A quantitative diagnostic method for crack complexity based on real-time microseismic monitoring data

Abstract

This invention discloses a quantitative diagnostic method for fracture complexity based on real-time microseismic monitoring data, comprising the following steps: S1: Collecting microseismic event data during fracturing operations and preprocessing the microseismic event data to obtain a set of effective event points; S2: Determining the anisotropy ratio of the modified volume, the density of fracture network branch points, and the number of simultaneously active event clusters based on the set of effective event points; S3: Establishing a comprehensive fracture complexity index calculation model based on the anisotropy ratio of the modified volume, the density of fracture network branch points, and the number of simultaneously active event clusters, and calculating the comprehensive fracture complexity index accordingly; S4: Determining the fracture complexity based on the comprehensive fracture complexity index; the larger the comprehensive fracture complexity index, the more complex the fracture. This invention enables dynamic diagnosis and quantitative evaluation of fracture network morphology and propagation behavior, providing a reliable real-time decision-making basis for fracturing operations.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a quantitative diagnostic method for fracture complexity based on real-time microseismic monitoring data. Background Technology

[0002] Hydraulic fracturing is a key production enhancement technology for the efficient development of unconventional oil and gas reservoirs. By injecting high-pressure fluids into the reservoir to induce rock fracture, an artificial fracture system can be formed in low-permeability tight reservoirs, significantly improving oil and gas permeability. However, during fracturing operations, the propagation morphology, geometry, and coupling characteristics of fractures with natural fractures are difficult to observe directly. The assessment of fracture complexity and conductivity still relies on indirect methods, leading to significant uncertainties in on-site diagnosis and construction optimization.

[0003] Currently, fracture complexity identification mainly relies on techniques such as pre-compression geological modeling, post-compression production data inversion, and microseismic monitoring. Among these, microseismic monitoring, as an important method for fracture geometry identification, can reflect the spatial distribution and activity characteristics of fractures. However, existing analyses are mostly limited to geometric indicators such as event distribution morphology, main fracture direction, and stimulus volume (SRV), failing to adequately reflect the branching, interconnection, and dynamic evolution characteristics of fracture networks. Traditional SRV or ellipsoid fitting methods cannot distinguish between structural differences such as "multi-branch complex" and "single-channel dominant" fractures; while statistical quantities such as event number and energy distribution are greatly affected by detection thresholds, noise levels, and location errors, lacking stability and physical constraints.

[0004] Therefore, there is an urgent need to propose a new method for diagnosing fracture complexity, which can comprehensively utilize the spatial, topological, and temporal characteristics of microseismic events to establish a multi-dimensional complexity index system without relying on complex geological assumptions, and achieve comprehensive quantitative identification of fracture geometry and propagation dynamics. Summary of the Invention

[0005] To address the aforementioned problems, this invention aims to provide a quantitative diagnostic method for crack complexity based on real-time microseismic monitoring data.

[0006] The technical solution of the present invention is as follows:

[0007] A quantitative diagnostic method for crack complexity based on real-time microseismic monitoring data includes the following steps:

[0008] S1: Collect microseismic event data during the hydraulic fracturing process, and preprocess the microseismic event data to obtain a set of valid event points; the microseismic event data includes the three-dimensional spatial coordinates and trigger time of the microseismic events;

[0009] S2: Based on the set of effective event points, determine the anisotropy ratio of the modified volume, the density of the stitching branch points, and the number of event clusters that are active simultaneously;

[0010] S3: Based on the anisotropy ratio of the modified volume, the density of the crack network branch points, and the number of simultaneously active event clusters, establish a comprehensive crack complexity index calculation model, and use this model to calculate the comprehensive crack complexity index.

[0011] S4: Determine the crack complexity based on the crack complexity comprehensive index. The larger the crack complexity comprehensive index, the more complex the crack.

[0012] Preferably, in step S2, the anisotropy ratio of the modified volume is calculated using the following formula:

[0013] (1)

[0014] (2)

[0015] In the formula: A r The anisotropy ratio of the modified volume is dimensionless; L1 and L2 are the lengths of the principal and secondary axes of the fracturing modified ellipsoid, respectively, in meters; λ1 and λ2 are the largest and second largest eigenvalues ​​of the event cloud covariance matrix of the effective event point set, respectively.

[0016] Preferably, the event cloud covariance matrix of the effective event point set is:

[0017] (3)

[0018] (4)

[0019] In the formula: C is the event cloud covariance matrix; N is the total number of valid event points, dimensionless; r i Let be the three-dimensional spatial coordinates of the microseismic event, in meters (m). The time-space mean is m; the symbol T represents the transpose; x i y i z i Let m be the three-dimensional spatial coordinates of the effective microseismic event point triggered by the i-th event.

[0020] Preferably, in step S2, the density of the sewing mesh branch points is calculated using the following formula:

[0021] (5)

[0022] In the formula: ρ b denoted as the density of branch points in the stitch mesh, dimensionless; n is the number of branch nodes, dimensionless; N is the total number of valid event points, dimensionless.

[0023] Preferably, the number of branch nodes is obtained through the following sub-steps: calculate the distance between any two microseismic event points in the effective event point set; when the distance is less than the connection threshold, connect the two event points; when an event point has more than 3 connections, mark the event point as a branch node, and count the number of branch nodes.

[0024] Preferably, the distance is a Euclidean distance.

[0025] Preferably, in step S2, the number of simultaneously active event clusters is obtained through the following sub-steps: using a clustering method to identify event clusters that are simultaneously active in the same time period, and statistically obtaining the number of simultaneously active event clusters.

[0026] Preferably, the clustering method employs density clustering or similarity clustering.

[0027] Preferably, in step S3, the comprehensive index calculation model for crack complexity is as follows:

[0028] (6)

[0029] In the formula: S is the comprehensive index of crack complexity, which is dimensionless; A r To modify the volume anisotropy ratio, dimensionless; ρ b is the density of branch points in the stitch mesh, dimensionless; m is the number of event clusters that are active simultaneously, dimensionless; w1, w2, and w3 are all empirical weights, their sum is 1, dimensionless; the symbol min represents taking the minimum value.

[0030] Preferably, in step S4, when determining the crack complexity based on the comprehensive crack complexity index...

[0031] If the comprehensive index of crack complexity is less than 0.3, then the crack is a single-wing crack or a restricted propagation crack.

[0032] If the comprehensive index of crack complexity is less than 0.6 and greater than or equal to 0.3, then the crack is a multi-branch complex crack.

[0033] If the comprehensive index of crack complexity is greater than or equal to 0.6, then the crack is a highly complex crack or a network crack.

[0034] The beneficial effects of this invention are:

[0035] This invention introduces three types of indicators: the anisotropy ratio of the modified volume, the density of branch points in the fracture network, and the number of simultaneously active event clusters. These indicators can comprehensively reflect the dynamic characteristics of fracture geometry, spatial branching, and expansion, and achieve a quantitative evaluation of fracture complexity. Compared with traditional single methods that rely on the number of events or SRV volume, this invention has higher reliability and physical interpretability. Attached Figure Description

[0036] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to the present invention. Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0039] like Figure 1 As shown, this invention provides a method for quantitative diagnosis of crack complexity based on real-time microseismic monitoring data, comprising the following steps:

[0040] S1: Collect microseismic event data during the hydraulic fracturing process, and preprocess the microseismic event data to obtain a set of valid event points; the microseismic event data includes the three-dimensional spatial coordinates and trigger time of the microseismic events.

[0041] In one specific embodiment, the preprocessing includes noise reduction and anomaly removal. Abnormal event points with positioning errors exceeding a threshold (e.g., more than 3 times the length of the perforation segment) are removed to obtain valid event points, and a set of valid event points is formed in chronological order.

[0042] S2: Based on the set of valid event points, determine the anisotropy ratio of the modified volume, the density of the stitching branch points, and the number of event clusters that are active simultaneously.

[0043] In one specific embodiment, the anisotropy ratio of the modified volume is calculated using the following formula:

[0044] (1)

[0045] (2)

[0046] In the formula: A rThe anisotropy ratio of the modified volume is dimensionless; L1 and L2 are the lengths of the principal and secondary axes of the fracturing modified ellipsoid, respectively, in meters; λ1 and λ2 are the largest and second largest eigenvalues ​​of the event cloud covariance matrix of the effective event point set, respectively.

[0047] In the above embodiments, the physical meaning of the modified volume anisotropy ratio is the degree of directionality of the event cloud deployment:

[0048] When A r When the value is ≥5, microseismic events exhibit a strong directional distribution and are characterized by single-channel or main crack-type structures.

[0049] When 5>A r When the value is ≥3, it is a multi-wing or locally branched crack;

[0050] When 3>A r At this time, the event distribution tends to be uniform, the cracks expand in multiple directions, and the complexity is high.

[0051] In a specific embodiment, the event cloud covariance matrix of the effective event point set is:

[0052] (3)

[0053] (4)

[0054] In the formula: C is the event cloud covariance matrix; N is the total number of valid event points, dimensionless; r i Let be the three-dimensional spatial coordinates of the microseismic event, in meters (m). The time-space mean is m; the symbol T represents the transpose; x i y i z i Let m be the three-dimensional spatial coordinates of the effective microseismic event point triggered by the i-th event.

[0055] In the above embodiment, the event cloud covariance matrix is ​​obtained by performing principal component analysis on the effective event point set. Then, the three eigenvalues ​​λ1≥λ2≥λ3 of the event cloud covariance matrix are calculated, which correspond to the variances of the principal axis, secondary axis and minor axis of the fracturing ellipsoid, respectively. The lengths of the principal axis and secondary axis of the fracturing ellipsoid can be calculated by taking the first two eigenvalues.

[0056] In one specific embodiment, the density of the stitch mesh branch points is calculated using the following formula:

[0057] (5)

[0058] In the formula: ρ b denoted as the density of branch points in the stitch mesh, dimensionless; n is the number of branch nodes, dimensionless; N is the total number of valid event points, dimensionless.

[0059] In the above embodiments, the density of the sewing mesh branch points can reflect the degree of sewing mesh branching and interconnection.

[0060] In a specific embodiment, the number of branch nodes is obtained through the following sub-steps: calculating the distance between any two microseismic event points in the effective event point set; when the distance is less than the connection threshold, connecting the two event points; when an event point has more than 3 connections, marking the event point as a branch node, and counting the number of branch nodes.

[0061] Optionally, the distance is calculated using Euclidean distance. It should be noted that the Euclidean distance in this embodiment is only one preferred distance calculation method of the present invention. Besides the Euclidean distance, other distance calculation methods in the prior art, such as Manhattan distance and Chebyshev distance, can also be applied to the present invention.

[0062] In one specific embodiment, the number of simultaneously active event clusters is obtained through the following sub-steps: using a clustering method to identify event clusters that are simultaneously active within the same time period, and statistically obtaining the number of simultaneously active event clusters.

[0063] Optionally, the clustering method employs density clustering or similarity clustering. It should be noted that the clustering method in this embodiment is merely a preferred method of the present invention; other existing clustering methods, such as K-means, can also be applied to the present invention.

[0064] S3: Based on the anisotropy ratio of the modified volume, the density of the crack network branch points, and the number of simultaneously active event clusters, establish a comprehensive crack complexity index calculation model, and use this model to calculate the comprehensive crack complexity index.

[0065] In one specific embodiment, the comprehensive index calculation model for crack complexity is as follows:

[0066] (6)

[0067] In the formula: S is the comprehensive index of crack complexity, which is dimensionless; A r To modify the volume anisotropy ratio, dimensionless; ρ b is the density of branch points in the stitch mesh, dimensionless; m is the number of event clusters that are active simultaneously, dimensionless; w1, w2, and w3 are all empirical weights, their sum is 1, dimensionless; the symbol min represents taking the minimum value.

[0068] It should be noted that the empirical weights can be set manually, and can be corrected using typical wells after being set.

[0069] In the above embodiments, the crack complexity comprehensive index calculation model of the present invention can comprehensively reflect the dynamic characteristics of crack geometric distribution, spatial branching and expansion by simultaneously introducing three types of indicators: the anisotropy ratio of the modification volume, the density of crack network branch points, and the number of simultaneously active event clusters, thereby realizing a quantitative evaluation of crack complexity.

[0070] S4: Determine the crack complexity based on the crack complexity comprehensive index. The larger the crack complexity comprehensive index, the more complex the crack.

[0071] In one specific embodiment, when determining the crack complexity based on the crack complexity comprehensive index...

[0072] If the comprehensive index of crack complexity is less than 0.3, then the crack is a single-wing crack or a restricted propagation crack.

[0073] If the comprehensive index of crack complexity is less than 0.6 and greater than or equal to 0.3, then the crack is a multi-branch complex crack.

[0074] If the comprehensive index of crack complexity is greater than or equal to 0.6, then the crack is a highly complex crack or a network crack.

[0075] In a specific embodiment, taking shale gas well Y in southern Sichuan as an example, the fracture complexity is determined by the fracture complexity quantitative diagnosis method based on real-time microseismic monitoring data described in this invention.

[0076] In this embodiment, the volumetric anisotropy ratio of the shale gas well Y is A. r 3.2, density of branch points ρ of the seam mesh b With a value of 0.71 and the number of active clusters m being 2, and with the three weights set to 0.4, 0.3, and 0.3, the comprehensive fracture complexity index S of this well is calculated to be 0.538, indicating a multi-branch complex fracture.

[0077] In summary, this invention enables a quantitative evaluation of crack complexity. Compared with existing technologies, this invention represents a significant advancement.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A quantitative diagnostic method for crack complexity based on real-time microseismic monitoring data, characterized in that, Includes the following steps: S1: Collect microseismic event data during the hydraulic fracturing process, and preprocess the microseismic event data to obtain a set of valid event points; the microseismic event data includes the three-dimensional spatial coordinates and trigger time of the microseismic events; S2: Based on the set of effective event points, determine the anisotropy ratio of the modified volume, the density of the stitching branch points, and the number of event clusters that are active simultaneously; The anisotropy ratio of the modified volume is calculated using the following formula: （1） （2） In the formula, A r is the reconstruction volume anisotropy ratio, dimensionless; L1 and L2 are the lengths of the major axis and the minor axis of the fracturing reconstruction ellipsoid, respectively, m; λ1 and λ2 are the maximum eigenvalue and the second largest eigenvalue of the event cloud covariance matrix of the effective event point set, respectively; The density of the stitching mesh branch points is calculated using the following formula: （5） where: p b is the density of the branch points of the net, dimensionless; n is the number of branch nodes, dimensionless; N is the total number of effective event points, dimensionless; The number of branch nodes is obtained through the following sub-steps: calculate the distance between any two microseismic event points in the effective event point set; when the distance is less than the connection threshold, connect the two event points; when an event point has more than 3 connections, mark the event point as a branch node, and count the number of branch nodes. S3: Based on the anisotropy ratio of the modified volume, the density of the crack network branch points, and the number of simultaneously active event clusters, establish a comprehensive crack complexity index calculation model, and use this model to calculate the comprehensive crack complexity index. S4: Determine the crack complexity based on the crack complexity comprehensive index. The larger the crack complexity comprehensive index, the more complex the crack.

2. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to claim 1, characterized in that, The event cloud covariance matrix of the effective event point set is: （3） （4） In the formula: C is the event cloud covariance matrix; N is the total number of valid event points, dimensionless; r i Let be the three-dimensional spatial coordinates of the microseismic event, in meters (m). The time-space mean is m; the symbol T represents the transpose; x i y i z i Let m be the three-dimensional spatial coordinates of the effective microseismic event point triggered by the i-th event.

3. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to claim 1, characterized in that, The distance is measured in Euclidean form.

4. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to claim 1, characterized in that, In step S2, the number of simultaneously active event clusters is obtained through the following sub-steps: using clustering methods to identify event clusters that are simultaneously active in the same time period, and statistically obtaining the number of simultaneously active event clusters.

5. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to claim 4, characterized in that, The clustering method used is density clustering or similarity clustering.

6. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to any one of claims 1-5, characterized in that, In step S3, the comprehensive index calculation model for crack complexity is as follows: （6） In the formula, S is a fracture complexity comprehensive index, dimensionless; A r is a modified volume anisotropy ratio, dimensionless; ρ b is the density of branch points in the stitch mesh, dimensionless; m is the number of event clusters that are active simultaneously, dimensionless; w1, w2, and w3 are all empirical weights, their sum is 1, dimensionless; the symbol min represents taking the minimum value.

7. The quantitative diagnosis method for crack complexity based on real-time microseismic monitoring data according to any one of claims 1-5, characterized in that, In step S4, when determining the crack complexity based on the comprehensive crack complexity index, If the comprehensive index of crack complexity is less than 0.3, then the crack is a single-wing crack or a restricted propagation crack. If the comprehensive index of crack complexity is less than 0.6 and greater than or equal to 0.3, then the crack is a multi-branch complex crack. If the comprehensive index of crack complexity is greater than or equal to 0.6, then the crack is a highly complex crack or a network crack.

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