, four groups of interconnected fracture zones were obtained. Fractures within each group are interconnected via stable edges, while stable connections have not yet formed between different groups. The largest group contains 82 fractures, accounting for 62.6% of all interconnected fractures, corresponding to the most prominent fracture connectivity zone around the well and serving as a key area for identifying dominant channels after fracturing. The remaining three groups contain a total of 49 fractures, averaging approximately 16.3 fractures per group, indicating that in addition to the dominant connectivity zone, several smaller local connectivity zones have developed within the wellbore fracture network. This result reflects the segmentation differences between different well sections or fracture-rich areas, helping to distinguish between primary and secondary fracturing areas.

[0054] Figure 7 This study illustrates the composition of different connectivity types within the dominant connected network. Among the stable edges with clearly defined physical types, nearest-neighbor connections predominate (26 edges), direct connections are present (2 edges), and associative connections did not advance to the final stable stratification results. The final retained stable connections mainly consist of crack pairs that are close together and have relatively coordinated distribution directions; a small number of directly intersecting cracks serve as local fixing connections. Although associative connections constitute a certain proportion of the candidate edges, their connection distances are typically larger, requiring higher geometric compatibility and stress direction consistency; therefore, fewer associative connections are retained in the final stable results.

[0055] The above embodiments illustrate that the present invention can screen out key fracture clusters closely connected to the wellbore from a complex discrete fracture network, and further identify the main connected fracture zones around the well. The 131 stable connected fractures are mainly concentrated within two wellbore analysis units, with the largest connected fracture zone accounting for over 60%, reflecting that the effective connectivity structure after fracturing has obvious dominant fracture zone characteristics. The output results not only provide the number of fractures involved in connectivity and the number of stable connections, but also reflect the composition of the main connected zones, secondary connected zones, and different connectivity types, providing spatial structural basis for subsequent effective stimulation volume calculation, identification of the target object's advantageous stimulation zone, and evaluation of fracturing effect.

[0056] Therefore, this application solves the problems of unclear identification of key fracture clusters and susceptibility to interference from weakly connected fractures in the prior art by determining the candidate analysis space based on the target analysis object and the reservoir range, performing density clustering on the candidate fractures in the space to obtain fracture clusters, and then screening out the target fracture clusters based on the spatial relationship between each fracture cluster and the target analysis object. Furthermore, by determining the connectivity evaluation index that characterizes the connectivity potential between fractures and using the target path search algorithm to extract target edges from the candidate connectivity graph to construct the target connectivity graph, this application solves the problem of the prior art's difficulty in outputting the specific fracture units and spatial skeleton structure involved in the connectivity.

[0057] See Figure 8 As shown, an embodiment of the present invention discloses a device for identifying interconnected cracks, comprising: The spatial determination module 11 is used to acquire the three-dimensional discrete fracture network, target analysis object, target reservoir range and local stress direction information corresponding to the target region, and determine the candidate analysis space in the target region according to the target analysis object and the target reservoir range; The crack cluster screening module 12 is used to perform density clustering on the candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space to obtain a number of crack clusters, and to screen out the target crack cluster from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object. The connected graph construction module 13 is used to abstract each target crack in the target crack cluster as a graph node, construct a candidate connected graph based on the spatial relationship between the graph nodes and each target crack, and determine the connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph; the connection evaluation index characterizes the connectivity potential between the corresponding target cracks. The connectivity analysis module 14 is used to perform several rounds of path search in the candidate connected graph according to the target path search algorithm and each of the connection evaluation indicators, determine the target edge from each of the candidate edges according to the path search results of several rounds, and construct the target connected graph according to each target edge and the corresponding graph node to complete the process of identifying the connectivity cracks corresponding to the target region.

[0058] In some specific embodiments, the crack cluster screening module 12 may specifically include: The core point determination unit is used to count the number of other cracks contained in the neighborhood space corresponding to any candidate crack. If the number of cracks in the neighborhood space corresponding to any candidate crack is not less than a preset number threshold, then the candidate crack is determined as a core point. The boundary point determination unit is used to determine the cracks located in the neighborhood space corresponding to any candidate crack that do not meet the core point condition as the boundary points corresponding to any candidate crack, and to classify any candidate crack and the corresponding boundary points into the same crack cluster, so as to complete the density clustering operation corresponding to each candidate crack.

[0059] In some specific embodiments, the connected graph construction module 13 may specifically include: The crack center distance determination unit is used to determine the crack center distance and crack surface normal angle between each of the target cracks, and to determine the crack size, local stress direction and crack location corresponding to each of the target cracks respectively. The connected graph generation unit is used to determine the node pairs with connectivity potential from each of the graph nodes based on the crack center distance, the crack surface normal angle, the crack size, the local stress direction and the crack location, and to construct candidate edges between each of the node pairs to generate the candidate connected graph.

[0060] In some specific embodiments, the connected graph construction module 13 may specifically include: The sub-index determination unit is used to determine the crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index corresponding to each of the candidate connection edges. The crack activity sub-index characterizes the ability of a crack to participate in connectivity; the distance attenuation sub-index characterizes the influence of the crack center distance on connectivity probability; the geometric compatibility sub-index characterizes the degree of matching between two cracks in scale and orientation; the extension trend consistency sub-index characterizes the connection relationship between two cracks in the spatial extension direction; and the local stress direction consistency sub-index characterizes the degree of coordination between the direction of the connection between two cracks and the crack propagation direction controlled by the principal stress. The sub-index fusion unit is used to perform weighted fusion of the corresponding crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index to obtain the connection evaluation index corresponding to the candidate edges between each graph node.

[0061] In some specific embodiments, the connectivity analysis module 14 may specifically include: The path search unit is used to initialize the pheromone intensity on each of the candidate edges, and to perform several rounds of path search in the candidate connected graph according to the target path search algorithm, the pheromone intensity corresponding to each of the candidate edges, and each of the connection evaluation indicators; wherein, the target path search algorithm is an improved ant colony algorithm.

[0062] In some specific embodiments, the connectivity analysis module 14 may specifically include: The target edge determination unit is used to determine the selection frequency, path contribution, and final pheromone intensity of each candidate edge based on several rounds of path search results, and to determine the target edge from each candidate edge based on the selection frequency, the path contribution, and the final pheromone intensity.

[0063] Furthermore, embodiments of this application also disclose an electronic device, Figure 9 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0064] Figure 9 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the connectivity gap identification method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0065] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0066] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0067] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the connectivity gap identification method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0068] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for identifying connectivity gaps. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0069] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0070] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0072] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0073] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying connected cracks, characterized in that, include: The three-dimensional discrete fracture network, target analysis object, target reservoir range, and local stress direction information corresponding to the target region are obtained, and the candidate analysis space in the target region is determined based on the target analysis object and the target reservoir range. Density clustering is performed on the candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space to obtain several crack clusters, and the target crack cluster is selected from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object. Each target crack in the target crack cluster is abstracted as a graph node. A candidate connected graph is constructed based on the spatial relationship between the graph nodes and each target crack. The connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph is determined. The connection evaluation index characterizes the connectivity potential between the corresponding target cracks. In the candidate connected graph, several rounds of path search are performed based on the target path search algorithm and each of the connection evaluation indicators. Based on the results of the several rounds of path search, the target edge is determined from each of the candidate edges. The target connected graph is then constructed based on each target edge and the corresponding graph nodes to complete the process of identifying the connected cracks corresponding to the target region.

2. The method for identifying connected cracks according to claim 1, characterized in that, The density clustering process for candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space includes: The number of other cracks contained in the neighborhood space corresponding to any candidate crack is counted. If the number of cracks in the neighborhood space corresponding to any candidate crack is not less than a preset number threshold, then any candidate crack is determined as a core point. Cracks located in the neighborhood space corresponding to any candidate crack that do not meet the core point condition are identified as boundary points corresponding to any candidate crack, and any candidate crack and the corresponding boundary point are assigned to the same crack cluster to complete the density clustering operation corresponding to each candidate crack.

3. The method for identifying connected cracks according to claim 1, characterized in that, The step of constructing a candidate connected graph based on the spatial relationships between the graph nodes and each of the target cracks includes: Determine the center-to-center distance between each target crack and the angle between the crack surface normals, and determine the crack size, local stress direction and crack location corresponding to each target crack respectively; Based on the crack center distance, the crack surface normal angle, the crack size, the local stress direction, and the crack location, node pairs with connectivity potential are determined from each of the graph nodes, and candidate edges are constructed between each node pair to generate the candidate connected graph.

4. The method for identifying connected cracks according to claim 1, characterized in that, The nodes in the candidate connected graph include three types of connectivity relationships: direct connectivity, nearest neighbor connectivity, and associated connectivity. The direct connectivity relationship indicates that the cracks directly intersect or are tangent to each other. The nearest neighbor connectivity relationship indicates that the two cracks do not intersect and the spatial distance is less than a preset distance threshold. The associated connectivity relationship indicates that the two cracks are spaced apart and have a bridging relationship under the common constraints of spatial position, geometric shape, and local stress direction.

5. The method for identifying connected cracks according to claim 1, characterized in that, The step of determining the connection evaluation index corresponding to each candidate edge between the nodes in the candidate connected graph includes: Each candidate connection edge is assigned a corresponding sub-index for crack activity, a sub-index for distance attenuation, a sub-index for geometric compatibility, a sub-index for consistency of extension trend, and a sub-index for consistency of local stress direction. Specifically, the crack activity sub-index characterizes the ability of a crack to participate in connectivity; the distance attenuation sub-index characterizes the influence of the crack center distance on connectivity probability; the geometric compatibility sub-index characterizes the degree of matching between two cracks in scale and orientation; the consistency of extension trend sub-index characterizes the connection relationship between two cracks in the spatial extension direction; and the consistency of local stress direction sub-index characterizes the degree of coordination between the direction of the connection between two cracks and the crack propagation direction controlled by the principal stress. The corresponding crack activity sub-index, distance attenuation sub-index, geometric compatibility sub-index, extension trend consistency sub-index, and local stress direction consistency sub-index are weighted and fused to obtain the connection evaluation index corresponding to the candidate edges between each graph node.

6. The method for identifying connected cracks according to claim 1, characterized in that, The step of performing several rounds of path search in the candidate connected graph based on the target path search algorithm and each of the connection evaluation metrics includes: The pheromone intensity on each of the candidate edges is initialized, and several rounds of path search are performed in the candidate connected graph according to the target path search algorithm, the pheromone intensity corresponding to each candidate edge, and each connection evaluation index; wherein, the target path search algorithm is an improved ant colony algorithm.

7. The method for identifying connected cracks according to any one of claims 1 to 6, characterized in that, The step of determining the target edge from the candidate edges based on several rounds of path search results includes: The selection frequency, path contribution, and final pheromone intensity of each candidate edge are determined based on several rounds of path search results. The target edge is then determined from the candidate edges based on the selection frequency, path contribution, and final pheromone intensity.

8. A device for identifying interconnected cracks, characterized in that, include: The spatial determination module is used to acquire the three-dimensional discrete fracture network, target analysis object, target reservoir range and local stress direction information corresponding to the target region, and determine the candidate analysis space in the target region based on the target analysis object and the target reservoir range; The crack cluster screening module is used to perform density clustering on the candidate cracks in the three-dimensional discrete crack network corresponding to the candidate analysis space to obtain several crack clusters, and to screen out the target crack cluster from each crack cluster according to the spatial relationship between each crack cluster and the target analysis object. A connected graph construction module is used to abstract each target crack in the target crack cluster as a graph node, construct a candidate connected graph based on the spatial relationship between the graph nodes and each target crack, and determine the connection evaluation index corresponding to the candidate edges between each graph node in the candidate connected graph; the connection evaluation index characterizes the connectivity potential between the corresponding target cracks. The connectivity analysis module is used to perform several rounds of path search in the candidate connected graph according to the target path search algorithm and each of the connection evaluation indicators, determine the target edge from each of the candidate edges according to the path search results of the several rounds, and construct the target connected graph according to each target edge and the corresponding graph node to complete the process of identifying the connectivity cracks corresponding to the target region.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the connectivity crack identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the connectivity crack identification method as described in any one of claims 1 to 7.

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