Crack structure description method and device, storage medium and processor

Abstract

The invention relates to the field of geological exploration, and discloses a fracture structure description method and device, a storage medium and a processor, and the method comprises the steps: obtaining original waveform data corresponding to a target depth interval; performing singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstructing the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; and carrying out fracture structure description based on the target waveform data. Therefore, detail information is well reserved while complex noise interference is effectively removed, local features and edge contours of the outside-well crack structure can be enhanced in imaging, and the outside-well crack structure is more accurately depicted.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and in particular to a fracture structure characterization method, a fracture structure characterization device, a machine-readable storage medium and a processor. Background Art

[0002] Acoustic remote detection technology is a technology that uses the propagation characteristics of sound waves for long-distance detection and monitoring. It is widely used in the field of geological exploration. Through complex wave field processing technology, the development of geological bodies such as fractures and caves outside the well can be obtained, and the underground structure and rock characteristics can be inferred, which can then be used to explore mineral resources, groundwater resources, and oil and gas reservoirs.

[0003] At present, the processing flow of characterizing the fracture structure outside the well based on acoustic wave remote detection technology usually includes raw data preprocessing, wellbore direct wave suppression, offset imaging, imaging filtering, imaging quality monitoring, etc.

[0004] However, the above processing method has boundary effects and lacks multi-scale characteristics. While suppressing noise, it will also blur and destroy some detailed information, such as the edge information of the off-hole fracture structure, resulting in unsatisfactory characterization accuracy of the off-hole fracture structure. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem of unsatisfactory accuracy in characterizing an out-of-hole fracture structure in the prior art, and to provide a fracture structure characterization method, a fracture structure characterization device, a machine-readable storage medium and a processor.

[0006] In order to achieve the above object, the present invention provides a method for characterizing a fracture structure, the method comprising: Obtaining the original waveform data corresponding to the target depth interval; Performing singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstructing the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; Performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; The fracture structure is characterized based on the target waveform data.

[0007] In the embodiment of the present application, before performing singular value decomposition processing on the original waveform data, the method further includes: performing imaging preprocessing on the original waveform data; The performing singular value decomposition processing on the original waveform data comprises: Perform singular value decomposition on the original waveform data that has undergone imaging preprocessing.

[0008] In the embodiment of the present application, the first waveform data is reconstructed based on a target singular value greater than a preset value, based on the following formula: ;

[0009] in, is an m×n matrix data corresponding to the first waveform data, is an m×r matrix data, is an r×r matrix data, is an r×n matrix data, r is less than m, and r is less than n;

[0010] The r singular values ​​in are the target singular values.

[0011] In the embodiment of the present application, the denoising process is performed on the second waveform data to obtain the target waveform data corresponding to the target depth interval, including: Performing a first denoising process on the second waveform data using a window function to obtain third waveform data; The third waveform data is subjected to imaging enhancement processing to obtain target waveform data corresponding to the target depth interval.

[0012] In the embodiment of the present application, the first denoising process is performed on the second waveform data using a window function based on the following formula: ;

[0013] Wherein, g(x) is the third waveform data, is the second waveform data, ω 1 is the upper frequency bound, ω 2 is the lower frequency limit, and K is the transition band width.

[0014] In the embodiment of the present application, before obtaining the original waveform data corresponding to the target depth interval, the method further includes: Acquire original waveform data corresponding to the first depth interval; The original waveform data corresponding to the target depth interval is obtained from the original waveform data corresponding to the first depth interval.

[0015] In the embodiment of the present application, the first depth interval includes a plurality of target depth intervals. The characterization of the fracture structure based on the target waveform data comprises: The fracture structure is characterized based on the target waveform data corresponding to each target depth interval.

[0016] A second aspect of the present application provides a fracture structure characterization device, comprising: An acquisition module is used to acquire the original waveform data corresponding to the target depth interval; A reconstruction module, configured to perform singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstruct the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; A denoising module, used for performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; An imaging module is used to characterize the fracture structure based on the target waveform data.

[0017] A third aspect of the present application provides a processor configured to execute the above-mentioned fracture structure characterization method.

[0018] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the above-mentioned fracture structure characterization method.

[0019] Through the above technical scheme, the technical scheme includes: obtaining the original waveform data corresponding to the target depth interval; performing singular value decomposition processing on the original waveform data to obtain the first waveform data, reconstructing the first waveform data based on the target singular value greater than the preset value to obtain the second waveform data; performing denoising processing on the second waveform data to obtain the target waveform data corresponding to the target depth interval; and characterizing the fracture structure based on the target waveform data. Based on the scheme provided in the embodiment of the present application, it is possible to improve the capture and utilization of weak reflection signals outside the well, so that while effectively removing complex noise interference, the detail information is better retained, so that the local features and edge contours of the fracture structure outside the well can be enhanced in the imaging, that is, the fracture structure outside the well can be more accurately characterized.

[0020] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 A schematic diagram of a process flow of a fracture structure characterization method according to an embodiment of the present application is schematically shown; Figure 2 A schematic diagram of singular value decomposition according to an embodiment of the present application is shown schematically; Figure 3A schematic diagram of a process flow of another fracture structure characterization method according to an embodiment of the present application is shown; Figure 4-1 The schematic diagram shows the out-of-hole imaging results based on the original waveform data of acoustic wave remote detection; Figure 4-2 The schematic diagram shows the out-of-hole imaging result of the original waveform data of acoustic wave remote detection after singular value decomposition, waveform reconstruction and window function denoising; Figure 4-3 The schematic diagram shows the out-of-hole imaging result of the original waveform data of acoustic wave remote detection after singular value decomposition processing, waveform reconstruction, window function denoising processing and imaging enhancement processing; Figure 5 A structural block diagram of a fracture structure characterization device according to an embodiment of the present application is schematically shown; Figure 6 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown.

[0022] Description of Reference Numerals 210 - acquisition module; 220 - reconstruction module; 230 - denoising module; 240 - imaging module; A01 - processor; A02 - network interface; A03 - internal memory; A04 - display screen; A05 - input device; A06 - non-volatile storage medium; B01 - operating system; B02 - computer program. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0024] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0025] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0026] As described in the background technology, acoustic wave remote detection technology has been applied to the identification and characterization of fractures outside the well. In practical applications, due to the influence of strong signals such as direct waves and bending waves in the wellbore, the effective signals of acoustic wave remote detection are "submerged" in complex noise, which seriously affects the extraction of weak reflection waveform signals outside the wellbore, and the characterization of fracture structures outside the wellbore is not accurate, especially for large geological structures. At present, acoustic wave remote detection technology mostly adopts processing steps such as raw data preprocessing, wellbore direct wave suppression, offset imaging, imaging filtering, and imaging quality monitoring to enhance the identification and extraction of effective reflection signals. For example, Tang et al. proposed to use bandpass filtering to eliminate low-frequency interference in dipole data, and after wave field separation of uplink and downlink waveform signals, the imaging results of the reflection interface were obtained by offset. Wei Zhoutuo et al. used the prestack fk migration method to obtain the geological interface beside the well for the full-wave waveform data of array acoustic logging, and then determined the position, inclination and extension of the formation interface beside the well. Che Xiaohua et al. used the received multi-mode scattered wave signals for scanning inversion and imaging. However, the above processing method has boundary effects and lacks multi-scale characteristics. While suppressing noise, it will also blur and destroy some detailed information, such as the edge information of the off-hole fracture structure, resulting in unsatisfactory accuracy in the characterization of the off-hole fracture structure.

[0027] In view of this, a fracture structure characterization method is provided in one embodiment of the present application, such as Figure 1 As shown, the fracture structure characterization method may include the following steps: Step 101: Obtain original waveform data corresponding to a target depth interval.

[0028] The target depth interval may be a sub-depth interval in the first depth interval, that is, the first depth interval includes the target depth interval.

[0029] Then, before obtaining the raw waveform data corresponding to the target depth interval, the raw waveform data corresponding to the first depth interval may be obtained first, and then the raw waveform data corresponding to the target depth interval may be obtained from the raw waveform data corresponding to the first depth interval. In a specific implementation, after obtaining the raw waveform data corresponding to the first depth interval, the first depth interval may be divided into a plurality of target depth intervals, a target depth interval may be selected from each target depth interval, and then the raw waveform data corresponding to the target depth interval may be obtained.

[0030] The original waveform data may be acoustic wave long-range detection original waveform data, and the acoustic wave long-range detection original waveform data may be specifically acquired by an array acoustic wave logging instrument.

[0031] Step 102, performing singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstructing the first waveform data based on a target singular value greater than a preset value to obtain second waveform data.

[0032] In practical applications, in order to prevent abnormal data in the original waveform data from affecting the singular value decomposition processing, the original waveform data may be subjected to imaging preprocessing before the singular value decomposition processing is performed. Furthermore, step 102 of performing singular value decomposition processing on the original waveform data may include: performing singular value decomposition processing on the original waveform data that has undergone imaging preprocessing.

[0033] The imaging preprocessing may include filtering, attenuation correction, etc. Among them, filtering may include median filtering, smoothing filtering, wavelet filtering, etc. Since sound waves are attenuated during propagation, the signal far from the emission source is weaker than the signal near, and attenuation correction can make the signal strength more uniform.

[0034] In addition, to facilitate the singular value decomposition processing of the original waveform data, after obtaining the original waveform data, the original waveform data can also be split to obtain dipole four-component data, and then step 102 performs singular value decomposition processing on the original waveform data, which can include: performing singular value decomposition processing on the dipole four-component data in the original waveform data.

[0035] In the embodiment of the present application, the dipole four-component data in the original waveform data is waveform matrix data, and the waveform matrix data can be subjected to singular value decomposition based on the following formula (1): (1);

[0036] In the above formula (1), is an m×n matrix data corresponding to the waveform matrix data; is an m×m square matrix, also known as the left singular vector; is an n×n square matrix, also known as the right singular vector; is an m×n matrix, in which all elements except the diagonal elements are 0, and the elements on the diagonal are singular values. The waveform matrix data includes a plurality of sub-matrix data, It can be regarded as a sub-matrix data in the waveform matrix data. For each sub-matrix data in the waveform matrix data, singular value decomposition can be performed based on the above formula (1).

[0037] The above formula (1) can also be expressed as the following formula (2): (2).

[0038] Since the signal correlation degree corresponding to the front singular values ​​in the singular value decomposition is relatively strong, such as the front 10% singular values ​​in the singular value decomposition, the front 1% singular values ​​in the singular value decomposition, etc. Therefore, the first waveform data can be reconstructed based on the target singular value greater than the preset value to obtain the second waveform data. The preset value can be set according to actual needs.

[0039] Wherein, based on the target singular value greater than the preset value, the first waveform data is reconstructed based on the following formula (3): (3);

[0040] In the above formula (3), is an m×n matrix data corresponding to the first waveform data, is an m×r matrix data, is an r×r matrix data, is an r×n matrix data, where r is less than m and r is less than n; The r singular values ​​in are the target singular values. The first waveform data includes a plurality of sub-matrix data, It can be regarded as a sub-matrix data in the first waveform data. Each sub-matrix data in the first waveform data can be reconstructed based on the above formula (3). The singular value decomposition diagram corresponding to formula (3) can be as follows: Figure 2 shown.

[0041] It can be understood that the above formula (3) uses the first r largest singular values ​​in the singular value decomposition to approximate the reconstruction of the imaging result, which can reduce the noise level and redundant information of the image. In practical applications, r is preferably much smaller than m and n, and the closer it is to n, the closer the result of matrix multiplication is to , thus the reconstruction effect is better.

[0042] Step 103: denoising the second waveform data to obtain target waveform data corresponding to the target depth interval.

[0043] In specific implementation, step 103 may include step 1 and step 2, which are as follows: Step 1: Perform a first denoising process on the second waveform data using a window function to obtain third waveform data.

[0044] Among them, a specific window function can be set according to actual needs, and the upper and lower limits of the frequency can be set so that the window function allows the waveform signal at the specified frequency to pass. Thereby, the signals of other frequencies can be effectively suppressed to achieve the filtering effect of smoothing and denoising. Through this digital filtering method, the generation of pseudo contours in the final imaging can be suppressed. The window function can include a rectangular window, a Hanning window, a Gaussian window, etc.

[0045] In a specific implementation, the first denoising process is performed on the second waveform data using a window function, which can be performed based on the following formula (4): (4);

[0046] Wherein, g(x) is the third waveform data, is the second waveform data, ω 1 is the upper frequency bound, ω 2 is the lower frequency limit, and K is the transition band width.

[0047] Step 2: Perform imaging enhancement processing on the third waveform data to obtain target waveform data corresponding to the target depth interval.

[0048] The imaging enhancement processing of the third waveform data may include: identifying noise signals from the third waveform data and reducing the weight of the noise signals, so that the edge contour of the geological structure can be highlighted in the final imaging, and the fracture structure can be depicted more accurately and meticulously, which is convenient for further picking up fracture occurrence information such as strike, dip and dip angle.

[0049] Step 104, characterizing the fracture structure based on the target waveform data.

[0050] Another more specific flow chart of the fracture structure characterization method provided in the embodiment of the present application can be as follows: Figure 3 As shown, each step can refer to the aforementioned content and will not be repeated here.

[0051] In practical applications, after obtaining the target waveform data corresponding to the target depth interval, the next target depth interval can be obtained from the first depth interval, so as to perform steps 101 to 103 on the raw waveform data corresponding to the next target depth interval to obtain the target waveform data corresponding to the next target depth interval, and so on, the same processing is performed on other target depth intervals in the first depth interval in sequence. That is, the raw waveform data corresponding to each target depth interval in the first depth interval is sequentially performed on steps 101 to 103 to obtain the target waveform data corresponding to each target depth interval.

[0052] Then, step 104 of performing fracture structure characterization based on the target waveform data may include: performing fracture structure characterization based on the target waveform data corresponding to each target depth interval, thereby obtaining an imaging result corresponding to the first depth interval.

[0053] It can be understood that the fracture structure characterization method provided by the embodiment of the present application includes: obtaining the original waveform data corresponding to the target depth interval; performing singular value decomposition processing on the original waveform data to obtain the first waveform data, reconstructing the first waveform data based on the target singular value greater than the preset value to obtain the second waveform data; performing denoising processing on the second waveform data to obtain the target waveform data corresponding to the target depth interval; and characterizing the fracture structure based on the target waveform data. Based on the scheme provided by the embodiment of the present application, the capture and utilization of the weak reflection signal outside the well can be improved, so that the detail information, such as local features and edge information, can be better retained while effectively removing the complex noise interference, so that the local features and edge contours of the fracture structure outside the well can be enhanced in the imaging, that is, the fracture structure outside the well can be more accurately characterized and the imaging quality can be higher. As a result, in the imaging, the morphology of the fracture outside the well is clearer, and the position of the fracture outside the well is more accurate, which can provide effective guidance for the exploration and development of fracture-type oil and gas reservoirs.

[0054] The effects of the fracture structure characterization method provided in the embodiment of the present application are described below with reference to specific examples. Figure 4-1 This is the out-of-hole imaging result based on the original waveform data of acoustic remote detection. Figure 4-2 The original waveform data of acoustic wave remote detection is processed by singular value decomposition, waveform reconstruction and window function denoising to obtain the out-of-hole imaging result; Figure 4-3 The original waveform data of acoustic wave long-range detection is processed by singular value decomposition, waveform reconstruction, window function denoising and imaging enhancement to obtain the out-of-hole imaging result.

[0055] from Figure 4-1It can be seen from the figure that in the off-hole imaging results based on the original waveform data of acoustic wave remote detection, the noise near the wellbore section is obvious, which has seriously affected the identification and characterization of the characteristics and morphology of the fracture structure. Figure 4-2 It can be seen that after singular value decomposition, waveform reconstruction and window function denoising, compared with Figure 4-1 The reconstructed imaging results clearly show multiple cross-well fractures. The imaging results effectively retain the morphological characteristics of multi-scale fracture structures in the depth range of 2560m-2640m, while removing invalid reflection waves and background noise, improving the imaging effect of large-scale geological bodies. Figure 4-3 It can be seen that after singular value decomposition, waveform reconstruction, window function denoising and imaging enhancement, compared with Figure 4-2 , the edge contour of the through-well fracture zone in the depth range of 2560m-2640m is more prominent, interference and noise are further eliminated and suppressed, and the fracture imaging can describe the fracture dip, azimuth, length, distance from the well and the number of fractures. In the figure, 3 through-well fractures with a height of about 10m are clearly visible at 2580m-2620m. There are also secondary fractures with weak reflection signal amplitude near the through-well fractures. Small fractures are also developed in other depth ranges.

[0056] It can be seen that based on the fracture structure characterization method provided in the embodiment of the present application, the local features and edge information in the final imaging result are retained and enhanced, and the interference signal is also effectively suppressed, which greatly improves the accuracy of the fracture structure characterization and the imaging quality.

[0057] Figure 1 FIG. 1 is a flow chart of a fracture structure characterization method. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0058] Based on the same inventive concept, Figure 5 As shown, Figure 5The structural block diagram of a fracture structure characterization device according to an embodiment of the present application is schematically shown. In one embodiment, a fracture structure characterization device 200 is provided, comprising an acquisition module 210, a reconstruction module 220, a denoising module 230 and an imaging module 240, wherein: An acquisition module 210 is used to acquire original waveform data corresponding to a target depth interval; A reconstruction module 220, configured to perform singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstruct the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; A denoising module 230, configured to perform denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; The imaging module 240 is used to characterize the fracture structure based on the target waveform data.

[0059] It can be understood that the fracture structure characterization device provided in the embodiment of the present application can improve the capture and utilization of weak reflection signals outside the well, so that while effectively removing complex noise interference, detail information such as local features and edge information can be better retained, so that the local features and edge contours of the fracture structure outside the well can be enhanced in imaging, that is, the fracture structure outside the well can be more accurately characterized and the imaging quality can be higher. As a result, in the imaging, the morphology of the fracture outside the well is clearer, and the location of the fracture outside the well is more accurate, which can provide effective guidance for the exploration and development of fracture-type oil and gas reservoirs.

[0060] In one embodiment, the fracture structure characterization device also includes an imaging preprocessing module for performing imaging preprocessing on the original waveform data before performing singular value decomposition processing on the original waveform data; the reconstruction module is used to perform singular value decomposition processing on the original waveform data that has undergone imaging preprocessing.

[0061] In one implementation, the reconstruction module is used to reconstruct the first waveform data based on the following formula: ;

[0062] in, is an m×n matrix data corresponding to the first waveform data, is an m×r matrix data, is an r×r matrix data, is an r×n matrix data, r is less than m, and r is less than n;

[0063] The r singular values ​​in are the target singular values.

[0064] In one embodiment, the denoising module is used to perform a first denoising process on the second waveform data using a window function to obtain third waveform data; and perform imaging enhancement processing on the third waveform data to obtain target waveform data corresponding to the target depth interval.

[0065] In one embodiment, the denoising module is used to perform a first denoising process on the second waveform data based on the following formula; ;

[0066] Wherein, g(x) is the third waveform data, is the second waveform data, ω 1 is the upper frequency bound, ω 2 is the lower frequency limit, and K is the transition band width.

[0067] In one embodiment, the acquisition module is used to acquire original waveform data corresponding to a first depth interval before acquiring original waveform data corresponding to a target depth interval; and to acquire original waveform data corresponding to the target depth interval from original waveform data corresponding to the first depth interval.

[0068] In one embodiment, the first depth interval includes a plurality of target depth intervals, and the imaging module is used to characterize the fracture structure based on target waveform data corresponding to each target depth interval.

[0069] The fracture structure characterization device includes a processor and a memory. The acquisition module 210, reconstruction module 220, denoising module 230 and imaging module 240 are all stored in the memory as program units, and the processor executes the above program modules stored in the memory to implement corresponding functions.

[0070] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to achieve fast and efficient prediction of sand body distribution at the full chip scale.

[0071] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0072] An embodiment of the present application provides a machine-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for characterizing a fracture structure.

[0073] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a fracture structure characterization method is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0074] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] In one embodiment, the fracture structure characterization device provided in the present application can be implemented in the form of a computer program. The computer program can be Figure 6 The computer device shown in the figure is run on the computer device. The memory of the computer device can store various program modules that constitute the construction task intelligent scheduling device, such as: Figure 5 The acquisition module 210, reconstruction module 220, denoising module 230 and imaging module 240 are shown. The computer program composed of various program modules enables the processor to execute the steps of the fracture structure characterization method of each embodiment of the present application described in this specification.

[0076] Figure 6 The computer device shown can be Figure 5 The acquisition module 210, reconstruction module 220, denoising module 230 and imaging module 240 in the fracture structure characterization device shown execute the method.

[0077] The embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: Obtaining the original waveform data corresponding to the target depth interval; Performing singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstructing the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; Performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; The fracture structure is characterized based on the target waveform data.

[0078] In one embodiment, before performing singular value decomposition processing on the original waveform data, the method further includes: performing imaging preprocessing on the original waveform data; The performing singular value decomposition processing on the original waveform data comprises: Perform singular value decomposition on the original waveform data that has undergone imaging preprocessing.

[0079] In one embodiment, the first waveform data is reconstructed based on a target singular value greater than a preset value, based on the following formula: ;

[0080] in, is an m×n matrix data corresponding to the first waveform data, is an m×r matrix data, is an r×r matrix data, is an r×n matrix data, r is less than m, and r is less than n;

[0081] The r singular values ​​in are the target singular values.

[0082] In one embodiment, the denoising process is performed on the second waveform data to obtain the target waveform data corresponding to the target depth interval, including: Performing a first denoising process on the second waveform data using a window function to obtain third waveform data; The third waveform data is subjected to imaging enhancement processing to obtain target waveform data corresponding to the target depth interval.

[0083] In one embodiment, the first denoising process is performed on the second waveform data using a window function based on the following formula: ;

[0084] Wherein, g(x) is the third waveform data, is the second waveform data, ω 1 is the upper frequency bound, ω 2 is the lower frequency limit, and K is the transition band width.

[0085] In one embodiment, before obtaining the original waveform data corresponding to the target depth interval, the method further includes: Acquire original waveform data corresponding to the first depth interval; The original waveform data corresponding to the target depth interval is obtained from the original waveform data corresponding to the first depth interval.

[0086] In one embodiment, the first depth interval includes a plurality of target depth intervals. The characterization of the fracture structure based on the target waveform data comprises: The fracture structure is characterized based on the target waveform data corresponding to each target depth interval.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0093] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0095] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for characterizing a fracture structure, characterized in that: The method comprises: Obtaining the original waveform data corresponding to the target depth interval; Performing singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstructing the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; Performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; The fracture structure is characterized based on the target waveform data.

2. The method for characterizing a fracture structure according to claim 1, characterized in that: Before performing singular value decomposition processing on the original waveform data, the method further includes: performing imaging preprocessing on the original waveform data; The performing singular value decomposition processing on the original waveform data comprises: Perform singular value decomposition on the original waveform data that has undergone imaging preprocessing.

3. The fracture structure characterization method according to claim 1, characterized in that: The first waveform data is reconstructed based on a target singular value greater than a preset value, based on the following formula: ； in, is an m×n matrix data corresponding to the first waveform data, is an m×r matrix data, is an r×r matrix data, is an r×n matrix data, r is less than m, and r is less than n; The r singular values ​​in are the target singular values.

4. The fracture structure characterization method according to claim 1, characterized in that: The denoising process is performed on the second waveform data to obtain target waveform data corresponding to the target depth interval, including: Performing a first denoising process on the second waveform data using a window function to obtain third waveform data; The third waveform data is subjected to imaging enhancement processing to obtain target waveform data corresponding to the target depth interval.

5. The fracture structure characterization method according to claim 4, characterized in that: The first denoising process is performed on the second waveform data using a window function based on the following formula: ； Wherein, g(x) is the third waveform data, is the second waveform data, ω1 is the upper frequency limit, ω2 is the lower frequency limit, and K is the transition band width.

6. The fracture structure characterization method according to claim 1, characterized in that: Before obtaining the original waveform data corresponding to the target depth interval, the method further includes: Acquire original waveform data corresponding to the first depth interval; The original waveform data corresponding to the target depth interval is obtained from the original waveform data corresponding to the first depth interval.

7. The fracture structure characterization method according to claim 6, characterized in that: The first depth interval includes a plurality of target depth intervals, The characterization of the fracture structure based on the target waveform data comprises: The fracture structure is characterized based on the target waveform data corresponding to each target depth interval.

8. A fracture structure characterization device, characterized in that: include: An acquisition module is used to acquire the original waveform data corresponding to the target depth interval; A reconstruction module, configured to perform singular value decomposition processing on the original waveform data to obtain first waveform data, and reconstruct the first waveform data based on a target singular value greater than a preset value to obtain second waveform data; A denoising module, used for performing denoising processing on the second waveform data to obtain target waveform data corresponding to the target depth interval; An imaging module is used to characterize the fracture structure based on the target waveform data.

9. A processor, characterized in that: The method is configured to execute the fracture structure characterization method according to any one of claims 1 to 7.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the fracture structure characterization method according to any one of claims 1 to 7.

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