Formation fracturing detection method, device, processor and storage medium

Abstract

The application provides a formation fracturing detection method and device, a storage medium and a processor, and belongs to the technical field of well logging. The method obtains a frequency spectrum array of original array waveform data, calculates slowness values of each acoustic mode, then obtains a frequency dispersion curve greater than a preset resolution according to iterative operation of hierarchical clustering and threshold division, projects the time difference and the frequency of the frequency dispersion curve to a time difference axis by using a time difference-frequency projection method to obtain a time difference-frequency projection curve after fracturing, overlaps the time difference-frequency projection curve after fracturing with a time difference-frequency projection curve before fracturing to obtain a time difference change value at the lowest frequency, and finally determines the degree of formation fracturing according to the time difference change value at the lowest frequency to obtain a formation fracturing detection result, so that the change of formation shear wave velocity caused by fracturing and the degree of formation fracturing can be accurately distinguished.

Description

Technical Field

[0001] This invention relates to the field of well logging technology, and more specifically to a formation fracturing detection method, a formation fracturing detection device, a machine-readable storage medium, and a processor. Background Technology

[0002] Fracturing is an effective means of increasing production in tight reservoirs and a crucial step in subsequent oil and gas field exploration and development. Therefore, accurately characterizing the fracturing effect on the formation is of paramount importance. Currently, the main methods for evaluating fracturing effects include:

[0003] (i) The hydraulic fracturing effect evaluation method based on orthogonal dipole anisotropy has many limitations. There are many influencing factors and the method is singular, which cannot accurately characterize the fracturing effect of the formation.

[0004] (ii) Dispersion analysis methods (such as the Prony method and the weighted spectral coherence method) can effectively calculate the propagation speed and attenuation of different acoustic wave modes in the well. However, since this method is susceptible to noise interference and the algorithm itself has poor stability, the resolution and stability of its data processing results cannot be guaranteed.

[0005] (III) Clustering algorithm. Although this method has a wide range of applications in evaluating fracturing effects and processing well logging data, it lacks practicality and reliability in field measured data for complex heterogeneous reservoirs after hydraulic fracturing. None of the three commonly used methods can accurately describe the formation modification effect after fracturing operations. Summary of the Invention

[0006] The purpose of this invention is to provide a formation fracturing detection method to solve the problems of insufficient resolution and low stability in existing unconventional oil and gas formation fracturing detection methods.

[0007] To achieve the above objectives, a first aspect of the present invention provides a method for detecting formation fracturing, the method comprising:

[0008] The original array waveform data is acquired and preprocessed to obtain the spectrum array;

[0009] The slowness values ​​of each acoustic mode are calculated using a spectral array, and effective dispersion curves are obtained using hierarchical clustering and threshold partitioning methods.

[0010] The time difference-frequency projection method is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis to obtain the time difference-frequency projection curve after fracturing;

[0011] Obtain the time difference-frequency projection curve before fracturing, and overlay the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency.

[0012] The degree of formation fracturing is determined by the time difference change value at the lowest frequency, and the formation fracturing detection results are obtained.

[0013] Optionally, the preprocessing of the original array waveform data to obtain the spectrum array includes:

[0014] The original array waveform data is depth-corrected, and the depth-corrected original array waveform data is then stitched together to obtain the effective array waveform data.

[0015] Perform a Fourier transform on the effective array waveform data to obtain the spectrum array.

[0016] Optionally, the matrix bundle formed by the original array waveform data is specifically Y2-zY1:

[0017] in,

[0018]

[0019]

[0020]

[0021] Z0 = diag[z1 z2 … z] p ]

[0022] B = diag[b1 b2 … b] p ]

[0023] i = 1, 2, ..., p

[0024] In the formula, z i The term is a complex exponential term, where p represents the number of acoustic wave modes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0025] Optionally, the slowness value can be calculated using the following formula:

[0026]

[0027] k i =arctan[Im(z i ) / Re(z i ) / 2πd]i=1,2…p.

[0028] In the formula, s i k represents the slowness value of the i-th sound wave mode. i Let ω represent the wave number of the i-th sound wave mode, and z represent the angular frequency.i It is a complex exponential term.

[0029] Optionally, the step of calculating the slowness value of each sound wave mode through a spectral array and obtaining the effective dispersion curve using hierarchical clustering and threshold partitioning methods includes:

[0030] Hierarchical clustering algorithm is used to repeatedly cluster the slowness values ​​of each acoustic mode to obtain effective slowness scatter points;

[0031] The effective slowness scatter points are divided based on the threshold division method, and the dispersion curves with a resolution greater than the preset resolution are taken as the effective dispersion curves.

[0032] Optionally, the hierarchical clustering algorithm is used to repeatedly perform hierarchical clustering on the slowness values ​​of each sound wave mode, specifically as follows:

[0033]

[0034] In the formula, ε represents the neighborhood radius, MinPts represents the minimum number of numerical points within the neighborhood radius, c represents the effective slowness scatter points, and d represents the noise scatter points.

[0035] Optionally, determining the formation fracturing degree based on the time difference change value at the lowest frequency and obtaining the formation fracturing detection results includes:

[0036] The degree of formation fracturing is determined based on the lowest frequency variation value;

[0037] The formation fracturing level is determined based on the degree of formation fracturing, and the formation fracturing level is used as the formation fracturing detection result.

[0038] A second aspect of the present invention provides a formation fracturing detection device, the device comprising:

[0039] The raw data processing module is used to acquire raw array waveform data and preprocess the raw array waveform data to obtain a spectrum array;

[0040] The spectrum array processing module is used to calculate the slowness value of each sound wave mode through the spectrum array, and to obtain the effective dispersion curve by using hierarchical clustering and threshold partitioning methods.

[0041] The effective dispersion curve processing module is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis using the time difference-frequency projection method to obtain the time difference-frequency projection curve after fracturing.

[0042] The time difference change value calculation module is used to obtain the time difference-frequency projection curve before fracturing, and to overlay the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency.

[0043] The fracturing evaluation result acquisition module is used to determine the degree of formation fracturing based on the time difference change value at the lowest frequency and to acquire the formation fracturing detection results.

[0044] A third aspect of the present invention provides a processor configured to perform the above-described formation fracturing detection method.

[0045] A fourth aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the above-described formation fracturing detection method.

[0046] This invention provides a method, apparatus, processor, and storage medium for detecting formation fracturing. The method preprocesses the acquired raw array waveform data to obtain a spectral array. Then, it calculates the slowness value of each acoustic mode using the spectral array and obtains the effective dispersion curve using hierarchical clustering and thresholding. Next, it projects the time difference and frequency of the effective dispersion curve onto the time difference axis using a time-difference-frequency projection method to obtain the post-fracturing time-difference-frequency projection curve. The pre-fracturing and post-fracturing time-difference-frequency projection curves are overlaid to obtain the time difference change value at the lowest frequency. Finally, the degree of formation fracturing is determined based on the time difference change value at the lowest frequency, and the formation fracturing detection results are obtained. This effectively improves the accuracy of dipole flexural wave dispersion processing and the reliability of SFA analysis results, and can accurately distinguish the changes in formation shear wave velocity caused by fracturing and the degree of formation fracturing.

[0047] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 This is a flowchart of a formation fracturing detection method provided in one embodiment of the present invention;

[0050] Figure 2a This is a diagram showing the dispersion curve extraction result after standardization processing of the original array waveform data provided in one embodiment of the present invention.

[0051] Figure 2b This is a graph showing the dispersion curve processing result after hierarchical clustering standardization provided by one embodiment of the present invention;

[0052] Figure 3 This is a comparison chart of array acoustic wave SFA results before and after a secondary fracturing well operation provided by one embodiment of the present invention;

[0053] Figure 4 This is a block diagram of a formation fracturing detection device provided in one embodiment of the present invention. Detailed Implementation

[0054] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0055] Figure 1 This is a flowchart of a formation fracturing detection method provided in one embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a formation fracturing detection method, the method comprising:

[0056] S10: Acquire the original array waveform data and preprocess the original array waveform data to obtain the spectrum array.

[0057] Here, the original array waveform data refers to the full-wave array data of acoustic waves excited by monopoles and dipoles within the target depth range acquired during array acoustic logging. The array acoustic logging techniques used in this embodiment include, but are not limited to, digital acoustic logging and array acoustic logging.

[0058] Specifically, after acquiring the raw array waveform data, depth correction and curve stitching are required to obtain valid array waveform data. Depth correction involves correcting all logging curves to a completely consistent depth correspondence to meet the stringent depth requirements of data processing. Curve stitching involves removing well sections where the instrument encounters obstacles or jams, stitching together data from multiple measurements, and finally forming a complete logging curve.

[0059] Generally, if the collected data is waveform data of a deviated well array, the depth of the deviated well needs to be corrected to the depth of the vertical well to obtain the true depth and thickness of the formation.

[0060] Furthermore, in this embodiment, N receivers are set to receive array acoustic waveform data. The received array acoustic waveform data, after being subjected to Fourier transform, yields the following spectrum data:

[0061]

[0062] In the formula, ω0 represents the angular frequency, and k represents the space wavenumber; b i For a complex number, z i The term is a complex exponential term, where p represents the number of acoustic wave modes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0063] Construct a parent Hankel matrix:

[0064]

[0065] Extract the first Np-1 columns of the mother Hankel matrix into two matrices, Y1 and Y2, to obtain the matrix bundle Y2-zY1.

[0066] in,

[0067]

[0068]

[0069]

[0070] Z0 = diag[z1 z2 … z] p ]

[0071] B = diag[b1 b2 … b] p ]

[0072] i = 1, 2, ..., p

[0073] In the formula, z i The term is a complex exponential term, where p represents the number of acoustic wave modes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0074] S20: Calculate the slowness value of each sound wave mode using a spectrum array, and obtain the effective dispersion curve using hierarchical clustering and threshold partitioning methods.

[0075] Specifically, the formula for calculating the slowness value is:

[0076]

[0077] k i =arctan[Im(z i ) / Re(z i ) / 2πd]i=1,2…p.

[0078] In the formula, s i k represents the slowness value of the i-th sound wave mode. i Let ω represent the wave number of the i-th sound wave mode, and z represent the frequency. i It is a complex exponential term.

[0079] After obtaining the slowness values ​​of each acoustic wave mode, a hierarchical clustering algorithm is used to repeatedly cluster the slowness values ​​of each acoustic wave mode to obtain effective slowness scatter points. During the hierarchical clustering process, the retrieved acoustic wave amplitude and slowness are considered simultaneously, and the dispersion characteristics of the formation shear waves are gradually distinguished and extracted to obtain effective slowness scatter points and noise scatter points. Here, effective slowness scatter points refer to points of the fundamental order in the waveform data, and noise scatter points refer to points of non-fundamental order in the waveform data.

[0080] The specific execution process of the above hierarchical clustering algorithm is as follows:

[0081]

[0082] In the formula, ε represents the neighborhood radius, MinPts represents the minimum number of numerical points within the neighborhood radius, c represents the effective slowness scatter points, and d represents the noise scatter points.

[0083] The hierarchical clustering algorithm in this embodiment includes, but is not limited to, the DBSCAN algorithm.

[0084] Furthermore, to improve data processing efficiency, this embodiment also requires standardization of the effective dispersion curve to transform data from different data ranges into a unified standard data range.

[0085] Figure 2a and Figure 2b The images show the dispersion curve extraction results after standardization of the original array waveform data and after hierarchical clustering standardization. The results show that the dispersion curve after hierarchical clustering can automatically classify effective slow-speed scatter points and noise scatter points, achieving high-precision extraction of the dispersion curve of the array waveform data.

[0086] After obtaining the effective slowness scatter points, the effective slowness scatter points are divided based on the threshold division method, and the dispersion curves with a resolution greater than the preset resolution are taken as the effective dispersion curves.

[0087] S30: The time difference and frequency of the effective dispersion curve are projected onto the time difference axis using the time difference-frequency projection method to obtain the time difference-frequency projection curve after fracturing.

[0088] After obtaining the effective dispersion curve, the time difference and frequency of the effective dispersion curve are projected onto the time difference axis using the time difference-frequency projection method (SFA) to obtain the time difference-frequency projection curve after fracturing.

[0089] S40: Obtain the time difference-frequency projection curve before fracturing, and overlap the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency.

[0090] S50: Determine the degree of formation fracturing based on the time difference change value at the lowest frequency, and obtain the formation fracturing detection results.

[0091] Specifically, after obtaining the time difference change value at the lowest frequency, the degree of formation fracturing is determined based on the time difference change value at the lowest frequency. In this embodiment, the method for determining the degree of formation fracturing based on the time difference change value at the lowest frequency includes, but is not limited to, querying a formation fracturing degree table and obtaining the mapping relationship between time difference change value and formation fracturing degree.

[0092] After obtaining the degree of formation fracturing, the formation fracturing grade is determined based on the degree of formation fracturing, and the formation fracturing grade is used as the formation fracturing detection result.

[0093] Figure 3 This is an application example of the formation fracturing detection method in a secondary hydraulically fractured well. From left to right, the first figure is a curve of natural gamma ray and well diameter; the second figure is a comparison of SFA results after the open hole well and the first hydraulic fracturing operation; the third figure is a comparison of SFA results after the first and second hydraulic fracturing operations; and the fourth figure is the production conclusion, which includes: TCMR, total porosity; CMRP_3MS, effective porosity with a cutoff value of 3ms; and CMFF, nuclear magnetic resonance free fluid volume.

[0094] In the second and third figures, the blue scatter dots represent the SFA analysis results after the first fracturing operation, the gray scatter dots in the second figure represent the SFA analysis results of the open-hole well, and the orange scatter dots in the third figure represent the SFA analysis results after the second fracturing operation. Comparing the processing results before and after fracturing operations in the target layer X535-X550m, it can be seen that the SFA distribution and morphological characteristics of the open-hole well and the first fracturing operation are almost identical, indicating that the first fracturing operation was ineffective and did not form a large-scale complex network of fractures. However, the SFA distribution characteristics after the second fracturing operation showed a significant change, indicating a significant formation fracturing effect in the target depth range. Furthermore, the increase in the time difference distribution range is directly proportional to the degree of fracturing, which is consistent with the production conclusions.

[0095] This formation fracturing detection method utilizes array waveform data, combining a dispersion analysis algorithm based on linear prediction theory with hierarchical clustering. During density clustering, it simultaneously considers the inverted acoustic amplitude and slowness, minimizing human intervention and increasing automation. This effectively solves the problem of traditional array acoustic logging failing to accurately obtain reservoir characteristics. It achieves effective extraction of dispersion curves for slowness scatter points within three iterations, significantly improving the accuracy of dipole flexural wave dispersion processing and enhancing the reliability of SFA analysis results. This method can quickly and accurately distinguish the degree of formation fracturing caused by fracturing, further expanding the application scope of acoustic logging in formation fracturing detection.

[0096] Figure 4 This is a block diagram of a formation fracturing detection device provided in one embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides a formation fracturing detection device, which includes a raw data processing module 10, a spectrum array processing module 20, an effective dispersion curve processing module 30, a time difference change value calculation module 40, and a fracturing evaluation result acquisition module 50.

[0097] The raw data processing module 10 is used to acquire raw array waveform data and preprocess the raw array waveform data to obtain a spectrum array;

[0098] The spectrum array processing module 20 is used to calculate the slowness value of each sound wave mode through the spectrum array, and to obtain the effective dispersion curve by using hierarchical clustering and threshold partitioning methods.

[0099] The effective dispersion curve processing module 30 is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis using the time difference-frequency projection method to obtain the time difference-frequency projection curve after fracturing.

[0100] The time difference change value calculation module 40 is used to obtain the time difference-frequency projection curve before fracturing, and to overlay the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency.

[0101] The fracturing evaluation result acquisition module 50 is used to determine the degree of formation fracturing based on the time difference change value at the lowest frequency and to acquire the formation fracturing detection results.

[0102] The present invention also provides a machine-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described formation fracturing detection method.

[0103] The present invention also provides a machine-readable storage medium storing instructions that, when executed by an electronic speed controller, enable the processor to perform the aforementioned formation fracturing detection method.

[0104] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0105] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0106] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for detecting formation fracturing, characterized in that, The method includes: The original array waveform data is acquired and preprocessed to obtain the spectrum array. The slowness values ​​of each acoustic mode are calculated using a spectral array, and effective dispersion curves are obtained using hierarchical clustering and threshold partitioning methods. The time difference-frequency projection method is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis to obtain the time difference-frequency projection curve after fracturing; Obtain the time difference-frequency projection curve before fracturing, and overlay the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency. The degree of formation fracturing is determined by the time difference change value at the lowest frequency, and the formation fracturing detection results are obtained.

2. The formation fracturing detection method according to claim 1, characterized in that, The preprocessing of the original array waveform data to obtain the spectrum array includes: The original array waveform data is depth-corrected, and the depth-corrected original array waveform data is then stitched together to obtain the effective array waveform data. Perform a Fourier transform on the effective array waveform data to obtain the spectrum array.

3. The formation fracturing detection method according to claim 1, characterized in that, The matrix bundle formed by the original array waveform data is specifically as follows: ; in, In the formula, For a complex exponential term, This indicates the number of acoustic wave modes in the original array waveform. Indicates the number of receivers that receive the original array waveform. It is a complex number.

4. The formation fracturing detection method according to claim 1, characterized in that, The formula for calculating the slowness value is: In the formula, Indicates the first The slowness value of each sound wave mode. Indicates the first The wave number of each sound wave mode. Represents angular frequency. For a complex exponential term, Represents noise scatter points, This indicates the number of acoustic wave modes in the original array waveform.

5. The formation fracturing detection method according to claim 1, characterized in that, The process of calculating the slowness value of each acoustic mode using a spectral array and obtaining the effective dispersion curve using hierarchical clustering and threshold partitioning includes: Hierarchical clustering algorithm is used to repeatedly cluster the slowness values ​​of each acoustic mode to obtain effective slowness scatter points; The effective slowness scatter points are divided based on the threshold division method, and the dispersion curves with a resolution greater than the preset resolution are taken as the effective dispersion curves.

6. The formation fracturing detection method according to claim 5, characterized in that, The specific steps of repeatedly performing hierarchical clustering on the slowness values ​​of each sound wave mode using a hierarchical clustering algorithm are as follows: In the formula, Represents the neighborhood radius. This represents the point with the fewest values ​​within its neighborhood radius. Indicates the effective slowness scatter plot, This represents noise scatter points.

7. The formation fracturing detection method according to claim 1, characterized in that, The process of determining the degree of formation fracturing based on the time difference change value at the lowest frequency and obtaining formation fracturing detection results includes: Determine the degree of formation fracturing based on the time difference variation value at the lowest frequency; The formation fracturing level is determined based on the degree of formation fracturing, and the formation fracturing level is used as the formation fracturing detection result.

8. A formation fracturing detection device, characterized in that, The device includes: The raw data processing module is used to acquire raw array waveform data and preprocess the raw array waveform data to obtain a spectrum array; The spectrum array processing module is used to calculate the slowness value of each sound wave mode through the spectrum array, and to obtain the effective dispersion curve by using hierarchical clustering and threshold partitioning methods. The effective dispersion curve processing module is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis using the time difference-frequency projection method to obtain the time difference-frequency projection curve after fracturing. The time difference change value calculation module is used to obtain the time difference-frequency projection curve before fracturing, and to overlay the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency. The fracturing evaluation result acquisition module is used to determine the degree of formation fracturing based on the time difference change value at the lowest frequency and to acquire the formation fracturing detection results.

9. A processor, characterized in that, It is configured to perform the formation fracturing detection method according to any one of claims 1 to 7.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the formation fracturing detection method according to any one of claims 1 to 7.

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