Method and apparatus for estimating effective reservoir stimulated volume of a fracture based on microseismic

Abstract

The present disclosure relates to a method and device for estimating a microseismic-based effective reservoir stimulation volume in hydraulic fracturing, the method comprising: obtaining point cloud data corresponding to microseismic events generated during hydraulic fracturing; identifying and removing noise from the point cloud data according to the density of the point cloud data, to obtain effective microseismic event point cloud data; performing slicing processing on the effective microseismic event point cloud data to obtain a plurality of point cloud slice data; fitting the periphery contour for each point cloud slice data, and calculating the area of the fitted point cloud slice data to obtain the area corresponding to the plurality of point cloud slices; calculating the area corresponding to the plurality of point cloud slices to obtain an effective microseismic event point cloud volume; and taking the effective microseismic event point cloud volume as the effective reservoir stimulation volume in hydraulic fracturing. The method can improve the accuracy of the estimated effective reservoir stimulation volume in hydraulic fracturing, and is helpful for guiding and optimizing subsequent oil and gas development processes.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas exploration technology, and in particular to a method and apparatus for estimating the effective reservoir stimulation volume based on microseismic fracturing. Background Technology

[0002] The combination of horizontal drilling and hydraulic fracturing technologies has facilitated the development of shale oil and gas reservoirs globally. Hydraulic fracturing provides interconnected pathways between pores and allows for unrestricted gas and reservoir fluid flow. Furthermore, shale is known to possess pre-existing natural fractures that interact with induced hydraulic fractures, creating a complex fracture network called the reservoir stimulation volume (SRV), which characterizes the reservoir volume generated after hydraulic fracturing. SRV estimation can be accomplished through microseismic interpretation, induced fracture monitoring, and modeling. Since reservoir portions exhibiting microseismic events may not increase production, and natural fracture systems may increase production without showing any seismic activity, various methods used to estimate SRV do not directly provide the actual productive reservoir volume. Therefore, the concept of effective reservoir stimulation volume (ESRV) has been proposed, referring to the reservoir volume that contributes to production. However, how to accurately assess the effective reservoir stimulation volume remains a pressing technical problem. Summary of the Invention

[0003] To address, or at least partially address, the aforementioned technical problems, embodiments of this disclosure provide a method and apparatus for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0004] In a first aspect, embodiments of this disclosure provide a method for estimating the effective reservoir stimulation volume based on microseismic events during hydraulic fracturing. The method includes: acquiring point cloud data corresponding to microseismic events generated during hydraulic fracturing; identifying and removing noise from the point cloud data based on its density to obtain effective microseismic event point cloud data; slicing the effective microseismic event point cloud data to obtain multiple point cloud slices; fitting the outer contour of each point cloud slice and calculating the area of ​​the fitted point cloud slices to obtain the area corresponding to the multiple point cloud slices; calculating the effective microseismic event point cloud volume based on the area corresponding to the multiple point cloud slices; and using the effective microseismic event point cloud volume as the effective reservoir stimulation volume during hydraulic fracturing.

[0005] In some embodiments, noise identification and noise removal are performed on the point cloud data according to the density of the point cloud data to obtain effective microseismic event point cloud data, including: classifying point cloud data with a density less than a set threshold into noise categories and removing them based on the DBSCAN clustering algorithm to obtain effective microseismic event point cloud data; wherein the set threshold is limited by a preset neighborhood radius and a preset minimum number of neighborhood points corresponding to the core point.

[0006] In some embodiments, based on the DBSCAN clustering algorithm, point cloud data with a density less than a set threshold are classified into noise categories and removed to obtain effective microseismic event point cloud data. This includes: for the point cloud data corresponding to microseismic events, searching for unlabeled microseismic event points as the current points in each round for category labeling operations until all microseismic event points have been labeled; removing microseismic event points labeled as noise categories to obtain effective microseismic event point cloud data. Specifically, for each round of searching, the following category labeling operation is performed on the current point: based on the preset neighborhood radius, neighboring nodes within the neighborhood of the current point in the point cloud data are found and constitute the neighborhood point set of the current point; it is determined whether the number of neighboring nodes contained in the neighborhood point set of the current point is less than the preset minimum number of neighboring points; if the number of neighboring nodes is less than the preset minimum number of neighboring points, the current point is labeled as noise; and the search continues for the next unlabeled microseismic event point as the current point of the next round; if the number of neighboring nodes exceeds the preset minimum number of neighboring points, the current point is labeled as core point; and the search continues for neighboring nodes within the neighborhood point set as the current point of the next round.

[0007] In some embodiments, the preset neighborhood radius and the preset minimum number of neighborhood points are obtained by: using multiple data groups with different neighborhood radii and different minimum neighborhood points as different test parameters; performing classification tests on the point cloud data based on different test parameters and the DBSCAN clustering algorithm to obtain the total classification; and determining the data group corresponding to the test parameters that classify the total classification into two categories as the preset neighborhood radius and the preset minimum number of neighborhood points, according to the correspondence between the total classification and the test parameters.

[0008] In some embodiments, an outer contour is fitted for each point cloud slice data, and the area of ​​the fitted point cloud slice data is calculated to obtain the area corresponding to multiple point cloud slices. This includes: for each point cloud slice data, an outer contour is fitted based on the Alpha Shape algorithm to obtain the outer contour corresponding to the irregular point cloud slice; based on the Monte Carlo random sampling method, the area of ​​the point cloud slice data corresponding to the irregular point cloud slice with fitted outer contour is calculated to obtain the area corresponding to multiple point cloud slices.

[0009] In some embodiments, based on the Monte Carlo random sampling method, the area of ​​the point cloud slice data corresponding to the irregular point cloud slice with fitted outer contour is calculated to obtain the area corresponding to multiple point cloud slices. This includes: for each point cloud slice data, taking the maximum distance of the corresponding outer contour on the first coordinate axis and the maximum distance on the second coordinate axis as the length and width, respectively, to establish a rectangular region; uniformly and randomly scattering points within the rectangular region, and statistically calculating the probability value of the scattered points hitting the region where the irregular point cloud slice is located; multiplying the probability value with the area of ​​the rectangular region to obtain the area corresponding to the current point cloud slice data.

[0010] In some embodiments, for each point cloud slice data, the outer contour is fitted based on the Alpha Shape algorithm to obtain the outer contour corresponding to the irregular point cloud slice, including: for each point cloud slice data, searching for a point as an initial point, performing a contour point determination operation, until all points have been searched and determined. For each initial point, perform the following contour point determination operation: Construct a circular search range with the initial point as the center and a threshold 2α as the radius; search for points within the circular search range to form a search point set; α represents a preset parameter; arbitrarily select a point within the search point set to form two points to be determined with the initial point; draw two circles of different sizes whose boundaries contain the two points to be determined, with the centers of the two circles being the first center position and the second center position, respectively; calculate the first distance from all points in the search point set to the first center position and the second distance from all points in the search point set to the second center position; determine whether the first distance or the second distance obtained for all points in the search point set satisfies the condition of being greater than α; if the first distance or the second distance obtained for all points satisfies the condition of being greater than α, then determine the two points to be determined as contour points; and continue searching for the next undetermined point as the initial point; if neither the first distance nor the second distance obtained for all points satisfies the condition of being greater than α, then determine the two points to be determined as non-contour points; and continue searching for the next undetermined point in the search point set as the initial point.

[0011] Secondly, embodiments of this disclosure provide an estimation device for the effective reservoir stimulation volume based on microseismic fracturing. The estimation device includes: a data acquisition module, a noise processing module, a slice processing module, a contour fitting module, an area calculation module, and a volume calculation module. The data acquisition module acquires point cloud data corresponding to microseismic events generated during hydraulic fracturing. The noise processing module identifies and removes noise from the point cloud data based on its density to obtain effective microseismic event point cloud data. The slice processing module slices the effective microseismic event point cloud data to obtain multiple point cloud slices. The contour fitting module fits the outer contour of each point cloud slice. The area calculation module calculates the area of ​​the fitted point cloud slices to obtain the area corresponding to the multiple point cloud slices. The volume calculation module calculates the effective microseismic event point cloud volume based on the area corresponding to the multiple point cloud slices; this effective microseismic event point cloud volume serves as the effective reservoir stimulation volume.

[0012] Thirdly, embodiments of this disclosure provide an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus; the memory stores computer programs; and the processor, when executing the program stored in the memory, implements the method described above for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0013] Fourthly, embodiments of this disclosure provide a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described above for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0014] The technical solutions provided in the embodiments of this disclosure have at least some or all of the following advantages:

[0015] By acquiring point cloud data corresponding to microseismic events generated during hydraulic fracturing; considering that some microseismic events during hydraulic fracturing are only stress-related and the actual fracturing fluid has not reached the corresponding area, the point cloud data corresponding to the microseismic event points are relatively scattered; the point cloud data corresponding to microseismic event points in the area matched with the fracturing fluid is more concentrated, which is effective for reservoir stimulation; therefore, based on the density of the above point cloud data, noise identification and noise removal are performed on the above point cloud data to obtain effective microseismic event point cloud data; the above effective microseismic event point cloud data is sliced ​​to obtain multiple point cloud slice data; the outer contour of each point cloud slice data is fitted, and the area of ​​the fitted point cloud slice data is calculated to obtain the area corresponding to multiple point cloud slices; the effective microseismic event point cloud volume is calculated based on the area corresponding to the above multiple point cloud slices; the above effective microseismic event point cloud volume is used as the effective reservoir stimulation volume (ESRV) of fracturing; this can improve the accuracy of the estimated effective reservoir stimulation volume of fracturing, which helps to guide and optimize the subsequent oil and gas development process. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for estimating the effective reservoir stimulation volume based on microseismic fracturing according to an embodiment of the present disclosure is shown schematically.

[0019] Figure 2 A schematic diagram of a point cloud dataset corresponding to microseismic events generated during hydraulic fracturing according to an embodiment of the present disclosure is shown.

[0020] Figure 3 A schematic diagram of clustering obtained by noise identification based on density according to an embodiment of the present disclosure is shown.

[0021] Figure 4 The diagram schematically illustrates the spatial coordinates of effective microseismic event points after denoising according to an embodiment of the present disclosure.

[0022] Figure 5A schematic diagram of effective microseismic event point cloud data slices according to an embodiment of the present disclosure is shown; wherein (a) to (j) respectively represent multiple point cloud slice data corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h;

[0023] Figure 6 The diagram schematically illustrates the outer contour fitting map of an effective microseismic event point cloud data slice according to an embodiment of the present disclosure; wherein (a) to (j) respectively represent the outer contour fitting maps of multiple point cloud slice data corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h.

[0024] Figure 7 The illustration shows (a) a schematic diagram of the outer contour obtained after fitting the outer contour of irregular point cloud slice data according to an embodiment of the present disclosure; and (b) a schematic diagram of the result of calculating the area of ​​the fitted point cloud slice data to obtain the area corresponding to the point cloud slice.

[0025] Figure 8 The diagram illustrates the result of obtaining the effective reservoir stimulation volume by summing the areas corresponding to multiple point cloud slices according to an embodiment of the present disclosure.

[0026] Figure 9 A schematic block diagram of a device for estimating the effective reservoir stimulation volume based on microseismic fracturing according to an embodiment of the present disclosure is shown.

[0027] Figure 10 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0029] The first exemplary embodiment of this disclosure provides a method for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0030] Figure 1 A flowchart illustrating a method for estimating the effective reservoir stimulation volume based on microseismic fracturing according to an embodiment of the present disclosure is shown.

[0031] Reference Figure 1As shown, the method for estimating the effective reservoir stimulation volume based on microseismic fracturing provided in the embodiments of this disclosure includes the following steps: S110, S120, S130, S140 and S150.

[0032] In step S110, point cloud data corresponding to microseismic events generated during hydraulic fracturing are acquired.

[0033] The point cloud dataset X corresponding to the microseismic events generated during hydraulic fracturing is represented as:

[0034] X = {m1, m2, m3, ..., m} N}, m o =(x o ,y o ,z i (1)

[0035] Where, m i Indicates the location of the i-th microseismic event point; (x i ,y i ,z i Let represent the three-dimensional coordinates corresponding to the spatial location of the i-th microseismic event point, where i ∈ [1, N], i represents the sequence number of the microseismic event point, and N represents the total number of microseismic event points.

[0036] Figure 2 A schematic diagram of a point cloud dataset corresponding to microseismic events generated during hydraulic fracturing according to an embodiment of the present disclosure is shown.

[0037] Reference Figure 2 As shown, the spatial coordinate map of microseismic event point cloud data is illustrated; the horizontal axis is the east-west X coordinate (e.g., unit: meters (m)), the vertical axis is the north-south Y coordinate (e.g., unit: m), and the vertical axis is the depth Z coordinate (e.g., unit: m). Multiple microseismic event points are distributed in various locations, and the distribution of microseismic event points is sparse in some areas and dense in others.

[0038] In step S120, noise identification and noise removal are performed on the point cloud data according to the density of the point cloud data to obtain effective microseismic event point cloud data.

[0039] Microseismic events are caused by human activities in oil and gas exploration (such as mining and seismic exploration) that alter the stress distribution or volume of a rock mass. When solid Earth particles attempt to redistribute stress within a rock mass, they can suddenly slide or shear along pre-existing weak points (such as along fault or fracture networks). The result of this movement is the release of energy in the form of seismic waves, known as microseismic events.

[0040] Considering that some microseismic events during hydraulic fracturing only correspond to stress transmission, and the actual fracturing fluid does not reach the corresponding area, the point cloud data corresponding to these microseismic event points is relatively scattered. Conversely, the point cloud data corresponding to microseismic event points within the fracturing fluid-matched arrival area is more concentrated, and this portion is effective for reservoir stimulation. Therefore, based on the density of the aforementioned point cloud data, noise identification and removal are performed to obtain effective microseismic event point cloud data. This helps to distinguish the truly effective microseismic event point cloud data for reservoir stimulation, improving the accuracy of the assessment of the effective reservoir stimulation volume (ESRV).

[0041] In some embodiments, step S120 above involves identifying and removing noise from the point cloud data based on its density to obtain effective microseismic event point cloud data. This includes: using the DBSCAN clustering algorithm, classifying point cloud data with a density less than a set threshold into noise categories and removing them to obtain effective microseismic event point cloud data; wherein the set threshold is limited by a preset neighborhood radius Epsilon and a preset minimum number of neighborhood points MinPts corresponding to the core point.

[0042] The DBSCAN clustering algorithm relies on density rather than distance, thus overcoming the limitation of distance-based algorithms that can only detect spherical clusters. The core idea of ​​the DBSCAN algorithm is to start from a core point and continuously expand towards a density-accessible region, thereby obtaining a maximized region containing both the core point and boundary points, where any two points are density-connected. In the embodiments of this disclosure, the DBSCAN clustering algorithm is applied to point cloud data for noise identification and removal, thereby obtaining effective microseismic event point cloud data. This process distinguishes between noisy and effective microseismic event point cloud data based on the density of microseismic event points. The point cloud data of stress-transmission-related microseismic events is relatively dispersed; therefore, point cloud data with a density below a set threshold is classified as noise and removed. This helps to distinguish truly effective microseismic event point cloud data for reservoir stimulation, improving the accuracy of the assessment of the effective reservoir stimulation volume (ESRV).

[0043] In some embodiments, the preset neighborhood radius and the preset minimum number of neighborhood points are obtained by: using multiple data groups with different neighborhood radii and different minimum neighborhood points as different test parameters; performing classification tests on the point cloud data based on different test parameters and the DBSCAN clustering algorithm to obtain the total classification; and determining the data group corresponding to the test parameters that classify the total classification into two categories as the preset neighborhood radius and the preset minimum number of neighborhood points, according to the correspondence between the total classification and the test parameters.

[0044] By pre-classifying point cloud data using different test parameters (mainly composed of multiple data groups with different neighborhood radii and different minimum neighborhood points), appropriate test parameters can be obtained based on the specific classification situation. Based on these test parameters, the point cloud data of noise and effective microseismic events can be divided. This method is adaptable to various types of real point cloud data and achieves noise identification and removal through binary classification.

[0045] Figure 3 A schematic diagram of clustering obtained by noise identification based on density according to an embodiment of the present disclosure is shown. Figure 4 The diagram schematically illustrates the spatial coordinates of effective microseismic event points after denoising according to an embodiment of the present disclosure. Figure 3 and Figure 4 In the diagram, the horizontal axis is the east-west X coordinate (unit: m), the vertical axis is the north-south Y coordinate (unit: m), and the vertical axis is the depth Z coordinate (unit: m). Figure 3 In the diagram, hollow circles represent noise points with a cluster label of -1; solid circles represent valid microseismic event point cloud data with a cluster label of 1.

[0046] In some embodiments, based on the DBSCAN clustering algorithm, point cloud data with a density less than a set threshold are classified into noise categories and removed to obtain effective microseismic event point cloud data, including:

[0047] For the point cloud data corresponding to microseismic events, in each round, unlabeled microseismic event points are searched as the current points for category labeling, until all microseismic event points have been labeled.

[0048] Microseismic event points marked as noise are removed to obtain valid microseismic event point cloud data.

[0049] Specifically, for each point found in each round of searching, the following category labeling operation is performed:

[0050] Based on the aforementioned preset neighborhood radius, find the neighboring nodes in the point cloud data that are located within the neighborhood of the current point and form the neighborhood point set of the current point;

[0051] Determine whether the number of neighboring nodes contained in the neighborhood set of the current point is less than the preset minimum number of neighboring nodes.

[0052] If the number of neighboring nodes is less than the preset minimum number of neighboring points, the current point is marked as a noise category; and the search continues for the next unmarked microseismic event point as the current point for the next round.

[0053] If the number of neighboring nodes exceeds the preset minimum number of neighboring points, the current point is marked as a core point; and the search continues to find neighboring nodes in the aforementioned set of neighboring points as the current point for the next round.

[0054] The following example illustrates the specific execution process of noise identification and noise removal based on the DBSCAN clustering algorithm.

[0055] Step (1): Select the first unlabeled observation x1 from the point cloud dataset X corresponding to the microseismic event as the current point, and initialize the first cluster label C to 1.

[0056] Step (2): Based on the preset neighborhood radius Epsilon and the minimum number of neighborhood points MinPts required for the core point, find the set of points within the neighborhood of the current point x1, and describe it as the neighborhood point set. If the number of neighboring nodes contained in the neighborhood point set is less than MinPts, mark the current point as a noise point and place it in cluster D with a clustering label of -1, and proceed to step (4). Otherwise, mark the current point as the core point belonging to cluster C.

[0057] Step (3): Traverse each neighbor node (as the new current point) and repeat step (2) for each current node until no new neighbor node that can be marked as belonging to the current cluster C is found.

[0058] Step (4): Select the next unlabeled point in X as the current point and increment the cluster count by 1.

[0059] Step (5): Repeat steps (2) to (4) until all points in X are marked; the result after noise identification is as follows Figure 3 As shown, each microseismic event point is labeled with a corresponding cluster label. The microseismic event point corresponding to cluster label 1 belongs to the valid microseismic event point cloud data; the microseismic event point corresponding to cluster label -1 belongs to the noise.

[0060] Step (6): Delete noisy points with a cluster label of -1 from the point cloud dataset X corresponding to the microseismic events, obtaining the denoised effective microseismic event point cloud dataset Y, and compare it with... Figure 3 and Figure 4 As shown, the remaining valid microseismic event point cloud data after noise removal is valid.

[0061] The effective microseismic event point cloud dataset Y is expressed as follows:

[0062] Y = {m1,m2,m3,…,m} M}, m j =(x j ,y j ,z j (2)

[0063] Among them, (x j ,y j ,z j ) represents the spatial coordinates of the effective microseismic event points after denoising, j∈[1,M], where j represents the sequence number of the effective microseismic event points after denoising; M is the total number of effective microseismic event points after denoising.

[0064] In step S130, the above-mentioned effective microseismic event point cloud data is sliced ​​to obtain multiple point cloud slice data.

[0065] Figure 5 A schematic diagram of effective microseismic event point cloud data slices according to an embodiment of the present disclosure is shown; wherein (a) to (j) respectively represent multiple point cloud slice data corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h.

[0066] Between the minimum and maximum vertical height H of the effective microseismic event point cloud data after denoising, a set of horizontal planes with equal intervals of h are used to sequentially cut the point cloud from top to bottom, resulting in n sets of horizontal point cloud slice data S. k , refer to Figure 5 As shown in (a) to (j) (where j is the figure number, which has a different meaning from the number in the formula), each group of point cloud slice data corresponds to the data in the xy plane, and the k-th layer point cloud slice data S k It is expressed as follows:

[0067]

[0068] Where Y(x) j ,y j ,z j The 'z' represents the horizontal point cloud slice data corresponding to the ordinate z (i.e., the slice of the kth layer); 'n' represents the total number of slices, and 'n' = H / h. The 0th layer represents the slice data corresponding to the initial slice layer.

[0069] Reference Figure 5 As shown in (a) to (j), a total of multiple point cloud slice data corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h are obtained.

[0070] In step S140, the outer contour is fitted for each point cloud slice data, and the area of ​​the fitted point cloud slice data is calculated to obtain the area corresponding to multiple point cloud slices.

[0071] Figure 6The diagram schematically illustrates the outer contour fitting map of an effective microseismic event point cloud data slice according to an embodiment of the present disclosure; wherein (a) to (j) respectively represent the outer contour fitting maps of multiple point cloud slice data corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h.

[0072] In some embodiments, an outer contour is fitted for each point cloud slice data, and the area of ​​the fitted point cloud slice data is calculated to obtain the area corresponding to multiple point cloud slices. This includes: for each point cloud slice data, an outer contour is fitted based on the Alpha Shape algorithm (sometimes called the rolling ball algorithm) to obtain the outer contour corresponding to the irregular point cloud slice; based on the Monte Carlo random sampling method, the area of ​​the point cloud slice data corresponding to the irregular point cloud slice with fitted outer contour is calculated to obtain the area corresponding to multiple point cloud slices.

[0073] In some embodiments, for each point cloud slice data, the outer contour is fitted based on the Alpha Shape algorithm to obtain the outer contour corresponding to the irregular point cloud slice, including: for each point cloud slice data, searching for a point as an initial point, performing a contour point determination operation, until all points have been searched and determined.

[0074] By fitting the outer contour based on the Alpha Shape algorithm, boundary features can be extracted from the point cloud. By utilizing the neighborhood information of each point in the point cloud, the Alpha shape under different parameters can be calculated to find the boundary of the point cloud.

[0075] For each initial point, perform the following contour point determination operation:

[0076] A circular search range is constructed with the initial point as the center and the threshold 2α as the radius. Points within the circular search range are searched and constitute a search point set; α represents a preset parameter.

[0077] Take any point from the above search point set, and together with the above initial point, form two points to be determined;

[0078] Draw two circles of different sizes whose boundaries contain the two points to be determined mentioned above. The centers of these two circles are the first center and the second center, respectively.

[0079] Calculate the first distance from all points in the search point set to the first center position and the second distance from all points in the search point set to the second center position;

[0080] Determine whether the first or second distance obtained for all points in the search point set satisfies the condition that it is greater than α;

[0081] If the first or second distance obtained for all points satisfies the condition of being greater than α, then the two points to be determined are identified as contour points; and the search continues for the next undetermined point as the initial point.

[0082] If the first and second distances obtained for all points do not satisfy the condition of being greater than α, then the two points to be determined are identified as non-contour points; and the search continues to find the next undetermined point in the search point set as the initial point.

[0083] Reference Figure 6 As shown in (a) to (j), the boundaries corresponding to each contour point are obtained by fitting the contour data of multiple point cloud slices corresponding to heights z = 9h, 8h, 7h, 6h, 5h, 4h, 3h, 2h, 1h, and 0h.

[0084] The following describes the specific execution process of contour fitting using the Alpha Shape algorithm.

[0085] Step (1): Slice S from the k-th layer point cloud k Find any point P0 as the initial point, draw a circle with P0 as the center and a radius of threshold 2α, search all the points inside the circle and form a point set, called the search point set R1.

[0086] Step (2): Select a new point P1 from the search point set R1. The new point P1 and the initial point P0 constitute two points to be determined. Draw two circles whose boundaries include the two points P0 and P1, and denote the centers of the circles as C0 and C1 respectively.

[0087] Step (3): Calculate the distances from all points in the search point set R1 to C0 and C1, and store them in two arrays d0 and d1.

[0088] Step (4): Determine the contour points: If all points in d0 or d1 are greater than α, then define P0 and P1 as contour points and proceed to step (6) for execution.

[0089] Step (5): If none of the points in d0 and d1 satisfy the condition greater than α, that is, none of the points in d0 satisfy the condition greater than α, and none of the points in d1 satisfy the condition greater than α, then repeat steps (2) to (4) for the next point in R1 until all points are determined.

[0090] Step (6): Slice the point cloud S k Repeat steps (2) to (4) above for all undetermined points until S. k All points have been determined to be completed;

[0091] Step (7): Repeat steps (1) to (6) until the contours of all n-layer horizontal point cloud slices are determined.

[0092] Figure 7 The illustration schematically shows (a) a schematic diagram of the outer contour obtained after fitting the outer contour of irregular point cloud slice data according to an embodiment of the present disclosure; and (b) a schematic diagram of the result of calculating the area of ​​the fitted point cloud slice data to obtain the area corresponding to the point cloud slice.

[0093] In some embodiments, based on Monte Carlo random sampling, the area of ​​point cloud slice data corresponding to irregular point cloud slices fitted with outer contours is calculated to obtain the areas corresponding to multiple point cloud slices, including:

[0094] For each point cloud slice, a rectangular region is created by using the maximum distance of the corresponding outer contour along the first coordinate axis and the maximum distance along the second coordinate axis as its length and width, respectively; for example, referring to... Figure 7 As shown in (a), the horizontal axis is the east-west X coordinate (unit: m), and the vertical axis is the north-south Y coordinate (unit: m). The maximum distance a in the east-west direction (X-axis) and the maximum distance b in the north-south direction (Y-axis) of the outer contour of the point cloud slice data are used as the length and width to establish a rectangular area.

[0095] Within the aforementioned rectangular area, points are randomly and evenly scattered. The probability that the scattered points hit the region of the irregular point cloud slice is calculated; the probability value is the number N of points that hit the slice. hits Total number of points N total The ratio between them;

[0096] The above probability value N hits / N total Multiplying the area of ​​the rectangular region (a×b) by the area of ​​the rectangular region gives the area A corresponding to the current point cloud slice data. k , refer to Figure 7 As shown in (b), the horizontal axis represents the S-wave velocity (unit: m / s), and the vertical axis represents the north-south Y coordinate (unit: m). A diagonal cross is used to represent the scattered points located within the outer contour, and a dot is used to represent the scattered points located outside the outer contour. The area corresponding to the scattered points located within the outer contour is the area of ​​the current point cloud slice data.

[0097] In step S150, the effective microseismic event point cloud volume is calculated based on the area corresponding to the above multiple point cloud slices; the above effective microseismic event point cloud volume is used as the effective reservoir stimulation volume (ESRV) of fracturing.

[0098] Figure 8 This diagram schematically illustrates the result of obtaining the effective reservoir stimulation volume by summing the areas corresponding to multiple point cloud slices according to an embodiment of the present disclosure. The horizontal axis is the east-west X coordinate (unit: m), the vertical axis is the north-south Y coordinate (unit: m), and the vertical axis is the depth Z coordinate (unit: m).

[0099] Reference Figure 8 As shown, the area A of each point cloud slice is... k By summing the values ​​of k (k = 0, 1, ..., n) and multiplying them by the spacing h between adjacent slices, the volume V of the effective microseismic event point cloud can be obtained, which is the effective reservoir stimulation volume due to fracturing. The specific expression for the volume V of the effective microseismic event point cloud is as follows:

[0100]

[0101] In embodiments including steps S110 to S150 above, point cloud data corresponding to microseismic events generated during hydraulic fracturing are acquired. Considering that some microseismic events during hydraulic fracturing are only stress-transmission events and the actual fracturing fluid has not reached the corresponding area, the point cloud data corresponding to the microseismic event points are relatively scattered. The point cloud data corresponding to the microseismic event points in the area matched with the fracturing fluid is more concentrated, which is effective for reservoir stimulation. Therefore, based on the density of the point cloud data, noise identification and noise removal are performed on the point cloud data to obtain effective microseismic event point cloud data. The effective microseismic event point cloud data is sliced ​​to obtain multiple point cloud slice data. The outer contour of each point cloud slice data is fitted, and the area of ​​the fitted point cloud slice data is calculated to obtain the area corresponding to multiple point cloud slices. The effective microseismic event point cloud volume is calculated based on the area corresponding to the multiple point cloud slices. The effective microseismic event point cloud volume is used as the effective reservoir stimulation volume of fracturing. This can improve the accuracy of the estimated effective reservoir stimulation volume of fracturing, which helps to guide and optimize the subsequent oil and gas development process.

[0102] A second exemplary embodiment of this disclosure provides an apparatus for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0103] Figure 9 A schematic block diagram of a device for estimating the effective reservoir stimulation volume based on microseismic fracturing according to an embodiment of the present disclosure is shown.

[0104] Reference Figure 9 As shown in the present disclosure, the device 900 for estimating the effective reservoir stimulation volume based on microseismic fracturing provides the following components: a data acquisition module 910, a noise processing module 920, a slice processing module 930, a contour fitting module 940, an area calculation module 950, and a volume calculation module 960.

[0105] The aforementioned data acquisition module 910 is used to acquire point cloud data corresponding to microseismic events generated during hydraulic fracturing.

[0106] The noise processing module 920 is used to identify and remove noise from the point cloud data based on the density of the point cloud data, so as to obtain effective microseismic event point cloud data.

[0107] The aforementioned slicing module 930 is used to slice the aforementioned effective microseismic event point cloud data to obtain multiple point cloud slice data.

[0108] The aforementioned contour fitting module 940 is used to perform outer contour fitting for each point cloud slice data.

[0109] The area calculation module 950 described above is used to calculate the area of ​​the fitted point cloud slice data to obtain the area corresponding to multiple point cloud slices.

[0110] The aforementioned volume calculation module 960 is used to calculate the effective microseismic event point cloud volume based on the area corresponding to the aforementioned multiple point cloud slices; the aforementioned effective microseismic event point cloud volume is used as the effective reservoir stimulation volume for fracturing.

[0111] For more details or beneficial effects contained in this embodiment, please refer to the relevant description of the first embodiment, which will not be repeated here.

[0112] Any number of the functional modules included in the above-described estimation device can be combined into one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. At least one of the functional modules included in the above-described estimation device can be at least partially implemented as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the functional modules included in the above-described estimation device can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0113] A third exemplary embodiment of this disclosure provides an electronic device.

[0114] Figure 10 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure is shown.

[0115] Reference Figure 10As shown, the electronic device 1000 provided in this embodiment includes a processor 1001, a communication interface 1002, a memory 1003, and a communication bus 1004. The processor 1001, the communication interface 1002, and the memory 1003 communicate with each other through the communication bus 1004. The memory 1003 is used to store computer programs. When the processor 1001 executes the program stored in the memory, it implements the above-mentioned method for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0116] A fourth exemplary embodiment of this disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned method for estimating the effective reservoir stimulation volume based on microseismic fracturing.

[0117] The computer-readable storage medium may be included in the device or apparatus described in the above embodiments; or it may exist independently and not assembled into the device or apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0118] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for estimating the effective reservoir stimulation volume based on microseismic fracturing, characterized in that, include: Acquire point cloud data corresponding to microseismic events generated during hydraulic fracturing; Based on the density of the point cloud data, noise identification and noise removal are performed on the point cloud data to obtain effective microseismic event point cloud data; The effective microseismic event point cloud data is sliced ​​to obtain multiple point cloud slice data; For each point cloud slice, the outer contour is fitted, and the area of ​​the fitted point cloud slice is calculated to obtain the area corresponding to multiple point cloud slices. The effective microseismic event point cloud volume is calculated based on the area corresponding to the multiple point cloud slices; the effective microseismic event point cloud volume is used as the effective reservoir stimulation volume for fracturing.

2. The method according to claim 1, characterized in that, Based on the density of the point cloud data, noise identification and removal are performed on the point cloud data to obtain effective microseismic event point cloud data, including: Based on the DBSCAN clustering algorithm, point cloud data with a density less than a set threshold are classified into noise categories and removed to obtain effective microseismic event point cloud data. The threshold is defined by a preset neighborhood radius and a preset minimum number of neighborhood points corresponding to the core point.

3. The method according to claim 2, characterized in that, Based on the DBSCAN clustering algorithm, point cloud data with a density less than a set threshold are classified into noise categories and then removed to obtain effective microseismic event point cloud data, including: For the point cloud data corresponding to microseismic events, in each round, unlabeled microseismic event points are searched as the current points for category labeling, until all microseismic event points have been labeled. Microseismic event points marked as noise are removed to obtain effective microseismic event point cloud data; Specifically, for each point found in each round of searching, the following category labeling operation is performed: Based on the preset neighborhood radius, find the neighboring nodes in the point cloud data that are located in the neighborhood of the current point and form the neighborhood point set of the current point; Determine whether the number of neighboring nodes contained in the neighborhood set of the current point is less than the preset minimum number of neighboring points; If the number of neighboring nodes is less than the preset minimum number of neighboring points, the current point is marked as a noise category; and the search continues for the next unmarked microseismic event point as the current point for the next round. If the number of neighboring nodes exceeds the preset minimum number of neighboring points, the current point is marked as a core point; and the search continues to find neighboring nodes in the neighboring point set as the current point for the next round.

4. The method according to claim 2, characterized in that, The preset neighborhood radius and the preset minimum number of neighborhood points are obtained in the following way: Multiple data sets with different neighborhood radii and different minimum neighborhood points are used as different test parameters; Based on different test parameters and the DBSCAN clustering algorithm, the point cloud data is classified and tested to obtain the total classification categories. Based on the correspondence between the overall category and the test parameters, the data sets corresponding to the test parameters that divide the overall category into two classes are determined as the preset neighborhood radius and the preset minimum number of neighborhood points.

5. The method according to claim 1, characterized in that, For each point cloud slice, an outer contour is fitted, and the area of ​​the fitted point cloud slice is calculated to obtain the area corresponding to multiple point cloud slices, including: For each point cloud slice data, the outer contour is fitted based on the Alpha Shape algorithm to obtain the outer contour corresponding to the irregular point cloud slice. Based on the Monte Carlo random sampling method, the area of ​​the point cloud slice data corresponding to the irregular point cloud slices that have been fitted with the outer contour is calculated to obtain the area of ​​multiple point cloud slices.

6. The method according to claim 5, characterized in that, Based on the Monte Carlo random sampling method, the area of ​​point cloud slices corresponding to irregular point cloud slices fitted with outer contours is calculated to obtain the areas of multiple point cloud slices, including: For each point cloud slice data, the maximum distance of the corresponding outer contour on the first coordinate axis and the maximum distance on the second coordinate axis are used as the length and width, respectively, to create a rectangular region; Within the rectangular area, points are randomly and evenly scattered, and the probability of the scattered points hitting the area where the irregular point cloud slice is located is statistically analyzed. The probability value is multiplied by the area of ​​the rectangular region to obtain the area corresponding to the current point cloud slice data.

7. The method according to claim 5, characterized in that, For each point cloud slice, the outer contour is fitted using the Alpha Shape algorithm to obtain the outer contour corresponding to the irregular point cloud slice, including: For each point cloud slice, search for a point as the initial point and perform contour point determination operation until all points have been searched and determined. For each initial point, perform the following contour point determination operation: A circular search range is constructed using the initial point as the center and the threshold 2α as the radius. Points within the circular search range are searched and form a search point set; α represents a preset parameter. Take any point from the search point set, and combine it with the initial point to form two points to be determined; Draw two circles of different sizes whose boundaries contain the two points to be determined, with the centers of the two circles being the first center position and the second center position, respectively; Calculate the first distance from all points in the search point set to the first center position and the second distance from all points in the search point set to the second center position; Determine whether the first or second distance obtained for all points in the search point set satisfies the condition that it is greater than α; If the first or second distance obtained for all points satisfies the condition of being greater than α, then the two points to be determined are identified as contour points; and the search continues for the next undetermined point as the initial point. If the first and second distances obtained for all points do not satisfy the condition of being greater than α, then the two points to be determined are identified as non-contour points; and the search continues to find the next undetermined point in the search point set as the initial point.

8. A device for estimating the effective reservoir stimulation volume based on microseismic fracturing, characterized in that, include: The data acquisition module is used to acquire point cloud data corresponding to microseismic events generated during hydraulic fracturing. The noise processing module is used to identify and remove noise from the point cloud data based on the density of the point cloud data, so as to obtain effective microseismic event point cloud data. The slicing module is used to slice the effective microseismic event point cloud data to obtain multiple point cloud slice data. The contour fitting module is used to fit the outer contour of each point cloud slice data. The area calculation module is used to calculate the area of ​​the fitted point cloud slice data to obtain the area of ​​multiple point cloud slices. The volume calculation module is used to calculate the effective microseismic event point cloud volume based on the area corresponding to the multiple point cloud slices; the effective microseismic event point cloud volume is used as the effective reservoir stimulation volume for fracturing.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-7.

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