Fracture fine characterization method and system based on microseismic monitoring data

Abstract

This application discloses a method and system for fine characterization of fracturing fractures based on microseismic monitoring data, relating to the field of oil and gas field development technology. The method includes: acquiring basic parameters of the target well; preprocessing microseismic event points of each fracturing segment of the target well based on well completion data to obtain preprocessed microseismic event points for each fracturing segment; performing noise reduction processing on the preprocessed microseismic event points for each fracturing segment to obtain noise-reduced microseismic event points for each fracturing segment; clustering the noise-reduced microseismic event points according to the number of perforation clusters in the fracturing segment to determine the effective microseismic event points for each perforation cluster; obtaining the characterization parameters of the fractures in each perforation cluster based on the effective microseismic event points of each perforation cluster using a fracture surface fitting method; and obtaining the fine characterization results of the fracturing fractures in the target well based on the characterization parameters of the fractures in each perforation cluster. This application enables fine characterization of fracturing fractures.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development technology, and in particular to a method and system for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data. Background Technology

[0002] Tight oil and shale oil are currently hot topics in unconventional oil and gas development. However, due to poor reservoir pore network connectivity and low permeability (overburden matrix permeability less than 0.1 × 10⁻⁶), they are subject to various challenges. -3 μm 2 Oil wells typically have no natural production capacity or fall below the industrial oil production floor, thus requiring specialized technologies to achieve industrial oil flow. Volumetric fracturing is an essential technology for the large-scale development of tight / shale oil reservoirs. By fracturing, it creates "highways" for oil and gas flow within the tight reservoir, thereby increasing crude oil production. Accurate characterization of fracturing parameters is crucial for evaluating fracturing effectiveness and predicting production capacity in tight / shale oil reservoirs.

[0003] Microseismic monitoring is a commonly used technique for characterizing hydraulic fracturing fractures. This technique involves deploying highly sensitive sensors on the surface or downhole to capture microseismic signals generated during rock fracturing, indirectly reflecting the extension and distribution of fractures. However, existing microseismic data processing methods are susceptible to background noise, resulting in large errors in fracture characterization. Especially when processing multiple fracture clusters, existing methods struggle to accurately distinguish the geometric characteristics of each cluster due to the often overlapping or uneven distribution of microseismic event points between different clusters. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data, which can make full use of microseismic monitoring data to accurately characterize the fractures formed by volumetric hydraulic fracturing and solve the problem of fine characterization of each cluster of fractures in multi-cluster hydraulic fracturing.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data, including:

[0007] Obtain the basic parameters of the target well; the basic parameters include the completion data, geostress direction and microseismic data of the target well.

[0008] Based on the completion data of the target well, the microseismic event points of each fractured section of the target well are preprocessed to obtain the preprocessed microseismic event points of each fractured section.

[0009] The microseismic event points of each fractured segment after preprocessing are denoised to obtain the denoised microseismic event points of each fractured segment.

[0010] For each fracturing segment, the noise-reduced microseismic event points are clustered according to the number of perforation clusters in the fracturing segment to determine the effective microseismic event points of each perforation cluster.

[0011] Based on the effective microseismic event points of each perforation cluster, the characterization parameters of the fractures in each perforation cluster are obtained by means of fracture surface fitting.

[0012] Based on the characterization parameters of the fractures in each perforation cluster in the target well, the detailed characterization results of the hydraulic fracturing fractures in the target well are obtained.

[0013] Optionally, the completion data of the target well includes geodetic coordinates, perforation time, number of perforation clusters, perforation location, wellbore trajectory data, well section length, and bridge plug location; the microseismic data of the target well includes the time and three-dimensional spatial coordinates of microseismic event points for each fracturing section.

[0014] Optionally, based on the completion data of the target well, the microseismic event points of each fractured section of the target well are preprocessed to obtain the preprocessed microseismic event points of each fractured section, specifically including:

[0015] Based on the well trajectory data, well section length, and bridge plug position in the well completion data, determine the starting and ending positions of each fracturing section on the well trajectory.

[0016] The minimum curvature method was used to convert the starting and ending positions of each fracturing segment into three-dimensional coordinates, thus obtaining the spatial range of each fracturing segment.

[0017] Based on the spatial range of each fracturing segment, the microseismic event points located between the starting and ending points and perpendicular to the corresponding horizontal well segment are determined as the microseismic event points corresponding to the fracturing segment.

[0018] Optionally, the microseismic event points of each fracturing segment after preprocessing are denoised to obtain the denoised microseismic event points of each fracturing segment, specifically including:

[0019] For each fracturing segment, the microseismic event points of each pre-processed fracturing segment are sorted according to the order of measurement time to obtain the sorting results.

[0020] Based on the sorting results, microseismic event points that have no other event points within the surrounding preset range are removed.

[0021] A density clustering algorithm is used to cluster microseismic events based on a set neighborhood radius and core point density threshold, and event points identified as noise are removed.

[0022] A dual verification method based on spatiotemporal neighborhood continuity is used to determine the remaining microseismic event points. The determination includes: defining a spatiotemporal neighborhood for each microseismic event point, which consists of a time window and a spatial radius; for each microseismic event point, checking whether there are other event points within the spatiotemporal neighborhood; if no other event points exist, the microseismic event point is directly determined as an isolated noise point; if other microseismic event points exist, calculating the Euclidean distance between the microseismic event point and all other microseismic event points within the spatiotemporal neighborhood; if all Euclidean distances are greater than the set spatial radius, the microseismic event point is determined as a noise point; the noise points are removed to obtain the microseismic event points after noise point removal; the time window is a set duration covering the time before and after the occurrence of the microseismic event point; the spatial radius is the maximum allowable spatial span between two adjacent microseismic event points under the continuous crack propagation mode.

[0023] Based on the anomaly coefficient, secondary anomaly detection is performed on each microseismic event point after noise points are removed, and microseismic event points with anomaly coefficients higher than 2 are removed to obtain the microseismic event points of each fracturing segment after noise reduction.

[0024] Optionally, the formula for calculating the anomaly coefficient is:

[0025] .

[0026] In the formula, p is the microseismic event point, and LWD(p) is the local weighted density of the microseismic event point p. ; For each k-neighborhood point p of a microseismic event, sum the weighted reach distances from all points o to p; wreach_dist k (p, o) is the weighted reachable distance from point p to point o. reach_dist k (p,o) is the reachable distance from point p to point o; k_dist(o) is the distance from point o to its k-th nearest neighbor, d(p,o) is the Euclidean distance between points p and o, and w(p,o) is the weight between points p and o. ε is a positive number; LP(p) is the neighborhood average density of point p. ; It sums the local weighted densities of all neighboring points of point p; N k (p) is the set of the k nearest points to the microseismic event point p; o is N k (p) represents any microseismic event point; LWD(o) is the local weighted density of point o.

[0027] Optionally, based on the number of perforation clusters in the fracturing section, the noise-reduced microseismic event points are clustered to determine the effective microseismic event points for each perforation cluster, specifically including:

[0028] Based on the fundamental principle that pressure cracks extend along the direction of maximum principal stress, the two-dimensional plane orientation of crack propagation is obtained.

[0029] The preset number of clusters K in the clustering algorithm is set to the number of perforation clusters.

[0030] Based on the perforation location of the fracturing section and the two-dimensional plane orientation of the fracture propagation, each microseismic event point is assigned to the perforation cluster corresponding to the nearest perforation location.

[0031] The perforation location of the fracturing section is set as the initial centroid; the centroid is the geometric center of each microseismic event point in the cluster set; the coordinates of the centroid are determined by the arithmetic mean of the coordinates of each microseismic event point in the cluster set.

[0032] Calculate the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster, and update the centroid position.

[0033] Based on the updated centroid position, the microseismic event points within each perforation cluster are redistributed. The sum of squared distances between each microseismic event point and its corresponding centroid within all perforation clusters of the fracturing section is calculated until the absolute value of the difference between the sums of squared distances of two consecutive times is less than a set threshold and the centroid coordinates no longer change. This process yields the clustered microseismic event points.

[0034] For each perforation cluster, the clustered microseismic event points are sorted by time. Starting from the perforation location, the Euclidean distance between each microseismic event point and the perforation location is calculated. Starting from the third microseismic event point in the sequence, each microseismic event point is checked one by one. If the Euclidean distance between any microseismic event point and the perforation location is less than the Euclidean distance of the farthest microseismic event point from the perforation location among all the preceding microseismic event points, then the microseismic event point is removed, and the effective microseismic event points of each perforation cluster are obtained.

[0035] Optionally, the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster is calculated, specifically including:

[0036] According to the formula Calculate the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster.

[0037] Where K is the number of perforation clusters within the fracturing section, consistent with the number of cluster categories set by the clustering algorithm; C i Let x be the set of microseismic event points within the i-th perforation cluster; x is the set of events belonging to C. i Microseismic event points; μ iLet |x-μ be the centroid of the i-th perforation cluster; i | |2 Let the microseismic event point x and the centroid μ be... i The square of the Euclidean distance between them.

[0038] Optionally, based on the effective microseismic event points of each perforation cluster, the characterization parameters of the fractures in each perforation cluster are obtained through a fracture surface fitting method, specifically including:

[0039] Based on the valid event points of each perforation cluster, principal component analysis is used to obtain the fracture direction of the target well fracturing section.

[0040] A random simulation consensus algorithm is used to iteratively fit the effective event points of each perforation cluster under a set threshold to generate a three-dimensional crack surface equation.

[0041] When the angle deviation between the normal vector of the fracture surface equation of the fractured section and the normal vector of the fracture direction of the fractured section is within a preset range, the three-dimensional fracture surface equation of the fractured section of the target well is obtained.

[0042] Based on the three-dimensional fracture surface equation of the target well fracturing section, the fracture length, height and azimuth of the corresponding perforation cluster are calculated to obtain the characterization parameters of the fractures in each perforation cluster.

[0043] Secondly, this application provides a fine characterization system for hydraulic fracturing fractures based on microseismic monitoring data, including:

[0044] The parameter acquisition module is used to acquire the basic parameters of the target well; the basic parameters include the completion data, geostress direction and microseismic data of the target well.

[0045] The preprocessing module is used to preprocess the microseismic event points of each fractured section of the target well based on the completion data of the target well, so as to obtain the preprocessed microseismic event points of each fractured section.

[0046] The noise reduction module is used to reduce the noise of microseismic event points in each fracturing segment after preprocessing, so as to obtain the noise-reduced microseismic event points in each fracturing segment.

[0047] The clustering module is used to cluster the noise-reduced microseismic event points for each fracturing segment based on the number of perforation clusters in the fracturing segment, and to determine the effective microseismic event points for each perforation cluster.

[0048] The fitting module is used to obtain the characterization parameters of the fractures in each perforation cluster based on the effective microseismic event points of each perforation cluster through the fracture surface fitting method.

[0049] The characterization result output module is used to obtain the fine characterization results of the fracturing fractures in the target well based on the characterization parameters of each perforation cluster fracture in the target well.

[0050] Optionally, the completion data of the target well includes geodetic coordinates, perforation time, number of perforation clusters, perforation location, wellbore trajectory data, well section length, and bridge plug location; the microseismic data of the target well includes the time and three-dimensional spatial coordinates of microseismic event points for each fracturing section.

[0051] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0052] This application provides a method and system for fine characterization of fracturing fractures based on microseismic monitoring data. First, by acquiring basic parameters such as well completion data, geostress direction, and microseismic data of the target well, the microseismic event points of each fracturing segment are preprocessed to remove some interference factors. Then, noise reduction processing is performed to further improve data quality. Next, the noise-reduced microseismic event points are clustered according to the number of perforation clusters, assigning the microseismic event points of each fracturing segment to each perforation cluster. The characterization parameters of the fractures in each perforation cluster are obtained using a fracture surface fitting method. Finally, by integrating the characterization parameters of the fractures in each perforation cluster, the fine characterization results of the fracturing fractures in the target well are obtained. This allows for full utilization of microseismic monitoring data to accurately characterize fractures formed by volumetric fracturing, solving the problem of fine characterization of each fracture cluster in multi-cluster fracturing processes. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data, provided in one embodiment of this application.

[0055] Figure 2 This is a schematic diagram illustrating noise point identification in a microseismic event point according to an embodiment of this application.

[0056] Figure 3 This is a fracture morphology map obtained when the microseismic event points in the fractured segment are not clustered, as provided in an embodiment of this application.

[0057] Figure 4 The crack characterization morphology is obtained by applying a stochastic simulation consensus algorithm after denoising and clustering microseismic event points in one embodiment of this application.

[0058] Figure 5 This is a diagram showing the morphological characteristics of each cluster of fractures in all fracturing sections of a shale oil well, provided as an embodiment of this application.

[0059] Figure 6 This is a schematic diagram of the functional modules of a fine characterization system for hydraulic fracturing based on microseismic monitoring data, provided in an embodiment of this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] In the prior art, referring to the invention patent application with application number CN 202410994973.5, a fracture modeling method and device based on microseismic event clustering analysis is proposed. This method re-divides perforation segments according to the location of the hydraulic fracturing section and associates them with corresponding microseismic event points. It then uses a clustering algorithm to filter noise data and constructs a fracture plane. However, this patent only uses the density clustering algorithm (DBSCAN) for noise reduction when inverting fracture parameters based on microseismic data, without secondary screening, resulting in a low noise removal rate. Referring to the invention patent application with application number CN 202010012257.4, this invention provides a scatter point clustering analysis method and system for reservoir stimulation microseismic events. It acquires scatter points of reservoir stimulation microseismic events in real time, calculates the error ellipse of all microseismic events, and iterates to minimize the objective function, thus accurately describing and finely characterizing artificial fractures in reservoir stimulation. However, this patent does not consider the stress direction constraint when fitting hydraulic fracturing, resulting in a large deviation between the inversion results and the actual formation, and it cannot obtain the fracture parameters of each perforation cluster.

[0062] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment provides a method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data, including:

[0065] Step 101: Obtain the basic parameters of the target well; the basic parameters include the completion data, geostress direction and microseismic data of the target well.

[0066] Step 102: Based on the completion data of the target well, preprocess the microseismic event points of each fractured section of the target well to obtain the preprocessed microseismic event points of each fractured section.

[0067] Step 103: Perform noise reduction processing on the microseismic event points of each fracturing segment after preprocessing to obtain the noise-reduced microseismic event points of each fracturing segment.

[0068] Step 104: For each fracturing segment, based on the number of perforation clusters in the fracturing segment, the noise-reduced microseismic event points are clustered to determine the effective microseismic event points for each perforation cluster.

[0069] Step 105: Based on the effective microseismic event points of each perforation cluster, the characterization parameters of the fractures in each perforation cluster are obtained by means of the fracture surface fitting method.

[0070] Step 106: Based on the characterization parameters of the fractures in each perforation cluster in the target well, obtain the fine characterization results of the hydraulic fracturing fractures in the target well.

[0071] In this embodiment, for the fine characterization of fracturing fractures based on microseismic monitoring data after volumetric fracturing of shale oil, the event points obtained by microseismic monitoring are processed by noise reduction, clustering and fitting to obtain the effective microseismic event points of the fractures corresponding to each perforation cluster. Then, the fracture characterization parameters of each perforation cluster are calculated based on the event points.

[0072] In an exemplary embodiment, when performing steps 101-106, the specific steps may be as follows:

[0073] (a) Obtain completion data for the target well, including geodetic coordinates (planar position coordinates, vertical depth), perforation time, perforation location, number of perforation clusters, geostress direction, well trajectory, well section length, and bridge plug location.

[0074] (b) Acquire microseismic data of the target well, including the time and three-dimensional coordinates of microseismic event points of each fractured section, and preprocess the event points of each fractured section of the target well.

[0075] Based on the wellbore trajectory, well section length, and bridge plug position in the well completion data, determine the starting and ending positions of each fracturing segment on the wellbore trajectory. For example, the starting point of the first perforation cluster is recorded as the starting point of the first fracturing segment; since the bridge plug position is the dividing point between two adjacent fracturing segments, the ending point of the first fracturing segment is the position of bridge plug 1 on the wellbore; the starting point of the second fracturing segment is the position of bridge plug 1 on the wellbore, and the ending point is the position of bridge plug 2 on the wellbore, and so on; the ending point of the last perforation cluster is the ending point of the last fracturing segment.

[0076] The minimum curvature method was used to convert the start and end positions of each fracturing segment into three-dimensional coordinates, thus obtaining the spatial extent of each segment. First, the two adjacent measuring points above and below the start or end position of each fracturing segment were found in the survey data. Then, the inclination angle (θ) and azimuth angle (φ) at the start or end position were calculated using interpolation. Finally, the minimum curvature method was used to calculate the three-dimensional coordinates at the start and end positions.

[0077] X=X'+ΔMD×[sin(θ1)sin(φ1)+sin(θ2)sin(φ2)] / 2×RF.

[0078] Y=Y'+ΔMD×[sin(θ1)cos(φ1)+sin(θ2)cos(φ2)] / 2×RF.

[0079] Z=Z'+ΔMD×[cos(θ1)+cos(θ2)] / 2×RF(1).

[0080] In the formula, RF is the curvature factor, RF=2 / ΔMD×tan(ΔMD / 2)), θ1, φ1 and θ2, φ2 are the well inclination angle and azimuth angle of two adjacent measuring points, respectively; X, Y, Z are the coordinates of the starting position or ending position in the X, Y, Z directions, respectively; X', Y', Z' are the coordinates of the adjacent upper measuring point in the X, Y, Z directions, respectively.

[0081] Based on the spatial range of each fracturing segment, the microseismic event points located between the starting and ending points and perpendicular to the corresponding horizontal well segment are identified as the microseismic event points corresponding to that fracturing segment, thus obtaining the microseismic event points corresponding to each fracturing segment.

[0082] In the noise reduction stage, the main steps for noise reduction of microseismic event points using the density clustering algorithm are as follows:

[0083] 1) Set two parameters: neighborhood radius (Eps) and core point density threshold (MinPts); 2) If the number of sample points within the neighborhood radius of a microseismic event point is greater than or equal to MinPts, then that point is considered a core point; 3) Starting from an unvisited core point, find all core points and boundary points that are density-reachable from it. These points form a cluster. Find the next unvisited core point and repeat this process. Finally, points that are not assigned to any cluster are considered noise points (density reachable from p1 to p). n If every point is a core point and every point lies within the previous Eps, then the path from p1 to p2 is said to be a path from p1 to p2. n Density is achievable if the boundary point is within the Eps of the core point (then the density of the boundary point and the core point is achievable). Specific noise reduction processing can be as follows:

[0084] Preliminary screening of microseismic event points was conducted by sorting the microseismic event points of each fracturing segment by time. The corresponding spatial locations were obtained based on the chronological order, and microseismic event points that occurred alone without any other event points within a preset surrounding area were eliminated.

[0085] A density clustering algorithm is used to cluster microseismic events based on a set neighborhood radius and core point density threshold, and event points identified as noise points are removed. A dual verification method based on spatiotemporal neighborhood continuity is used to evaluate the remaining microseismic event points. First, a spatiotemporal neighborhood is defined for each event point, which is jointly defined by a time window (covering a specific duration before and after the event point's occurrence) and a spatial radius (the maximum allowable spatial span between two adjacent event points under the continuous crack propagation mode). For each event point, it is checked whether there are other event points in its spatiotemporal neighborhood. If there are no other event points, the point is directly identified as an isolated noise point. If other event points exist, the Euclidean distance between the point and all other event points in the spatiotemporal neighborhood is calculated. If all these distances are greater than the set spatial radius, the point is also identified as a noise point. Noise points that meet any of the above conditions are removed, thus obtaining the microseismic event points after noise point removal.

[0086] Secondary anomaly detection is performed on the microseismic event points after noise removal based on the anomaly coefficient. Event points with significant differences in location characteristics between the local area and surrounding points are removed to obtain the noise-reduced microseismic event points.

[0087] Specifically, in the denoising stage of seismic event points, the density clustering algorithm (DBSCAN) is used to denoise the event points and improve the accuracy of the data. For cluster analysis of a specific point set, the DBSCAN algorithm is determined by two key parameters: the neighborhood radius Eps and the core point density threshold MinPts. Let the microseismic event point set be M = {m1, m2, ..., m...} N Let m ∈ M be a point in a point set M, and let x be any other point in the point set different from m. Then the ε-neighborhood of point m is a circular region with radius Eps, defined as:

[0088] (2).

[0089] In the formula, m is a point in the point set; x is any other point in the point set that is different from m; M is the microseismic event point set; ε is the neighborhood radius; and d(x,m) is the distance between the two points.

[0090] For any point m, its density is defined as the number of points falling within its Eps neighborhood, and its size is determined by the neighborhood radius Eps, denoted by ρ(m):

[0091] (3).

[0092] In the formula, ρ(m) is the number of points in the neighborhood of Eps; Eps is a circular region with a neighborhood radius of ε.

[0093] Given a neighborhood radius and a core point density threshold, the set of all identified core points M in the point set M is... c It can be represented as:

[0094] (4).

[0095] In the formula, MinPts is the core point density threshold.

[0096] If a point falls within the neighborhood of other core points, but its density is less than MinPts, then that point is identified as a boundary point, and the set of boundary points is M. b It can be represented as:

[0097] (5).

[0098] If the density of a point is less than the density threshold MinPts, and the point is not located in the Eps neighborhood of any core point, then the point is identified as a noise point, and the set of noise points is M. n :

[0099] (6).

[0100] like Figure 2-5 As shown, after DBSCAN processing, the point set is divided into three types based on two parameters: neighborhood radius and core point density threshold: core points, boundary points, and noise points.

[0101] (7).

[0102] In the formula, M c For the set of core points; M b M is the set of boundary points; n This is the set of noise points.

[0103] The anomaly coefficient LTP(p) of the weighting function is calculated using the following method:

[0104] (1) Calculate the reachable distance from point p to point o:

[0105] (8).

[0106] Where k_dist(o) is the Euclidean distance from point o to its k-th nearest neighbor, and d(p,o) is the Euclidean distance between point p and point o.

[0107] (2) The weight w(p, o) between point p and point o is calculated as follows:

[0108] (9).

[0109] In the formula, w(p, o) is the weight between point p and point o, and ε is a very small positive number, such as 0.000001.

[0110] (3) Calculate the weighted reachability distance from point p to point o:

[0111] (10).

[0112] wreach_dist k (p, o) is the weighted reachability distance from point p to point o; reach_dist k (p,o) is the reachable distance from point p to point o, and w(p,o) is the weight between point p and point o.

[0113] (4) Calculate the local weighted density LWD(p) at point p:

[0114] (11).

[0115] In the formula, p represents the microseismic event point, and N... k (p) is the set of the k nearest points to the microseismic event point p (excluding p itself). It is the sum of the weighted reachable distances from all points o in the k-neighborhood of point p to p.

[0116] (5) Calculate the neighborhood average density LP(p) of point p:

[0117] (12).

[0118] In the formula, o represents N. k Any microseismic event point in (p) It sums the local weighted densities of all neighboring points of point p.

[0119] (6) Determine whether p is noise by calculating the anomaly coefficient LTP(p):

[0120] (13).

[0121] Set a threshold (usually 2), and if LTP(p) > the threshold, mark point p as an outlier.

[0122] In the microseismic event point clustering stage, clustering methods are used to cluster the points. The number of clusters generated by the clustering algorithm is consistent with the number of perforation clusters in each fracturing segment. The initial centroid is optimized and accurate clustering is achieved based on the number of perforation clusters in the field. The specific steps are as follows:

[0123] Based on the fundamental principle that pressure cracks extend along the direction of maximum principal stress, the two-dimensional plane orientation of crack propagation is obtained.

[0124] The preset number of clusters K in the clustering algorithm is set to the number of perforation clusters.

[0125] Based on the perforation location of the fracturing section and the two-dimensional plane orientation of the fracture propagation, each microseismic event point is assigned to the perforation cluster corresponding to the nearest perforation location.

[0126] The perforation location of the fracturing section is set as the initial centroid; the centroid is the geometric center of each microseismic event point in the cluster set; the coordinates of the centroid are determined by the arithmetic mean of the coordinates of each microseismic event point in the cluster set.

[0127] Calculate the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster, and update the centroid position.

[0128] Based on the updated centroid position, the sum of squared distances between each microseismic event point in each perforation cluster and its corresponding centroid is recalculated until the absolute value of the difference between the sum of squared distances of the two consecutive calculations is less than a set threshold and the centroid coordinates no longer change. This process yields the clustered microseismic event points.

[0129] For each perforation cluster, the clustered microseismic event points are sorted by time. Starting from the perforation location, the Euclidean distance between each microseismic event point and the perforation location is calculated. Starting from the third event point in the sequence, each event point is checked one by one. If the Euclidean distance between an event point and the perforation location is less than the Euclidean distance of the farthest event point from the perforation location among all the preceding event points, the point is determined to violate the fracture spatial propagation law and is removed. In this way, the effective microseismic event points of each perforation cluster are obtained.

[0130] Specifically, the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster is calculated, including:

[0131] According to the formula, calculate the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster.

[0132] (14).

[0133] Where K is the number of perforation clusters within the fracturing section, consistent with the number of cluster categories set by the clustering algorithm; C i Let x be the set of microseismic event points within the i-th perforation cluster; x is the set of events belonging to C. i Microseismic event points; μ i Let |x-μ be the centroid of the i-th perforation cluster; i | |2 Let the microseismic event point x and the centroid μ be...i The square of the Euclidean distance between them.

[0134] Specifically, in the stage of generating the three-dimensional crack surface equation, it can be done as follows:

[0135] (a) The Random Simulation Consensus Algorithm (RANSAC) is used to iteratively fit each cluster of event points, with ≥100,000 iterations, to generate a three-dimensional fracture surface equation. The main steps of the RANSAC algorithm are: 1) Randomly select three points that are not on the same line from the effective microseismic event points of the perforation cluster to perform preliminary fitting of the fracture surface; 2) Calculate the Euclidean distance from the remaining points to the fracture surface, and judge the goodness of fit τ based on the preset distance. The goodness of fit refers to the number of microseismic event points within the preset distance, where the microseismic event points within the preset distance are also called interior points; 3) Set the number of iterations, and obtain the three-dimensional fracture surface that best fits each cluster of microseismic event points (i.e., the fracture surface with the most interior points; if there are multiple surfaces with the same number of interior points, select the surface with the smallest root mean square error of the distance from all interior points to the fracture surface), which is the pressure fracture of the perforation cluster.

[0136] (b) If the in-point coverage is ≤60%, 1000 data points are randomly generated within a range of 0 to a preset distance plus 20. In each iteration, one point is selected from these 1000 data points and set as the distance threshold until the in-point coverage is >60%. The in-point coverage is the ratio of the number of in-points to the total number of event points in the perforation cluster.

[0137] (c) Analyze the spatial coordinates of microseismic event points. Using principal component analysis, determine the overall orientation of the fractures reflected by the microseismic event points. This means the deviation angle from the direction of the maximum principal stress should be within 15°. Multiple fracture clusters in each fracturing segment should have a similar spatial distribution, meaning the deviation of each fracture direction from the principal component direction should be within a certain range. Ideally, the maximum deviation should not exceed 10°-20°. If the deviation angle between individual fractures and the principal component direction is too large, adjust the threshold of the stochastic simulation consensus algorithm and refit the fracture until its orientation meets the requirements.

[0138] Specifically, in the microseismic event point clustering and identification stage, a random simulation consensus algorithm is used to accurately obtain highly consistent cracks from the microseismic point set of each perforation cluster, thereby achieving accurate characterization of cracks in each perforation cluster.

[0139] Assuming the set of microseismic event points is M, each event point in the set can be represented according to its validity as follows:

[0140] (15).

[0141] In the formula, MI A subset of valid event points; M O A subset of noise points; m i For each event point within the set; x i The x-axis coordinate of the event point; y i The y-coordinate of the event point; the z-coordinate. i The z-axis coordinate of the event point.

[0142] Assuming the orientation parameter of a single crack is θ, arbitrarily select a subset M from the point cloud. I By fitting the crack surface, the orientation of the crack surface can be expressed in terms of the intercept:

[0143] (16).

[0144] In the formula, a is the crack intercept on the x-axis; b is the crack intercept on the y-axis; and c is the crack intercept on the z-axis.

[0145] The Euclidean distance from any point in the point cloud to the crack surface can be obtained using the intercept:

[0146] (17).

[0147] In the formula, Let event point m i Euclidean distance to the crack surface θ; m i This is to remove any event points outside the subset of the fitted crack surface, i.e., m∈(M-MI).

[0148] After initial fitting, this embodiment calculates the goodness-of-fit index during the verification phase based on Euclidean distance. Intuitively, the goodness-of-fit index in RANSAC is the total number of event points within a certain distance threshold of the assumed crack surface. Let the goodness-of-fit index be sn, and the distance threshold be τ, then the goodness-of-fit index can be obtained by judging the threshold condition:

[0149] (18).

[0150] In the formula, τ is the distance threshold; c(m i ;θ) is the judgment value within the distance threshold, when event point m i Euclidean distance to crack surface θ When the distance is less than the distance threshold τ, the event point m i Since m is an interior point, therefore c(m) i ;θ)=1;When event point m i Euclidean distance to crack surface θ When the distance is greater than or equal to the distance threshold τ, the event point m i It is not an interior point, therefore c(m) i ;θ)=0. (MM)I c(m) within the range i By adding the values ​​of ;θ), the number of interior points sn within the distance threshold can be obtained.

[0151] The formula for the root mean square error of the distance from the interior point to the crack surface is:

[0152] (19).

[0153] Where N is the number of interior points contained within the crack surface, d i It is the Euclidean distance from the i-th interior point to the crack surface. It is the sum of the squares of the distances from all interior points to the crack surface.

[0154] Specifically, after crack identification, principal component analysis is used to determine the overall direction of the crack segment, thereby adjusting the incorrectly identified cracks and ensuring that the maximum deviation between the direction of each cluster of cracks in each fracturing segment and the direction of the maximum principal stress does not exceed 10°-20°.

[0155] The specific steps of principal component analysis are as follows: If there are n event points in a microseismic event, and the spatial coordinates of each point are a three-dimensional vector (x, y, z)... i y i , z i ), where i = 1, 2, 3, ..., n.

[0156] Subtract the mean vector from each event point to obtain the centered data:

[0157] (20).

[0158] in, This represents the average coordinates of the event point across three dimensions. This represents the three-dimensional coordinates after centralization.

[0159] The covariance matrix C of the centered data is:

[0160] (twenty one).

[0161] All summations are in the range of i=1 to n.

[0162] Perform eigenvalue decomposition on the covariance matrix C:

[0163] (twenty two).

[0164] We obtain the eigenvalues ​​λ1≥λ2≥λ3≥0 and the corresponding eigenvectors v1,v2,v3. Since C is a real symmetric matrix, the eigenvectors are mutually orthogonal unit vectors.

[0165] The principal component direction corresponds to the eigenvector v1 of the largest eigenvalue λ1, representing the direction of the greatest data change, i.e. the main trend direction of the distribution of microseismic points.

[0166] Specifically, based on the three-dimensional fracture surface equation of the target well fracturing section, the fracture length, height, and azimuth of the corresponding perforation cluster are calculated to obtain the characterization parameters of each perforation cluster fracture. This can be achieved through the following steps:

[0167] For a given perforation cluster, the interior points of the fracture surface are projected onto the xy two-dimensional plane, and the maximum Euclidean distance is calculated as the length of the fracturing fracture corresponding to the perforation cluster.

[0168] For this perforation cluster, the azimuth angle of the fracturing fracture is determined by the angle between the maximum fracture length and the coordinate axis.

[0169] For this perforation cluster, the fracturing fracture height is calculated by the difference in the vertical coordinates of the inner points.

[0170] Repeat the above steps to calculate the characterization parameters of the cracks in each perforation cluster; the characterization parameters of the cracks include crack length, height and azimuth.

[0171] Specifically, when performing parametric characterization of cracks, this embodiment considers the irregularity of the three-dimensional crack surface. Therefore, the crack length is obtained by performing two-dimensional projection of the interior points of each cluster of cracks and calculating the maximum length using Euclidean distance as the crack length. At this time, the angle between the straight line and the x-axis is the azimuth angle of the crack, and the crack height is the maximum height difference of the interior points.

[0172] The Euclidean distance between the i-th interior point and the j-th interior point is:

[0173] (twenty three).

[0174] Where, x i x represents the x-coordinate of the i-th interior point; j The x-coordinate of the j-th interior point; y i The y-coordinate represents the ordinate of the i-th interior point; j This represents the ordinate of the j-th interior point.

[0175] The angle θ between each crack and the x-axis is:

[0176] (twenty four).

[0177] (25).

[0178] The crack height is the maximum difference in the z-direction between the interior points:

[0179] (26).

[0180] In the formula, z i Let z be the coordinates of the i-th interior point in the z-direction; z j Let be the coordinates of the j-th interior point in the z-direction.

[0181] In some embodiments, the significance of full-well fracturing parameter characterization lies in the fact that the fracture length, height, and azimuth of each perforation cluster can be calculated independently, thereby truly reflecting the differences in the propagation capacity of each cluster and accurately evaluating the stimulation effect of each cluster. Traditional methods process the entire data as a whole, resulting in only one fracture per fracturing section. However, in actual production, each perforation cluster corresponds to only one fracture, leading to large fracture characterization errors that may mask abnormal propagation of some perforation clusters (such as fractures that are too short or too long). Clustering allows for precise identification of the development of each cluster and the design of targeted production plans. Furthermore, when multiple clusters propagate, fractures may exhibit intersecting, branching, or isolated distributions. Clustering characterization can clearly distinguish the geometric morphology of each fracture, thus more accurately demonstrating the interactions between fractures (such as stress shadowing effects) and guiding cluster spacing optimization.

[0182] This application also provides specific implementation examples, as follows:

[0183] The method was applied to field data from the second fracturing stage of a shale oil horizontal well. Table 1 shows the microseismic event points measured in the first two hours based on time acquisition. The optimized DBSCAN parameters have a neighborhood radius of Eps=25 m, a core point density threshold of MinPts=4, an anomaly coefficient LTP(p)=2, and a noise point rejection rate increased by 30%, thus effectively identifying noise points in the microseismic testing process and improving the accuracy of the method.

[0184] Table 1. Microseismic event points sorted by time.

[0185]

[0186] The method provided in this embodiment is used to characterize the parameters of the hydraulic fracture. Figure 2 This is a schematic diagram for identifying noise points in microseismic event points, where TVD represents vertical depth, and solid rhombuses are noise points, which are removed and then clustered. Figure 3 The crack characterization results obtained using the traditional method are shown. Since the traditional method does not perform clustering processing, only one crack can be identified for each fracturing segment. Figure 4The fracture characterization morphology was obtained by applying a stochastic simulation consensus algorithm after denoising and clustering the microseismic event points. The fracturing section included four perforation clusters, each corresponding to one fracture; therefore, a total of four fractures were characterized. The first fracture had a length of 254.71 m, a height of 24.89 m, and an azimuth of 0.54°; the second fracture had a length of 229.55 m, a height of 19.21 m, and an azimuth of 2.34°; the third fracture had a length of 244.84 m, a height of 23.75 m, and an azimuth of 1.06°; and the fourth fracture had a length of 256.82 m, a height of 24.35 m, and an azimuth of 1.46°. Comparison of these results with those from fiber optic monitoring and fracture propagation simulation shows that the fracture characterization results obtained for each perforation cluster are accurate, validating the effectiveness of the method. Meanwhile, based on the energy distribution data obtained from microseismic monitoring, the stress field characteristics and stratum dip angle parameters in the geological model were coupled, and the seismic wave propagation theory and fracture propagation mechanics analysis were applied to determine that the fracturing effect met the geological understanding and engineering expectations. Figure 5 The image shows the fracture characterization results of all fractured sections of a shale oil well in an embodiment of this application.

[0187] Example 2

[0188] like Figure 6 As shown, this embodiment provides a precise characterization system for shale oil fracturing fracture parameters based on microseismic monitoring data, including:

[0189] The parameter acquisition module 601 is used to acquire the basic parameters of the target well; the basic parameters include the completion data of the target well, the direction of ground stress, and the microseismic data of the target well.

[0190] The preprocessing module 602 is used to preprocess the microseismic event points of each fractured section of the target well based on the completion data of the target well, so as to obtain the preprocessed microseismic event points of each fractured section.

[0191] The noise reduction module 603 is used to perform noise reduction processing on the microseismic event points of each fracturing segment after preprocessing, so as to obtain the noise-reduced microseismic event points of each fracturing segment.

[0192] Clustering module 604 is used to cluster the noise-reduced microseismic event points for each fracturing segment according to the number of perforation clusters in the fracturing segment, and to determine the effective microseismic event points of each perforation cluster.

[0193] The fitting module 605 is used to obtain the characterization parameters of the fractures of each perforation cluster based on the effective microseismic event points of each perforation cluster through the fracture surface fitting method.

[0194] The characterization result output module 606 is used to obtain the fine characterization result of the fracturing fractures in the target well based on the characterization parameters of each perforation cluster fracture in the target well.

[0195] Compared with the prior art, the advantages of this application are:

[0196] (1) Traditional microseismic interpretation methods treat all microseismic event points in the entire fracturing segment as a whole for fitting, which is equivalent to forcibly "kneading" the fracturing fractures corresponding to different perforation clusters into a "clump". Therefore, the final interpretation can only be a general fracturing fracture, which cannot reflect the multiple clusters of fractures induced by different perforation clusters, resulting in a large difference between the fracture characterization results and the actual underground situation. The method provided by this patent can accurately allocate the microseismic event points of each fracturing segment to each perforation cluster according to the number of perforation clusters, and then calculate the accurate characterization parameters of the fractures corresponding to each perforation cluster.

[0197] (2) Existing methods for denoising microseismic event points are basically single-stage denoising. When screening effective microseismic event points, they often use simple threshold filtering (such as setting a fixed distance and discarding those exceeding the distance) or only retaining early data points based on time sequence, ignoring the correlation between time and space. This patent proposes a dual verification method based on the continuity of spatiotemporal neighborhood to judge the remaining microseismic event points to achieve secondary denoising. Then, considering the crack propagation law, effective event points are screened. First, the microseismic event points are sorted according to the measurement time sequence. The spatial location corresponding to each point is obtained through time sorting. Then, taking the perforation location as the starting point, the Euclidean distance from each point to the perforation location after time sorting is calculated. Starting from the third event point in the sequence, each event point is checked one by one. If the Euclidean distance between an event point and the perforation location is less than the Euclidean distance of the farthest event point from the perforation location among all the preceding event points, it is discarded. In this way, the effective microseismic event points of each perforation cluster are obtained, and the results are more accurate.

[0198] (3) Conventional methods, when fitting fracture surfaces using microseismic event points, directly use all data points for fitting and then directly obtain the interpretation results without using geostress information to verify the direction of the hydraulic fractures. The interpretation results may contradict the propagation law of hydraulic fractures. For example, hydraulic fractures generally propagate along the direction of the maximum principal stress, but the interpretation results of existing methods may differ significantly from the direction of the maximum principal stress. This patent, based on the propagation law of hydraulic fractures, uses the direction of the maximum principal stress as a constraint, and then uses a random simulation consensus algorithm to iteratively fit the effective event points of each perforation cluster. If multiple surfaces have the same number of interior points, the surface with the smallest root mean square error of the distance from all interior points to the fracture surface is selected, ensuring that the fracture propagation direction corresponding to each perforation cluster is near the direction of the maximum principal stress and that the fracture surface is the optimal surface, which is more in line with reality.

[0199] (4) In traditional methods for characterizing fracture parameters, fracture length is often roughly measured manually on a two-dimensional projection map, azimuth angle is determined by experience, and fracture height is simply taken as the maximum depth difference of data points. The interpretation process is highly subjective and has low accuracy. Furthermore, since traditional methods cannot distinguish between multiple fracture clusters, they can only obtain the average parameters of the entire fracturing section, thus failing to accurately characterize the fractures corresponding to each perforation cluster. This patent, based on the clustering processing of microseismic data points, projects the interior points of the fracture surface onto a two-dimensional plane, calculates the maximum Euclidean distance as the fracture length, determines the fracture azimuth angle by the angle between the maximum fracture length and the coordinate axis, and calculates the fracture height based on the vertical coordinate range of the interior points, thereby achieving parameterized characterization of fracturing fractures. Since the interior points are obtained at a specified distance on the fracture surface, the interpretation results are more accurate and can reflect the specific characteristics of each fracture cluster.

[0200] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] This document uses specific examples 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. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data, characterized in that, include: Obtain the basic parameters of the target well; The basic parameters include the completion data of the target well, the direction of ground stress, and the microseismic data of the target well; Based on the completion data of the target well, the microseismic event points of each fractured section of the target well are preprocessed to obtain the preprocessed microseismic event points of each fractured section. The microseismic event points of each fractured segment after preprocessing are denoised to obtain the denoised microseismic event points of each fractured segment. For each fracturing segment, the noise-reduced microseismic event points are clustered according to the number of perforation clusters in the fracturing segment to determine the effective microseismic event points of each perforation cluster. Based on the effective microseismic event points of each perforation cluster, the characterization parameters of the fractures in each perforation cluster are obtained by means of the fracture surface fitting method. Based on the characterization parameters of the fractures in each perforation cluster in the target well, the fine characterization results of the hydraulic fracturing fractures in the target well are obtained; Based on the number of perforation clusters in the fracturing section, the noise-reduced microseismic event points are clustered to determine the effective microseismic event points for each perforation cluster, specifically including: Based on the fundamental principle that pressure cracks extend along the direction of maximum principal stress, the two-dimensional plane orientation of crack propagation is obtained; Set the preset number of clusters K in the clustering algorithm to the number of perforation clusters; Based on the perforation location of the fracturing section and the two-dimensional plane orientation of the fracture propagation, each microseismic event point is assigned to the perforation cluster corresponding to the nearest perforation location; The perforation location of the fracturing section is set as the initial centroid; the centroid is the geometric center of each microseismic event point in the cluster set; the coordinates of the centroid are determined by the arithmetic mean of the coordinates of each microseismic event point in the cluster set. Calculate the sum of squared distances between each microseismic event point within each perforation cluster and its corresponding centroid, and update the centroid position; Based on the updated centroid position, the microseismic event points within each perforation cluster are redistributed. The sum of squared distances between each microseismic event point and its corresponding centroid within all perforation clusters of the fracturing section is calculated until the absolute value of the difference between the sums of squared distances of two consecutive times is less than a set threshold and the centroid coordinates no longer change. This yields the clustered microseismic event points. For each perforation cluster, the clustered microseismic event points are sorted by time. Starting from the perforation location, the Euclidean distance between each microseismic event point and the perforation location is calculated. Starting from the third microseismic event point in the sequence, each microseismic event point is checked one by one. If the Euclidean distance between any microseismic event point and the perforation location is less than the Euclidean distance of the farthest microseismic event point from the perforation location among all the preceding microseismic event points, then the microseismic event point is removed, and the effective microseismic event points of each perforation cluster are obtained.

2. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 1, characterized in that, The completion data of the target well includes geodetic coordinates, perforation time, number of perforation clusters, perforation location, wellbore trajectory data, well section length, and bridge plug location; the microseismic data of the target well includes the time and three-dimensional spatial coordinates of microseismic event points for each fracturing section.

3. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 2, characterized in that, Based on the completion data of the target well, the microseismic event points of each fractured section of the target well are preprocessed to obtain the preprocessed microseismic event points of each fractured section, specifically including: Based on the well trajectory data, well section length, and bridge plug location in the well completion data, determine the starting and ending points of each fracturing section on the well trajectory; The minimum curvature method is used to convert the starting and ending positions of each fracturing segment into three-dimensional coordinates to obtain the spatial range of each fracturing segment; Based on the spatial range of each fracturing segment, the microseismic event points located between the starting and ending points and perpendicular to the corresponding horizontal well segment are determined as the microseismic event points corresponding to the fracturing segment.

4. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 3, characterized in that, The microseismic event points of each fracturing segment after preprocessing are denoised to obtain the denoised microseismic event points of each fracturing segment, specifically including: For each fracturing segment, the microseismic event points of each pre-processed fracturing segment are sorted according to the order of measurement time to obtain the sorting results; Based on the sorting results, microseismic event points that have no other event points within the surrounding preset range are removed; A density clustering algorithm is used to cluster microseismic events based on a set neighborhood radius and core point density threshold, and event points identified as noise are removed. A dual verification method based on spatiotemporal neighborhood continuity is used to determine the remaining microseismic event points. The determination includes: defining a spatiotemporal neighborhood for each microseismic event point, consisting of a time window and a spatial radius; for each microseismic event point, checking if any other event points exist within the spatiotemporal neighborhood; if no other event points exist, the microseismic event point is directly determined as an isolated noise point; if other microseismic event points exist, calculating the Euclidean distance between the microseismic event point and all other microseismic event points within the spatiotemporal neighborhood; if all Euclidean distances are greater than the set spatial radius, the microseismic event point is determined as a noise point; and removing the noise points to obtain the remaining microseismic event points. The time window is a set duration covering the time before and after the occurrence of the microseismic event point; the spatial radius is the maximum allowable spatial span between two adjacent microseismic event points under the continuous crack propagation mode. Based on the anomaly coefficient, secondary anomaly detection is performed on each microseismic event point after noise points are removed, and microseismic event points with anomaly coefficients higher than 2 are removed to obtain the microseismic event points of each fracturing segment after noise reduction.

5. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 4, characterized in that, The formula for calculating the anomaly coefficient is: ； In the formula, p is the microseismic event point, and LWD(p) is the local weighted density of the microseismic event point p. ; For each k-neighborhood point p of a microseismic event, sum the weighted reach distances from all points o to p; wreach_dist k (p, o) is the weighted reachable distance from point p to point o. reach_dist k (p,o) is the reachable distance from point p to point o; k_dist(o) is the distance from point o to its k-th nearest neighbor, d(p,o) is the Euclidean distance between points p and o, and w(p,o) is the weight between points p and o. ε is a positive number; LP(p) is the neighborhood average density of point p. ; It sums the local weighted densities of all neighboring points of point p; N k (p) is the set of the k nearest points to the microseismic event point p; o is N k (p) represents any microseismic event point; LWD(o) is the local weighted density of point o.

6. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 1, characterized in that, Calculate the sum of squared distances between each microseismic event point within each perforation cluster and its corresponding centroid, specifically including: According to the formula Calculate the sum of squared distances between each microseismic event point and its corresponding centroid within each perforation cluster; Where K is the number of perforation clusters within the fracturing section, consistent with the number of cluster categories set by the clustering algorithm; C i Let x be the set of microseismic event points within the i-th perforation cluster; x is the set of events belonging to C. i Microseismic event points; Let be the centroid of the i-th perforation cluster; Let the microseismic event point x and the centroid be... The square of the Euclidean distance between them.

7. The method for fine characterization of hydraulic fracturing fractures based on microseismic monitoring data according to claim 6, characterized in that, Based on the effective microseismic event points of each perforation cluster, the characterization parameters of the fractures in each perforation cluster are obtained through fracture surface fitting, specifically including: Based on the effective event points of each perforation cluster, principal component analysis is used to obtain the fracture direction of the target well fracturing section; A random simulation consensus algorithm is used to iteratively fit the effective event points of each perforation cluster under a set threshold to generate a three-dimensional crack surface equation. When the angle deviation between the normal vector of the fracture surface equation of the fractured section and the normal vector of the fracture direction of the fractured section is within a preset range, the three-dimensional fracture surface equation of the fractured section of the target well is obtained. Based on the three-dimensional fracture surface equation of the target well fracturing section, the fracture length, height and azimuth of the corresponding perforation cluster are calculated to obtain the characterization parameters of the fractures in each perforation cluster.

8. A fine characterization system for hydraulic fracturing based on microseismic monitoring data, used to implement the fine characterization method for hydraulic fracturing based on microseismic monitoring data as described in any one of claims 1-7, characterized in that, include: The parameter acquisition module is used to acquire the basic parameters of the target well; The basic parameters include the completion data of the target well, the direction of ground stress, and the microseismic data of the target well; The preprocessing module is used to preprocess the microseismic event points of each fractured section of the target well based on the completion data of the target well, so as to obtain the preprocessed microseismic event points of each fractured section. The noise reduction module is used to reduce the noise of microseismic event points of each fracturing segment after preprocessing, so as to obtain the noise-reduced microseismic event points of each fracturing segment. The clustering module is used to cluster the noise-reduced microseismic event points for each fracturing section according to the number of perforation clusters in the fracturing section, and to determine the effective microseismic event points for each perforation cluster. The fitting module is used to obtain the characterization parameters of the fractures of each perforation cluster based on the effective microseismic event points of each perforation cluster through the fracture surface fitting method. The characterization result output module is used to obtain the fine characterization results of the fracturing fractures in the target well based on the characterization parameters of each perforation cluster fracture in the target well.

9. A fine characterization system for hydraulic fracturing based on microseismic monitoring data according to claim 8, characterized in that, The completion data of the target well includes geodetic coordinates, perforation time, number of perforation clusters, perforation location, wellbore trajectory data, well section length, and bridge plug location; the microseismic data of the target well includes the time and three-dimensional spatial coordinates of microseismic event points for each fracturing section.

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