Formation fracturing detection method and apparatus, storage medium, and processor

Abstract

A formation fracturing detection method, belonging to the technical field of logging. The method comprises: acquiring frequency spectrum data of original array waveform data; calculating the slowness value of each acoustic wave vibration mode; then on the basis of an iterative operation of hierarchical clustering and threshold division, acquiring a dispersion curve greater than a preset resolution; using a slowness-frequency projection method to project the slowness and frequency of the dispersion curve onto an axis of slowness, so as to obtain a post-fracturing slowness-frequency projection curve; overlaying same with a pre-fracturing slowness-frequency projection curve, so as to obtain a slowness change value at the lowest frequency; and finally, according to the slowness change value at the lowest frequency, determining the degree of formation fracturing, thereby obtaining a formation fracturing detection result. The method can accurately identify changes in transverse wave velocities caused by formation fracturing and the degree of formation fracturing. Also provided are a formation fracturing detection apparatus, a storage medium and a processor.

Description

Formation fracturing detection method, device, storage medium and processor Technical Field

[0001] The present invention relates to the field of well logging technology, and in particular to a formation fracturing detection method, a formation fracturing detection device, a processor, and a machine-readable storage medium. Background Art

[0002] Fracturing of unconventional oil and gas reservoirs is an effective means of increasing the production of tight reservoirs and an important part of subsequent oil and gas field exploration and development. It is crucial to accurately characterize the fracturing effect of the formation. However, due to the geological characteristics and reservoir conditions of unconventional oil and gas reservoirs, it is difficult to accurately judge the fracturing effect by evaluating the fracturing effect based on well temperature logging, liquid production profiles, and production dynamic data. In the field of sonic logging, the method of evaluating the height of the fracture using orthogonal dipole anisotropy inversion is intuitive and accurate, but factors such as the fracture network morphology and the angle of the formation relative to the wellbore can lead to large uncertainties in the fracture evaluation effect, and are not applicable to the evaluation of the volumetric fracturing network in unconventional reservoirs. Therefore, in the unconventional field, there is still a lack of practical and reliable methods for evaluating the effect of formation fracturing.

[0003] Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a formation fracturing detection method, a formation fracturing detection device, a processor and a machine-readable storage medium to solve the problems of insufficient resolution and low stability of existing formation fracturing detection methods in the unconventional oil and gas field.

[0005] In order to achieve the above-mentioned object, the present invention provides a first aspect of a formation fracturing detection method, the method comprising:

[0006] Acquiring original array waveform data, and preprocessing the original array waveform data to obtain spectrum data;

[0007] The slowness value of each acoustic wave mode is calculated through the spectrum data, and the effective dispersion curve is obtained by using the hierarchical clustering method and threshold partitioning method.

[0008] 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.

[0009] Obtain the time difference-frequency projection curve before fracturing, overlap the time difference-frequency projection curves before and after fracturing, and obtain the time difference change value at the lowest frequency;

[0010] The formation fracturing degree is determined according to the time difference change value at the lowest frequency, and the formation fracturing detection result is obtained.

[0011] Optionally, preprocessing the original array waveform data to obtain spectrum data includes:

[0012] Performing depth correction on the original array waveform data, and curve splicing the depth-corrected original array waveform data to obtain effective array waveform data;

[0013] Perform Fourier transform on the effective array waveform data to obtain spectrum data.

[0014] Optionally, the matrix bundle formed by the original array waveform data is specifically Y2-zY1:

[0015] in,

[0016] Z0=diag[z1 Z2…z p ]

[0017] B=diag[b1 b2…b p ]

[0018] i=1,2…p;

[0019] Where z i is a complex exponential term, p represents the number of acoustic wave vibration shapes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0020] Optionally, the slowness value is calculated as:

[0021] k i =arctan[Im(z i ) / Re(z i ) / 2πd], i=1,2…p;

[0022] Where s i represents the slowness value of the i-th sound wave vibration mode, k i represents the wave number of the i-th acoustic vibration mode, ω represents the angular frequency, z i is a complex exponential term.

[0023] Optionally, the method of calculating the slowness value of each acoustic wave mode shape through spectrum data and obtaining an effective dispersion curve using a hierarchical clustering method and a threshold partitioning method includes:

[0024] The hierarchical clustering algorithm is used to repeatedly perform hierarchical clustering on the slowness values ​​of each acoustic wave mode to obtain effective slowness scatter points;

[0025] The effective slowness scatter points are divided based on a threshold division method, and a dispersion curve with a resolution greater than a preset resolution is taken as an effective dispersion curve.

[0026] Optionally, the hierarchical clustering algorithm is used to repeatedly perform hierarchical clustering on the slowness values ​​of each acoustic wave mode shape:

[0027] Where ε represents the neighborhood radius, MinPts represents the minimum number of points within the neighborhood radius, c represents the effective slowness scatter points, and d represents the noise scatter points.

[0028] Optionally, determining the formation fracturing degree according to the time difference change value at the lowest frequency and obtaining the formation fracturing detection result includes:

[0029] Determine the formation fracturing degree according to the minimum frequency change value;

[0030] A formation fracturing grade is determined based on the formation fracturing degree, and the formation fracturing grade is used as a formation fracturing detection result.

[0031] A second aspect of the present invention provides a formation fracturing detection device, comprising:

[0032] A raw data processing module is used to obtain raw array waveform data and pre-process the raw array waveform data to obtain spectrum data;

[0033] The spectrum data processing module is used to calculate the slowness value of each acoustic wave vibration mode through the spectrum data, and obtain the effective dispersion curve by using the hierarchical clustering method and threshold partitioning method;

[0034] An 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 a time difference-frequency projection method to obtain a time difference-frequency projection curve after fracturing;

[0035] The time difference change value calculation module is used to obtain the time difference-frequency projection curve before fracturing, overlap the time difference-frequency projection curves before and after fracturing, and obtain the time difference change value at the lowest frequency;

[0036] The fracturing evaluation result acquisition module is used to determine the formation fracturing degree according to the time difference change value at the lowest frequency and obtain the formation fracturing detection result.

[0037] A third aspect of the present invention provides a processor configured to execute the above-mentioned formation fracturing detection method.

[0038] A fourth aspect of the present invention provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned formation fracturing detection method.

[0039] The present invention provides a formation fracturing detection method, device, processor and storage medium. The method obtains spectrum data by preprocessing the acquired original array waveform data; then calculates the slowness value of each acoustic wave vibration mode through the spectrum data, and adopts hierarchical clustering method and threshold division method to obtain an effective dispersion curve; then adopts time difference-frequency projection method 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, and overlaps the time difference-frequency projection curves before and after fracturing to obtain the time difference change value at the lowest frequency; finally, determines the formation fracturing degree according to the time difference change value at the lowest frequency, obtains the formation fracturing detection result, effectively improves the accuracy of dipole flexural wave dispersion processing and the reliability of SFA analysis results, and can accurately distinguish the formation shear wave velocity change and the formation fracturing degree caused by fracturing.

[0040] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0042] FIG1 is a flow chart of a formation fracturing detection method provided by one embodiment of the present invention;

[0043] FIG2 a is a diagram showing dispersion curve extraction results after normalization of raw array waveform data provided by one embodiment of the present invention;

[0044] FIG2 b is a diagram showing dispersion curve processing results after hierarchical clustering normalization processing provided by one embodiment of the present invention;

[0045] FIG3a is the first part of a comparison diagram of array acoustic wave SFA results before and after a secondary fracturing operation in a certain well according to an embodiment of the present invention;

[0046] FIG3 b is the second part of a comparison diagram of array acoustic wave SFA results before and after a secondary fracturing operation in a certain well according to an embodiment of the present invention;

[0047] FIG4 is a block diagram of a formation fracturing detection device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0049] FIG1 is a flow chart of a formation fracturing detection method provided by an embodiment of the present invention. As shown in FIG1 , an embodiment of the present invention provides a formation fracturing detection method, the method comprising:

[0050] S10: Acquire original array waveform data, and preprocess the original array waveform data to obtain spectrum data.

[0051] The original array waveform data refers to the full-wave array data of the acoustic waves excited by the monopole and dipole within the target depth range collected during the array acoustic logging process. The array acoustic logging technology used in this embodiment includes but is not limited to digital acoustic logging and array acoustic logging.

[0052] Specifically, after acquiring the raw array waveform data, depth correction and curve splicing are required to obtain valid array waveform data. Depth correction involves calibrating all logging curves to a completely consistent depth correspondence to meet the strict depth requirements of data processing. Curve splicing involves removing sections where the instrument encounters obstructions or stuck, and splicing the data from multiple measurements to form a complete logging curve.

[0053] Generally, if the collected data is waveform data of a deviated well array, the deviated well depth needs to be corrected to the depth of the vertical well in order to obtain the true depth and thickness of the formation.

[0054] Furthermore, in this embodiment, N receivers are provided to receive array sound waveform data. The spectrum data obtained after Fourier transformation of the received array sound waveform data is:

[0055] Where ω0 represents the angular frequency, k represents the spatial wave number; b i is plural, z i is a complex exponential term, p represents the number of acoustic wave vibration shapes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0056] Construct a mother Hankel matrix:

[0057] Extract the two matrices Y1 of the first Np-1 columns and Y2 of the last Np-1 columns from the mother Hankel matrix to obtain the matrix bundle Y2-zY1;

[0058] in,

[0059] Z0=diag[z1 z2…z p ]

[0060] B=diag[b1 b2…b p ]

[0061] i=1,2...p;

[0062] Where z i is a complex exponential term, p represents the number of acoustic wave vibration shapes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

[0063] S20: Calculate the slowness value of each acoustic wave mode through the spectrum data, and use the hierarchical clustering method and threshold partitioning method to obtain the effective dispersion curve.

[0064] Specifically, the slowness calculation formula is:

[0065] k i =arctan[Im(z i ) / Re(z i ) / 2πd], i=1,2…p;

[0066] Where s i represents the slowness value of the i-th sound wave vibration mode, k i represents the wave number of the i-th acoustic vibration mode, ω represents the frequency, z i is a complex exponential term.

[0067] After obtaining the slowness values ​​for each acoustic mode shape, a hierarchical clustering algorithm is used to repeatedly cluster these slowness values ​​to obtain effective slowness scatter points. This hierarchical clustering process considers both the inverted acoustic amplitude and slowness, gradually distinguishing and extracting the dispersion characteristics of the formation shear waves to obtain effective slowness scatter points and noise scatter points. Effective slowness scatter points refer to fundamental-order points in the waveform data, while noise scatter points refer to non-fundamental-order points in the waveform data.

[0068] The specific execution process of the above hierarchical clustering algorithm is as follows:

[0069] Where ε represents the neighborhood radius, MinPts represents the minimum number of points within the neighborhood radius, c represents the effective slowness scatter points, and d represents the noise scatter points.

[0070] The hierarchical clustering algorithm in this embodiment includes but is not limited to the DBSCAN algorithm.

[0071] Furthermore, in order to improve data processing efficiency, this embodiment also needs to perform standardization processing on the effective dispersion curve to convert data in different data ranges into a unified standard data range.

[0072] Figures 2a and 2b are the dispersion curve extraction results after the original array waveform data is normalized and the dispersion curve extraction results after the hierarchical clustering normalization, respectively. From the processing results, it can be seen that the dispersion curve after hierarchical clustering can complete the automatic classification of effective slowness scatter points and noise scatter points, and realize high-precision extraction of the dispersion curve of the array waveform data.

[0073] After obtaining the effective slowness scatter points, they are divided based on the threshold division method, and the dispersion curves with a resolution greater than the preset resolution are taken as effective dispersion curves.

[0074] S30: Projecting the time difference and frequency of the effective dispersion curve onto the time difference axis using a time difference-frequency projection method to obtain a time difference-frequency projection curve after fracturing.

[0075] 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.

[0076] S40: Obtaining a time difference-frequency projection curve before fracturing, overlapping the time difference-frequency projection curves before fracturing and after fracturing, and obtaining a time difference change value at the lowest frequency.

[0077] S50: Determine the formation fracturing degree according to the time difference change value at the lowest frequency, and obtain the formation fracturing detection result.

[0078] Specifically, after obtaining the time difference change value at the lowest frequency, the formation fracturing degree is determined based on the time difference change value at the lowest frequency. In this embodiment, methods for determining the formation fracturing degree based on the time difference change value at the lowest frequency include, but are not limited to, querying a formation fracturing degree table and obtaining a mapping relationship between the time difference change value and the formation fracturing degree.

[0079] After the formation fracturing degree is obtained, the formation fracturing grade is determined based on the formation fracturing degree, and the formation fracturing grade is used as the formation fracturing detection result.

[0080] Figures 3a and 3b show examples of the application of this formation fracturing detection method in a secondary hydraulic fracturing well. In Figure 3a, the first figure from left to right is a graph of natural gamma and wellbore diameter, and the second figure is a comparison of the SFA results of the open hole well and after the first hydraulic fracturing operation. In Figure 3b, the first figure from left to right is a comparison of the SFA results after the first and second hydraulic fracturing operations, and the second figure is the production conclusion, which includes: TCMR, total porosity; CMRP_3MS, effective porosity with a cutoff value of 3ms; CMFF, nuclear magnetic resonance free fluid volume.

[0081] The darker scattered points in the second image from left to right in Figure 3a and the first image from left to right in Figure 3b represent the SFA analysis results after the first fracturing operation. The lighter scattered points in the second image from left to right in Figure 3a represent the SFA analysis results of the openhole well, and the lighter scattered points in the first image from left to right in Figure 3b represent the SFA analysis results after the second fracturing operation. Comparing the treatment results before and after the fracturing operation in the target interval X535-X550m, it can be seen that the SFA distribution and morphological characteristics of the openhole well and after the first fracturing operation are almost identical, indicating that the first fracturing operation was not effective and did not form a large-scale complex network of fractures. However, the SFA distribution characteristics after the second fracturing operation changed significantly, indicating that the formation fracturing effect in the target depth range was significant. The increase in the time difference distribution range is directly proportional to the fracturing degree, which is consistent with the production conclusions.

[0082] This formation fracturing detection method utilizes array waveform data, combining a dispersion analysis algorithm based on linear prediction theory with hierarchical clustering. This method simultaneously considers the inverted acoustic amplitude and slowness during density clustering. With minimal human intervention and a high degree of automation, it effectively addresses the inability of traditional array acoustic logging to accurately capture reservoir characteristics. It effectively extracts dispersion curves from slowness scatter points within three iterations, significantly improving the accuracy of dipole flexural wave dispersion processing and the reliability of SFA analysis results. This method can quickly and accurately identify the degree of formation fracturing caused by fracturing, further expanding the application of acoustic logging in formation fracturing detection.

[0083] FIG4 is a block diagram of a formation fracturing detection device provided by one embodiment of the present invention. As shown in FIG4 , the present invention provides a formation fracturing detection device, which includes a raw data processing module 10, a spectrum data 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.

[0084] The raw data processing module 10 is used to obtain raw array waveform data and pre-process the raw array waveform data to obtain spectrum data;

[0085] The spectrum data processing module 20 is used to calculate the slowness value of each acoustic wave mode through the spectrum data, and obtain the effective dispersion curve by using the hierarchical clustering method and the threshold division method;

[0086] 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;

[0087] The time difference change value calculation module 40 is used to obtain the time difference-frequency projection curve before fracturing, overlap the time difference-frequency projection curves before fracturing and after fracturing, and obtain the time difference change value at the lowest frequency;

[0088] The fracturing evaluation result acquisition module 50 is used to determine the formation fracturing degree according to the time difference change value at the lowest frequency, and obtain the formation fracturing detection result.

[0089] Each module of the above formation fracturing detection device can be applied to a computing device including a memory and a processor.

[0090] An embodiment of the present invention further provides a machine-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-mentioned formation fracturing detection method.

[0091] An embodiment of the present invention further provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by the electronic speed regulator, the processor can be configured to execute the above-mentioned formation fracturing detection method.

[0092] Those skilled in the art will appreciate that all or part of the steps in the methods of the aforementioned embodiments can be accomplished by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a single-chip microcomputer, 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 mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] The above describes in detail the optional embodiments of the present invention in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, a variety of simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the scope of protection 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 unless there is any contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will no longer describe the various possible combinations separately.

[0094] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A formation fracturing detection method, characterized in that: The method comprises: Acquire original array waveform data, and preprocess the original array waveform data to obtain spectrum data; The slowness value of each acoustic wave vibration type is calculated through the spectrum data, and the effective dispersion curve is obtained by using the hierarchical clustering method and threshold division method. 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, overlap the time difference-frequency projection curves before fracturing and after fracturing, and obtain the time difference change value at the lowest frequency; The formation fracturing degree is determined according to the time difference change value at the lowest frequency, and the formation fracturing detection result is obtained.

2. The formation fracturing detection method according to claim 1, characterized in that: The preprocessing of the original array waveform data to obtain spectrum data includes: Performing depth correction on the original array waveform data, and curve splicing the original array waveform data after depth correction to obtain effective array waveform data; Perform Fourier transform on the effective array waveform data to obtain spectrum data.

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 Y2-zY1; in, Z0=diag[z1 z2 … z p ] B=diag[b1 b2 … b p ] i=1,2…p; In the formula, z i is a complex exponential term, p represents the number of acoustic wave vibration modes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

4. The formation fracturing detection method according to claim 1, characterized in that: The slowness calculation formula is: k i =arctan[Im(z i ) / Re(z i ) / 2πd], i=1,2…p; In the formula, s i represents the slowness value of the ith sound wave vibration mode, k i represents the wave number of the ith acoustic vibration mode, ω represents the angular frequency, z i is a complex exponential term.

5. The formation fracturing detection method according to claim 1, characterized in that: The method of calculating the slowness value of each acoustic wave vibration type through the spectrum data and obtaining the effective dispersion curve by using the hierarchical clustering method and the threshold division method includes: The hierarchical clustering algorithm is used to repeatedly perform hierarchical clustering on the slowness values ​​of each acoustic wave vibration mode to obtain effective slowness scatter points; The effective slowness scatter points are divided based on a threshold division method, and a dispersion curve having a resolution greater than a preset resolution is taken as an effective dispersion curve.

6. The formation fracturing detection method according to claim 5, characterized in that: The hierarchical clustering algorithm is used to repeatedly perform hierarchical clustering on the slowness values ​​of each acoustic wave vibration mode: Where ε represents the neighborhood radius, MinPts represents the minimum number of points within the neighborhood radius, c represents the effective slowness scatter points, and d represents the noise scatter points.

7. The formation fracturing detection method according to claim 1, characterized in that: The step of determining the formation fracturing degree according to the time difference change value at the lowest frequency and obtaining the formation fracturing detection result comprises: Determine the formation fracturing degree according to the minimum frequency change value; A formation fracturing grade is determined based on the formation fracturing degree, and the formation fracturing grade is used as a formation fracturing detection result.

8. A formation fracturing detection device, characterized in that: The device comprises: A raw data processing module, used to obtain raw array waveform data and pre-process the raw array waveform data to obtain spectrum data; The spectrum data processing module is used to calculate the slowness value of each acoustic wave vibration type through the spectrum data, and obtain the effective dispersion curve by using the hierarchical clustering method and the threshold division method; An 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 a time difference-frequency projection method to obtain a 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, overlap the time difference-frequency projection curves before fracturing and after fracturing, and obtain the time difference change value at the lowest frequency; The fracturing evaluation result acquisition module is used to determine the formation fracturing degree according to the time difference change value at the lowest frequency and obtain the formation fracturing detection result.

9. A processor, characterized in that: The method is configured to perform the formation fracture detection 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 a formation fracture detection method according to any one of claims 1 to 7.

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