Method, device, equipment, medium and product for determining wellbore fracture parameters

Abstract

The embodiment of the present invention provides a method, device, equipment, medium and product for determining wellbore fracture parameters, the method comprising: obtaining reflected wave amplitude image data of a target wellbore; determining a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function; segmenting the reflected wave amplitude image data into fracture image data according to the segmentation threshold; extracting fracture parameters corresponding to each target fracture data set from the fracture image data; the target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflected wave amplitude image data. The wellbore fracture parameter determination method of the embodiment of the present invention can extract fracture parameters corresponding to each target fracture data set from the segmented fracture image data, thereby improving the accuracy of determining fracture parameters.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of oil and gas exploration and development, and in particular to a method, device, equipment, medium and product for determining wellbore fracture parameters. Background Art

[0002] In the field of oil and gas exploration and development technology, wellbore detection and testing is a very important link in terms of oil and gas production capacity and safety, especially the detection of wellbore cracks. At present, wellbore detection and testing is mainly carried out through ultrasonic imaging logging. Ultrasonic imaging logging uses a rotating transducer probe to continuously rotate and scan the ultrasonic beam to measure the amplitude and propagation time of the well wall reflection wave, thereby obtaining the well wall reflection wave amplitude image.

[0003] After the reflection wave amplitude image is obtained, the crack situation in the reflection wave amplitude image is identified manually. However, the current method of manually identifying cracks in the reflection wave amplitude image can identify whether there are cracks, but it is difficult to determine the parameters of the cracks. Summary of the invention

[0004] The present invention provides a method, device, equipment, medium and product for determining wellbore fracture parameters, which are used to solve the problem that although the manual method of identifying fractures in the reflection wave amplitude image can identify whether there are fractures, it is difficult to determine the parameters of the fractures.

[0005] A first aspect of an embodiment of the present invention provides a method for determining wellbore fracture parameters, comprising:

[0006] Acquire the reflected wave amplitude image data of the target wellbore;

[0007] Determine a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function;

[0008] Segmenting the reflected wave amplitude image data into crack image data according to the segmentation threshold;

[0009] The fracture parameters corresponding to each target fracture data set are extracted from the fracture image data; the target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data.

[0010] Furthermore, in the above method, determining the segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function includes:

[0011] Determine wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function;

[0012] Determine a plurality of modulus maximum points according to the horizontal component and the vertical component of the wavelet domain amplitude image data;

[0013] The segmentation threshold is determined according to each modulus maximum point.

[0014] Furthermore, in the above method, the step of determining the wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function comprises:

[0015] Performing fast Fourier transform processing on the reflected wave amplitude image data and the preset wavelet function to determine the transformed reflected wave amplitude image data and the wavelet function;

[0016] determining the product between the transformed reflected wave amplitude image data and the wavelet function as frequency image data;

[0017] The frequency image data is subjected to inverse fast Fourier transform processing to determine wavelet domain amplitude image data.

[0018] Furthermore, in the above method, the step of determining the segmentation threshold according to each modulus maximum point includes:

[0019] Determine the corresponding wavelet domain amplitude image data according to each modulus maximum point;

[0020] Determine the reflected wave amplitude value of the corresponding reflected wave amplitude image data according to the corresponding wavelet domain amplitude image data;

[0021] The average value of each reflected wave amplitude is determined as the segmentation threshold.

[0022] Furthermore, in the above method, segmenting the reflected wave amplitude image data into crack image data according to the segmentation threshold comprises:

[0023] Determine the reflected wave amplitude image data whose amplitude is less than the segmentation threshold in the reflected wave amplitude image data as the image data to be segmented;

[0024] The image data to be segmented is segmented from the reflected wave amplitude image data, and the segmented image data to be segmented is determined as crack image data.

[0025] Further, in the method as described above, the crack parameters at least include: the number of pixels of the target crack data set;

[0026] After determining the crack parameters corresponding to each target crack data set according to the crack image data, the method further includes:

[0027] Determine a target crack data set whose pixel number is less than a preset threshold as a secondary crack image data set;

[0028] The crack parameters corresponding to each secondary crack image data set are extracted.

[0029] A second aspect of an embodiment of the present invention provides a device for determining wellbore fracture parameters, comprising:

[0030] An acquisition module, used for acquiring reflected wave amplitude image data of a target wellbore;

[0031] A threshold determination module, used to determine a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function;

[0032] A segmentation module, used for segmenting the reflection wave amplitude image data into crack image data according to the segmentation threshold;

[0033] The parameter extraction module is used to extract the fracture parameters corresponding to each target fracture data set from the fracture image data; the target fracture data set is the image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data.

[0034] Further, in the above device, the threshold determination module is specifically used for:

[0035] Determine wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function; determine multiple modulus maximum points according to the horizontal component and vertical component of the wavelet domain amplitude image data; and determine a segmentation threshold according to each modulus maximum point.

[0036] Furthermore, in the above-mentioned device, when the threshold determination module determines the wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function, it is specifically used to:

[0037] The reflected wave amplitude image data and the preset wavelet function are subjected to fast Fourier transform processing to determine the transformed reflected wave amplitude image data and the wavelet function; the product between the transformed reflected wave amplitude image data and the wavelet function is determined as frequency image data; the frequency image data is subjected to inverse fast Fourier transform processing to determine the wavelet domain amplitude image data.

[0038] Furthermore, in the above-mentioned device, when determining the segmentation threshold according to each modulus maximum point, the threshold determination module is specifically used to:

[0039] Determine the corresponding wavelet domain amplitude image data according to each modulus maximum point; determine the corresponding reflection wave amplitude value of the reflection wave amplitude image data according to the corresponding wavelet domain amplitude image data; and determine the average value of each reflection wave amplitude value as the segmentation threshold.

[0040] Furthermore, in the above device, the segmentation module is specifically used for:

[0041] The reflected wave amplitude image data with an amplitude less than the segmentation threshold in the reflected wave amplitude image data is determined as the image data to be segmented; the image data to be segmented is segmented from the reflected wave amplitude image data, and the segmented image data to be segmented is determined as the crack image data.

[0042] Further, in the above-mentioned device, the crack parameters at least include: the number of pixels of the target crack data set;

[0043] The wellbore fracture parameter determination device further comprises:

[0044] The secondary parameter extraction module is used to determine the target crack data set whose pixel number is less than a preset threshold as a secondary crack image data set; and extract the crack parameters corresponding to each secondary crack image data set.

[0045] A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor;

[0046] Memory; Memory for storing instructions executable by the processor;

[0047] Wherein, the processor is configured to execute the method for determining wellbore fracture parameters as described in any one of the first aspects.

[0048] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method for determining wellbore fracture parameters described in any one of the first aspects.

[0049] A fifth aspect of an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining wellbore fracture parameters as described in any one of the first aspects.

[0050] A method, device, equipment, medium and product for determining wellbore fracture parameters provided by an embodiment of the present invention include: obtaining reflected wave amplitude image data of a target wellbore; determining a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function; segmenting the reflected wave amplitude image data into fracture image data according to the segmentation threshold; extracting fracture parameters corresponding to each target fracture data set from the fracture image data; the target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflected wave amplitude image data. The method for determining wellbore fracture parameters of an embodiment of the present invention first obtains the reflected wave amplitude image data of the target wellbore, and determines a segmentation threshold used in the subsequent segmentation of the reflected wave amplitude image data according to the reflected wave amplitude image data and a preset wavelet function. The segmentation threshold can be used to distinguish fracture image data corresponding to fractures in the reflected wave amplitude image data from other interference data. At the same time, the segmentation threshold can be used to segment fracture image data corresponding to fractures from the reflected wave amplitude image data. Thus, fracture parameters corresponding to each target fracture data set can be extracted from the segmented fracture image data, thereby improving the accuracy of determining fracture parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0052] Figure 1 A scene diagram of a method for determining wellbore fracture parameters in an embodiment of the present invention can be implemented;

[0053] Figure 2 A schematic diagram of a flow chart of a method for determining wellbore fracture parameters provided by a first embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a flow chart of a method for determining wellbore fracture parameters provided in a second embodiment of the present invention;

[0055] Figure 4 A comparison diagram of the segmentation effect of the method for determining wellbore fracture parameters provided by the second embodiment of the present invention;

[0056] Figure 5 A comparison diagram of the secondary segmentation effect of the method for determining wellbore fracture parameters provided by the second embodiment of the present invention;

[0057] Figure 6 A parameter extraction diagram of a method for determining wellbore fracture parameters provided by a second embodiment of the present invention;

[0058] Figure 7 A schematic diagram of the structure of a wellbore fracture parameter determination device provided in a third embodiment of the present invention;

[0059] Figure 8 A schematic structural diagram of an electronic device provided in a fourth embodiment of the present invention.

[0060] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0061] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0062] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0063] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail. In the field of oil and gas exploration and development technology, whether the wellbore cracks are well developed has a great relationship with the production capacity of oil and gas extraction. Therefore, the detection of wellbore cracks is a very important link in the current field of oil and gas exploration and development technology. At present, the exploration and testing of the wellbore is mainly carried out through ultrasonic imaging logging, which continuously rotates and scans to emit ultrasonic beams, measures the amplitude and propagation time of the wellbore reflection wave, and obtains the wellbore reflection wave amplitude image. Then, the color depth of the reflection wave amplitude image is used to manually identify the cracks in the reflection wave amplitude image. In general, humans can only identify the number of cracks and whether there are cracks, and it is difficult to determine the parameters of the cracks.

[0064] Therefore, in view of the problem that the existing method of manually identifying cracks in the reflection wave amplitude image can identify whether there are cracks, but it is difficult to determine the parameters of the cracks, the inventors found in the research that in order to solve the problem that it is difficult to determine the parameters of the cracks manually, the data corresponding to the cracks in the reflection wave amplitude image data can be segmented out to more accurately extract the parameters of the cracks. Specifically, the reflection wave amplitude image data of the target wellbore is first obtained, and the segmentation threshold used in the subsequent segmentation of the reflection wave amplitude image data is determined according to the reflection wave amplitude image data and the preset wavelet function. The segmentation threshold can be used to distinguish the crack image data corresponding to the cracks in the reflection wave amplitude image data from other interference data. At the same time, the crack image data corresponding to the crack can be segmented from the reflection wave amplitude image data by the segmentation threshold. Therefore, the crack parameters corresponding to each target crack data set can be extracted from the segmented crack image data, thereby improving the accuracy of determining the crack parameters.

[0065] Based on the above creative findings, the inventor proposed the technical solution of the present application.

[0066] The following is an introduction to the application scenarios of the method for determining wellbore fracture parameters provided by the embodiment of the present invention. Figure 1 As shown, 1 is a first electronic device, and 2 is a second electronic device. The network architecture of the application scenario corresponding to the method for determining wellbore fracture parameters provided by the embodiment of the present invention includes: a first electronic device 1 and a second electronic device 2. The second electronic device 2 stores the reflected wave amplitude image data of the target wellbore. When the fracture parameters of the target wellbore need to be determined, the first electronic device 1 obtains the reflected wave amplitude image data of the target wellbore from the second electronic device 2. Then, the first electronic device 1 determines the segmentation threshold used in the subsequent segmentation of the reflected wave amplitude image data according to the reflected wave amplitude image data and the preset wavelet function. The preset wavelet function can use a spline wavelet function to improve the accuracy of the segmentation threshold. At the same time, the first electronic device 1 segments the reflected wave amplitude image data into fracture image data according to the segmentation threshold, and then extracts the fracture parameters corresponding to each target fracture data set from the fracture image data. The target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflected wave amplitude image data. After the first electronic device 1 extracts the fracture parameters corresponding to each target fracture data set, the fracture development of the target wellbore can be analyzed according to each fracture parameter, such as analyzing the area size and length of the fracture, so as to analyze the oil and gas production capacity of the target wellbore according to the fracture development. At the same time, the crack parameters can also be sent to other electronic devices to implement subsequent analysis processes.

[0067] The embodiments of the present invention are described below in conjunction with the accompanying drawings.

[0068] Figure 2A schematic diagram of a flow chart of a method for determining wellbore fracture parameters provided by the first embodiment of the present invention, as shown in FIG. Figure 2 As shown, in this embodiment, the execution subject of the embodiment of the present invention is a wellbore fracture parameter determination device, and the wellbore fracture parameter determination device can be integrated in an electronic device. Then the wellbore fracture parameter determination method provided in this embodiment includes the following steps:

[0069] Step S101, obtaining reflected wave amplitude image data of the target wellbore.

[0070] In this embodiment, the acquisition method may be to obtain from a database storing the reflected wave amplitude image data, or to obtain from a collection device, which is not limited in this embodiment.

[0071] The reflected wave amplitude image data is a time domain image data, which mainly reflects the relationship between the reflected wave amplitude change and time.

[0072] Step S102: determining a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function.

[0073] In this embodiment, the wavelet function can use a spline wavelet function to determine a more accurate segmentation threshold. The segmentation threshold is the basis for subsequently segmenting the reflection wave amplitude image data into crack image data. The reflection wave amplitude image data and the preset wavelet function are mainly used to determine the image data corresponding to the crack, so as to determine the segmentation threshold according to the image data corresponding to the crack.

[0074] Step S103, segmenting the reflected wave amplitude image data into crack image data according to the segmentation threshold.

[0075] In this embodiment, the segmentation threshold refers to the reflection wave amplitude value used as the basis for segmentation. The reflection wave amplitude image data is segmented into crack image data by the segmentation threshold, thereby providing a basis for subsequently determining the parameters corresponding to the cracks.

[0076] Step S104: extract fracture parameters corresponding to each target fracture data set from the fracture image data. The target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data.

[0077] In this embodiment, since there are usually multiple cracks in the target wellbore, and each crack has multiple image data, it is necessary to determine the image data set corresponding to the cracks in the target wellbore, so as to extract the corresponding crack parameters according to each target crack data set. The crack parameters may include area, perimeter, length, width, etc.

[0078] A method for determining wellbore fracture parameters provided by an embodiment of the present invention comprises: obtaining reflected wave amplitude image data of a target wellbore. Determining a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function. Segmenting the reflected wave amplitude image data into fracture image data according to the segmentation threshold. Extracting fracture parameters corresponding to each target fracture data set from the fracture image data. The target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflected wave amplitude image data. The method for determining wellbore fracture parameters of an embodiment of the present invention first obtains the reflected wave amplitude image data of the target wellbore, and determines a segmentation threshold used in the subsequent segmentation of the reflected wave amplitude image data according to the reflected wave amplitude image data and a preset wavelet function. The segmentation threshold can be used to distinguish the fracture image data corresponding to the fracture in the reflected wave amplitude image data from other interference data. At the same time, the segmentation threshold can be used to segment the fracture image data corresponding to the fracture from the reflected wave amplitude image data. Thus, the fracture parameters corresponding to each target fracture data set can be extracted from the segmented fracture image data, thereby improving the accuracy of determining the fracture parameters.

[0079] Figure 3 A flow chart of a method for determining wellbore fracture parameters provided by the second embodiment of the present invention is shown in FIG. Figure 3 As shown, the method for determining wellbore fracture parameters provided in this embodiment is based on the method for determining wellbore fracture parameters provided in the previous embodiment of the present invention, and each step is further refined. The method for determining wellbore fracture parameters provided in this embodiment includes the following steps.

[0080] Step S201, obtaining reflection wave amplitude image data of the target wellbore.

[0081] In this embodiment, the implementation of step 201 is similar to the implementation of step 101 in the previous embodiment of the present invention, and will not be described in detail here.

[0082] Step S202: determining wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function.

[0083] In this embodiment, the wavelet domain amplitude image data is two-dimensional image data that has been processed by wavelet coefficient transformation and Fourier transformation between the reflected wave amplitude image data and a preset wavelet function.

[0084] The corresponding modulus value can be determined through the wavelet domain amplitude image data, and the modulus maximum value that is more closely related to the crack can be determined based on the modulus value.

[0085] Optionally, in this embodiment, determining the wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function includes:

[0086] The reflected wave amplitude image data and the preset wavelet function are subjected to fast Fourier transform processing to determine the transformed reflected wave amplitude image data and the wavelet function.

[0087] The product between the transformed reflected wave amplitude image data and the wavelet function is determined as frequency image data.

[0088] The frequency image data is processed by inverse fast Fourier transform to determine the wavelet domain amplitude image data.

[0089] In this embodiment, the reflected wave amplitude image data and the preset wavelet function are subjected to fast Fourier transform processing in order to convert both the reflected wave amplitude image data and the preset wavelet function into image data in the frequency domain.

[0090] At the same time, the frequency image data is processed by inverse fast Fourier transform, so that the wavelet domain amplitude image data can be determined.

[0091] Step S203, determining a plurality of modulus maximum points according to the horizontal component and the vertical component of the wavelet domain amplitude image data.

[0092] In this embodiment, the modulus value can be determined by the horizontal component and the vertical component of the wavelet domain amplitude image data. The modulus value is equal to the square root of the sum of the square of the horizontal component and the square of the vertical component. The modulus value is the two-dimensional wavelet coefficient under the wavelet transform. The modulus maximum point is the point with the maximum modulus value, corresponding to the spatial point with the steepest change in the reflected wave amplitude, that is, the edge point between the crack target and the background rock.

[0093] Step S204, determining a segmentation threshold according to each modulus maximum point.

[0094] In this embodiment, the modulus maximum point can be determined in the horizontal direction or the vertical direction by a three-point peak-finding algorithm, and the vertical direction is more efficient. Since the modulus maximum point corresponds to the spatial point where the reflected wave amplitude changes steepest, that is, the edge point between the crack target and the background rock. Therefore, the segmentation threshold can be determined according to each modulus maximum point, for example, the segmentation threshold can be determined by the reflected wave amplitude of the reflected wave amplitude image data corresponding to each modulus maximum point. The segmentation threshold can be set to the average value of the reflected wave amplitude corresponding to each modulus maximum point, or it can be set to the middle value of the reflected wave amplitude corresponding to each modulus maximum point. It can be set according to actual needs, and this embodiment does not limit this.

[0095] Optionally, in this embodiment, determining the segmentation threshold according to each modulus maximum point includes:

[0096] The corresponding wavelet domain amplitude image data is determined according to each modulus maximum point.

[0097] The reflected wave amplitude value of the corresponding reflected wave amplitude image data is determined according to the corresponding wavelet domain amplitude image data.

[0098] The average value of each reflected wave amplitude is determined as the segmentation threshold.

[0099] In this embodiment, the wavelet domain amplitude image data is obtained from the reflected wave amplitude image data after transformation processing, and thus has an associated relationship with the reflected wave amplitude image data. The reflected wave amplitude value of the corresponding reflected wave amplitude image data can be determined according to the corresponding wavelet domain amplitude image data.

[0100] At the same time, since the data corresponding to the cracks are segmented from the reflection wave amplitude image data, the average value of each reflection wave amplitude value can be determined as the segmentation threshold.

[0101] Step S205: Determine the reflected wave amplitude image data whose amplitude is less than the segmentation threshold in the reflected wave amplitude image data as the image data to be segmented.

[0102] In this embodiment, since image data with high reflection wave amplitude is generally background rock target, and image data with low reflection wave amplitude is generally crack image data, the reflection wave amplitude image data with amplitude less than the segmentation threshold in the reflection wave amplitude image data can be determined as the image data to be segmented.

[0103] Step S206 , segmenting the image data to be segmented from the reflected wave amplitude image data, and determining the segmented image data to be segmented as crack image data.

[0104] In this embodiment, the crack image data is the image data to be segmented that is segmented from the reflection wave amplitude image data.

[0105] Step S207: extract fracture parameters corresponding to each target fracture data set from the fracture image data. The target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data.

[0106] In this embodiment, the implementation of step 207 is similar to the implementation of step 104 in the previous embodiment of the present invention, and will not be described in detail here.

[0107] It should be noted that the crack parameters at least include: the number of pixels in the target crack data set.

[0108] Step S208 : determining a target crack data set whose pixel number is less than a preset threshold as a secondary crack image data set.

[0109] In this embodiment, the number of pixels of the target fracture data set is generally referred to as the area of ​​the target fracture data set. Since the borehole of the wellbore may be regular or irregular, the target fracture data set determined after a segmentation may still contain image data of background rocks. Therefore, the target fracture data set whose number of pixels of the target fracture data set is less than a preset threshold can be screened out through secondary screening. Usually, the preset threshold can be set to 800. When the number of pixels of the target fracture data set is greater than or equal to 800, the target fracture data set is a data set of background rocks. When the number of pixels of the target fracture data set is less than 800, the target fracture data set is a data set of fractures.

[0110] At the same time, it can also be judged according to the roundness of the target fracture data set. The roundness of the target fracture data set is defined as the ratio of the square of the perimeter of the target fracture data set to the area of ​​the target fracture data set divided by 4π, where the perimeter of the target fracture data set is defined as the number of pixels on the edge of the target fracture data set. When the roundness of the target fracture data set is less than 2, the target fracture data set is a data set of background rock. When the roundness of the target fracture data set is greater than or equal to 2, the target fracture data set is a data set of fractures.

[0111] Step S209, extracting the crack parameters corresponding to each secondary crack image data set.

[0112] In this embodiment, the crack parameters corresponding to each secondary crack image data set are more accurate than those of the initial crack image data set, and the number of secondary crack image data sets is closer to the actual number of cracks.

[0113] At the same time, after determining the secondary fracture image data set, the fracture parameters such as length, width, roundness, and area of ​​each secondary fracture image data set can be analyzed. Among them, the length is defined as the maximum distance between the boundary points. The width is defined as the length of the line with the largest distance in a set of straight lines perpendicular to the length on the edge. In addition, the average length, average width, average roundness, and average area of ​​all secondary fracture image data sets can also be analyzed, thereby providing a basis for the subsequent analysis of the oil and gas production capacity of the wellbore.

[0114] A method for determining wellbore fracture parameters provided by an embodiment of the present invention determines a more accurate segmentation threshold by performing fast Fourier transform processing and fast Fourier transform inverse processing on reflection wave amplitude image data and a preset wavelet function. Thus, more accurate fracture parameters corresponding to each target fracture data set are determined according to the segmentation threshold. At the same time, by performing secondary screening on the target fracture data set, the fracture parameters corresponding to the screened secondary fracture image data set are more accurate than those of the initial fracture image data set, and the number of secondary fracture image data sets is closer to the actual number of fractures.

[0115] In order to better illustrate the solution of this embodiment, Figure 4-Figure 6 As shown, the fracture parameter determination process of a wellbore in a certain area is taken as an example to illustrate, and at the same time, this embodiment is illustrated by two segmentations. First, the reflection wave amplitude image data of the wellbore in a certain area is obtained, and the reflection wave amplitude image data is divided into reflection wave amplitude image data of multiple image frames according to the depth of the wellbore.

[0116] The reflected wave amplitude image data in each image frame is subjected to FFT transformation (fast Fourier transform in Chinese, full name: fast Fourier transform) column by column and row by row, and the spline wavelet function is also subjected to FFT transformation. Then, the reflected wave amplitude image data after FFT transformation is multiplied with the spline wavelet function, and the product data is subjected to inverse FFT transformation to obtain the wavelet domain amplitude image data.

[0117] The modulus value is calculated based on the horizontal and vertical components of the wavelet domain amplitude image data, and is used to determine the edge features of the image. The modulus maximum point is found in the vertical direction of the wavelet domain amplitude image data using a three-point peak search algorithm. The modulus maximum point corresponds to the spatial point with the steepest change in the reflected wave amplitude, that is, the edge point between the fracture target and the background rock.

[0118] The reflected wave amplitude values ​​at the coordinate positions of the reflected wave amplitude image data corresponding to all modulus maximum points are counted, and the average value of the reflected wave amplitude values ​​corresponding to each modulus maximum point is calculated. Then, the calculated average value of the reflected wave amplitude is used as the initial segmentation threshold, and the crack targets with low reflection wave amplitudes below the average value of the reflected wave amplitude are retained, and the background rock targets with high reflection wave amplitudes above the average value of the reflected wave amplitude are removed, and the reflection wave amplitude image is initially segmented.

[0119] Move the image frame and repeat the above steps until the reflection wave amplitude image is completely processed. The initial segmentation image is as follows: Figure 4 shown. Figure 4In the figure, image orientation refers to direction, N, E, S, and W refer to north, east, south, and west, respectively. Since the wellbore is circular, the reflected wave amplitude image is the expanded image of the wellbore.

[0120] In the reflection wave amplitude image, the background rock and fractures when the wellbore is irregular are both responses of low reflection wave amplitude, but the two are different in roundness and area. In order to use the initial segmentation target shape parameters to distinguish the background rock and fracture targets when the wellbore is irregular, the image obtained by the initial segmentation is further processed to extract the roundness and area of ​​the low reflection wave amplitude data set in the image. At this time, the low reflection wave amplitude data set includes the target fracture data set and the background rock data set.

[0121] The area of ​​the low reflection wave amplitude data set is defined as the number of pixels in the low reflection wave amplitude data set.

[0122]

[0123] Among them, T is the initial segmentation threshold, f(x,y) is the reflection amplitude, and A is the area.

[0124] The perimeter of the low reflection wave amplitude data set is defined as the number of pixels on the edge of the low reflection wave amplitude data set. The length of the low reflection wave amplitude data set is defined as the maximum distance between two points between boundary points. The width of the low reflection wave amplitude data set is defined as the length of the line with the largest distance among a set of lines perpendicular to the length on the edge.

[0125] The circularity of the low reflection wave amplitude data set is defined as the ratio of the square of the perimeter of the low reflection wave amplitude data set to the area of ​​the low reflection wave amplitude data set divided by 4π.

[0126]

[0127] Where L is the circumference of the low reflection wave amplitude data set, and C is the circularity of the low reflection wave amplitude data set.

[0128] The secondary segmentation is performed based on the roundness or area of ​​the low reflection wave amplitude data set. In general, when the borehole is irregular, the roundness of the background rock noise is very small and the area is very large. Taking the roundness <2 or the area >800 as the criterion for the shape parameter of the background rock noise when the borehole is irregular, the initial segmentation fracture sub-image of the reflection wave amplitude image is secondary segmented to obtain the secondary segmentation fracture sub-image, such as Figure 5 shown. Figure 5 The image data belonging to the background rocks is removed.

[0129] For the segmented crack sub-images, based on the crack parameter calculation of the target crack data set, parameters such as the average roundness, average length, average width, crack surface ratio and crack pipe flow model capacity index of the multi-target crack data set in the image frame are extracted to characterize the cracks.

[0130] The final extracted crack parameters are as follows Figure 6 As shown, from Figure 6 The average length, average roundness, fracture surface ratio and other parameters of the cracks in the target wellbore at various depths can be intuitively seen.

[0131] Figure 7 A schematic diagram of the structure of a wellbore fracture parameter determination device provided in the third embodiment of the present invention is shown in FIG. Figure 6 As shown, in this embodiment, the wellbore fracture parameter determination device 300 includes:

[0132] The acquisition module 301 is used to acquire the reflected wave amplitude image data of the target wellbore.

[0133] The threshold determination module 302 is used to determine the segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function.

[0134] The segmentation module 303 is used to segment the reflection wave amplitude image data into crack image data according to the segmentation threshold.

[0135] The parameter extraction module 304 is used to extract the fracture parameters corresponding to each target fracture data set from the fracture image data. The target fracture data set is the image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data.

[0136] The wellbore fracture parameter determination device provided in this embodiment can be executed Figure 2 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 2 The method embodiments shown are similar and will not be described in detail here.

[0137] At the same time, the wellbore fracture parameter determination device provided by the present invention further refines the wellbore fracture parameter determination device 300 based on the wellbore fracture parameter determination device provided by the previous embodiment.

[0138] Optionally, in this embodiment, the threshold determination module 302 is specifically used to:

[0139] Determine the wavelet domain amplitude image data according to the reflected wave amplitude image data and the preset wavelet function. Determine multiple modulus maximum points according to the horizontal component and vertical component of the wavelet domain amplitude image data. Determine the segmentation threshold according to each modulus maximum point.

[0140] Optionally, in this embodiment, when the threshold determination module 302 determines the wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function, it is specifically used to:

[0141] The reflected wave amplitude image data and the preset wavelet function are subjected to fast Fourier transform processing to determine the transformed reflected wave amplitude image data and the wavelet function. The product between the transformed reflected wave amplitude image data and the wavelet function is determined as frequency image data. The frequency image data is subjected to inverse fast Fourier transform processing to determine the wavelet domain amplitude image data.

[0142] Optionally, in this embodiment, when the threshold determination module 302 determines the segmentation threshold according to each modulus maximum point, it is specifically used to:

[0143] The corresponding wavelet domain amplitude image data is determined according to each modulus maximum point. The corresponding reflected wave amplitude value of the reflected wave amplitude image data is determined according to the corresponding wavelet domain amplitude image data. The average value of each reflected wave amplitude value is determined as the segmentation threshold.

[0144] Optionally, in this embodiment, the segmentation module 303 is specifically used for:

[0145] The reflected wave amplitude image data with an amplitude less than the segmentation threshold in the reflected wave amplitude image data are determined as the image data to be segmented. The image data to be segmented are segmented from the reflected wave amplitude image data, and the segmented image data to be segmented are determined as the crack image data.

[0146] Optionally, in this embodiment, the crack parameter includes at least: the number of pixel points in the target crack data set.

[0147] The wellbore fracture parameter determination device 300 further includes:

[0148] The secondary parameter extraction module is used to determine the target crack data set whose pixel number is less than a preset threshold as a secondary crack image data set, and extract the crack parameters corresponding to each secondary crack image data set.

[0149] The wellbore fracture parameter determination device provided in this embodiment can be executed Figure 2-Figure 6 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 2-Figure 6 The method embodiments shown are similar and will not be described in detail here.

[0150] According to an embodiment of the present invention, the present invention also provides an electronic device, a computer-readable storage medium and a computer program product.

[0151] like Figure 8 As shown, Figure 8: is a schematic diagram of the structure of an electronic device provided by the fourth embodiment of the present invention. The electronic device is intended to be various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0152] like Figure 8 As shown, the electronic device includes: a processor 401 and a memory 402. The various components are connected to each other using different buses and can be installed on a common mainboard or in other ways as required. The processor can process instructions executed in the electronic device.

[0153] The memory 402 is a non-transient computer-readable storage medium provided by the present invention. The memory stores instructions executable by at least one processor to enable at least one processor to execute the method for determining wellbore fracture parameters provided by the present invention. The non-transient computer-readable storage medium of the present invention stores computer instructions, which are used to enable a computer to execute the method for determining wellbore fracture parameters provided by the present invention.

[0154] The memory 402 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the method for determining wellbore fracture parameters in the embodiment of the present invention (for example, the attached Figure 7 The processor 401 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 402, that is, the method for determining the wellbore fracture parameters in the above method embodiment is implemented.

[0155] At the same time, this embodiment also provides a computer product. When the instructions in the computer product are executed by the processor of the electronic device, the electronic device can execute the wellbore fracture parameter determination method of the above-mentioned embodiments one and two.

[0156] Those skilled in the art will readily appreciate other implementations of the embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the embodiments of the present invention that follow the general principles of the embodiments of the present invention and include common knowledge or customary technical means in the art that are not disclosed in the embodiments of the present invention. The specification and examples are to be regarded as exemplary only, and the true scope and spirit of the embodiments of the present invention are indicated by the following claims.

[0157] It should be understood that the embodiments of the present invention are not limited to the precise structures described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the embodiments of the present invention is limited only by the appended claims.

Claims

1. A method for determining wellbore fracture parameters, characterized in that: include: Acquire the reflected wave amplitude image data of the target wellbore; Determine a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function; Segmenting the reflected wave amplitude image data into crack image data according to the segmentation threshold; Extracting fracture parameters corresponding to each target fracture data set from the fracture image data; the target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data; After determining the crack parameters corresponding to each target crack data set according to the crack image data, the method further includes: Determine a target crack data set whose target shape parameter is less than a preset threshold as a secondary crack image data set, wherein the target shape parameter is area or roundness; Extracting crack parameters corresponding to each secondary crack image data set, wherein the crack parameters at least include: the number of pixel points of the target crack data set; The determining of the segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function comprises: Determine wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function; Determine a plurality of modulus maximum points according to the horizontal component and the vertical component of the wavelet domain amplitude image data; Determine the segmentation threshold according to each modulus maximum point; The step of segmenting the reflected wave amplitude image data into crack image data according to the segmentation threshold comprises: Determine the reflected wave amplitude image data whose amplitude is less than the segmentation threshold in the reflected wave amplitude image data as the image data to be segmented; The image data to be segmented is segmented from the reflected wave amplitude image data, and the segmented image data to be segmented is determined as crack image data.

2. The method according to claim 1, characterized in that The step of determining the wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function comprises: Performing fast Fourier transform processing on the reflected wave amplitude image data and the preset wavelet function to determine the transformed reflected wave amplitude image data and the wavelet function; determining the product between the transformed reflected wave amplitude image data and the wavelet function as frequency image data; The frequency image data is subjected to inverse fast Fourier transform processing to determine wavelet domain amplitude image data.

3. The method according to claim 1, characterized in that Determining the segmentation threshold according to each modulus maximum point includes: Determine the corresponding wavelet domain amplitude image data according to each modulus maximum point; Determine the reflected wave amplitude value of the corresponding reflected wave amplitude image data according to the corresponding wavelet domain amplitude image data; The average value of each reflected wave amplitude is determined as the segmentation threshold.

4. A device for determining wellbore fracture parameters, characterized in that: include: An acquisition module, used for acquiring reflected wave amplitude image data of a target wellbore; A threshold determination module is used to determine a segmentation threshold according to the reflected wave amplitude image data and a preset wavelet function; specifically used to: determine wavelet domain amplitude image data according to the reflected wave amplitude image data and a preset wavelet function; determine a plurality of modulus maximum points according to the horizontal component and the vertical component of the wavelet domain amplitude image data; and determine a segmentation threshold according to each modulus maximum point; a segmentation module, used to segment the reflected wave amplitude image data into crack image data according to the segmentation threshold; specifically used to: determine the reflected wave amplitude image data with an amplitude less than the segmentation threshold in the reflected wave amplitude image data as image data to be segmented; segment the image data to be segmented from the reflected wave amplitude image data, and determine the segmented image data to be segmented as crack image data; A parameter extraction module, used to extract fracture parameters corresponding to each target fracture data set from the fracture image data; the target fracture data set is an image data set corresponding to the fracture of the target wellbore in the reflection wave amplitude image data; Secondary parameter extraction module for: Determine a target crack data set whose target shape parameter is less than a preset threshold as a secondary crack image data set, wherein the target shape parameter is area or roundness; The crack parameters corresponding to each secondary crack image data set are extracted, and the crack parameters at least include: the number of pixel points of the target crack data set.

5. An electronic device, characterized in that: include: Memory, processor; Memory; a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the method for determining wellbore fracture parameters as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method for determining wellbore fracture parameters according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining wellbore fracture parameters according to any one of claims 1 to 3 is implemented.

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