Formation fracturing detection method and device, processor and storage medium

Abstract

The invention 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 comprises the steps of calculating slowness values of all sound wave vibration modes by obtaining a frequency spectrum array of original array waveform data, then obtaining a frequency dispersion curve greater than a preset resolution according to iterative operation of hierarchical clustering and threshold division, and projecting time difference and frequency of the frequency dispersion curve to a time difference axis by adopting a time difference-frequency projection method to obtain a time difference-frequency dispersion curve. The time difference-frequency projection curve after fracturing is obtained, the time difference-frequency projection curve after fracturing is overlapped with the time difference-frequency projection curve before fracturing, a time difference change value at the lowest frequency is obtained, finally, the stratum fracturing degree is determined according to the time difference change value at the lowest frequency, and a stratum fracturing detection result is obtained. And the stratum shear wave velocity change and the stratum fracturing degree caused by fracturing can be accurately distinguished.

Description

Technical Field

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

[0002] The fracturing transformation of unconventional oil and gas reservoirs is an effective means to increase production from tight reservoirs and an important link in the subsequent exploration and development of oil and gas fields. How to accurately and precisely characterize the fracturing transformation effect of the formation is crucial. Currently, the methods for evaluating the fracturing transformation effect mainly include:

[0003] (1) A method for evaluating the hydraulic fracturing effect based on orthogonal dipole anisotropy. This method has many limitations, with many influencing factors and a single means, and cannot accurately and precisely characterize the fracturing transformation effect of the formation.

[0004] (2) Dispersion analysis methods (such as the Prony method and the weighted spectral coherence method) can effectively calculate the propagation velocity and attenuation of different acoustic wave modes in the well. However, due to the fact that this method is easily affected by noise interference and the algorithm itself has poor stability, the resolution and stability of its data processing results cannot be guaranteed.

[0005] (3) Clustering algorithms. Although this method has a relatively wide application in the evaluation of fracturing effects and the processing of logging data, in on-site measured data, its application effect on complex heterogeneous reservoirs after hydraulic fracturing transformation lacks practicality and reliability. The above three commonly used methods cannot accurately and precisely describe the transformation effect of the formation after fracturing operations. Summary of the Invention

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

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

[0008] Obtain original array waveform data, and preprocess the original array waveform data to obtain a spectrum array;

[0009] Calculate the slowness values of each acoustic wave mode through the spectrum array, and use the hierarchical clustering method and the threshold division method to obtain an effective dispersion curve;

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

[0011] 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;

[0012] Determine the formation fracturing degree according to the time difference change value at the lowest frequency, and obtain the formation fracturing detection result.

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

[0014] Perform depth correction on the original array waveform data, and splice the curves of the depth-corrected original array waveform data to obtain effective array waveform data;

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

[0016] Optionally, the matrix pencil formed by the original array waveform data is specifically Y 2 -zY 1 :

[0017] Wherein,

[0018]

[0019]

[0020]

[0021] Z 0 =diag[z 1 z 2 … z p

[0022] B = diag[b 1 b 2 … b p

[0023] i = 1, 2…p

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

[0025] Optionally, the slowness value calculation formula is:

[0026]

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

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

[0029] Optionally, calculating the slowness value of each acoustic wave mode through the spectral array and obtaining the effective dispersion curve by using the hierarchical clustering method and the threshold division method includes:

[0030] Repeatedly performing hierarchical clustering on the slowness values of each acoustic wave mode by using the hierarchical clustering algorithm to obtain effective slowness scatter points;

[0031] Based on the threshold division method, dividing the effective slowness scatter points, and taking the dispersion curve greater than the preset resolution as the effective dispersion curve.

[0032] Optionally, the repeatedly performing hierarchical clustering on the slowness values of each acoustic wave mode by using the hierarchical clustering algorithm is specifically:

[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 according to the time difference change value at the lowest frequency and obtaining the formation fracturing detection result includes:

[0036] Determining the formation fracturing degree according to the lowest frequency change value;

[0037] Based on the formation fracturing degree, determining the formation fracturing grade, and taking the formation fracturing grade as the formation fracturing detection result.

[0038] The second aspect of the present invention provides a formation fracturing detection device, and the device includes:

[0039] An original data processing module, configured to obtain original array waveform data and preprocess the original array waveform data to obtain a spectral array;

[0040] A spectral array processing module, configured to calculate the slowness value of each acoustic wave mode through the spectral array and obtain an effective dispersion curve by using the hierarchical clustering method and the threshold division method;

[0041] An effective dispersion curve processing module, configured to project the time difference and frequency of the effective dispersion curve onto the time difference axis by using the time difference-frequency projection method to obtain a post-fracturing time difference-frequency projection curve;

[0042] 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;

[0043] 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.

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

[0045] A fourth aspect of the present invention provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned formation fracturing detection method.

[0046] The present invention provides a formation fracturing detection method, device, processor and storage medium. The method preprocesses the acquired original array waveform data to obtain a spectrum array; then calculates the slowness values of each acoustic mode through the spectrum array, and uses the hierarchical clustering method and the threshold division method to obtain an effective dispersion curve; then uses the 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, overlap the time difference-frequency projection curves before and after fracturing, and obtain the time difference change value at the lowest frequency; finally, determine the formation fracturing degree according to the time difference change value at the lowest frequency and obtain the formation fracturing detection result, effectively improving the accuracy of dipole flexural wave dispersion processing and the reliability of the SFA analysis result, and can accurately distinguish the formation shear wave velocity change caused by fracturing and the formation fracturing degree.

[0047] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific embodiments section. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The 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 specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0049] Figure 1 is a flowchart of a formation fracturing detection method provided by an embodiment of the present invention;

[0050] Figure 2a is a diagram of the extraction result of the dispersion curve after the normalization processing of the original array waveform data provided by an embodiment of the present invention;

[0051] Figure 2b is a diagram of the processing result of the dispersion curve after the hierarchical clustering normalization processing provided by an embodiment of the present invention;

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

[0053] Figure 4 It is a block diagram of a formation fracturing detection device provided by an embodiment of the present invention. Specific embodiments

[0054] The following will describe 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 for explaining and understanding the present invention, and are not used to limit the present invention.

[0055] Figure 1 It is a flowchart of a formation fracturing detection method provided by an embodiment of the present invention. As Figure 1 shown, an embodiment of the present invention provides a formation fracturing detection method, which includes:

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

[0057] Among them, the original array waveform data refers to the full-wave array data of monopole and dipole excited acoustic waves collected in the target depth interval during array acoustic logging. The technologies adopted in the array acoustic logging in this embodiment include but are not limited to digital acoustic logging and array acoustic logging.

[0058] Specifically, after obtaining the original array waveform data, it is necessary to perform depth correction and curve splicing on the original array waveform data to obtain effective array waveform data. Among them, depth correction is: correcting all logging curves to a completely consistent depth correspondence relationship to meet the strict requirements of data processing for depth; curve splicing is: removing the well sections where the instrument encounters resistance or jamming, splicing the data measured multiple times, and finally forming a complete logging curve.

[0059] Generally, if the collected data is inclined well array waveform data, it is necessary to correct the inclined well depth to the depth of the vertical well to obtain the true depth and thickness of the formation.

[0060] Further, this embodiment sets N receivers for receiving array acoustic waveform data, and the spectrum data obtained after Fourier transform of the received array acoustic waveform data is:

[0061]

[0062] In the formula, ω 0 represents the angular frequency, k represents the spatial wave number; b i is a complex number, z iis a complex exponential term, p represents the number of acoustic mode shapes in the original array waveform, and N represents the number of receivers for receiving the original array waveform.

[0063] Construct a mother Hankel Matrix:

[0064]

[0065] Extract the first N - p - 1 columns of Y from the mother Hankel Matrix 1 and the last N - p - 1 columns of Y 2 to obtain two matrices, getting the matrix pencil Y 2 -zY 1 ;

[0066] where

[0067]

[0068]

[0069]

[0070] Z 0 = diag[z 1 z 2 … z p

[0071] B = diag[b 1 b 2 … b p

[0072] i = 1, 2…p

[0073] wherein, z i is a complex exponential term, p represents the number of acoustic mode shapes in the original array waveform, and N represents the number of receivers for receiving the original array waveform.

[0074] S20: Calculate the slowness values of each acoustic mode shape through the spectral array, and obtain the effective dispersion curve by using the hierarchical clustering method and the threshold division method.

[0075] Specifically, the slowness value calculation formula is:

[0076]

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

[0078] wherein, s i represents the slowness value of the i-th acoustic mode shape, k​​i The wavenumber representing the i-th acoustic wave mode, ω represents the frequency, and z i is a complex exponential term.

[0079] After obtaining the slowness values of each acoustic wave mode, 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. During the hierarchical clustering process, the inverted acoustic wave amplitude and slowness are considered simultaneously, and the dispersion characteristics of the formation shear wave are gradually distinguished and extracted to obtain effective slowness scatter points and noise scatter points. Among them, the effective slowness scatter points refer to the points of the fundamental order in the waveform data, and the noise scatter points refer to the 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 the data processing efficiency, this embodiment also needs to perform standardization processing on the effective dispersion curves to convert data in different data ranges into a unified standard data range.

[0085] Figure 2a and Figure 2b are respectively the extraction result diagram of the dispersion curve after standardization processing of the original array waveform data and the extraction result diagram of the dispersion curve after standardization processing of hierarchical clustering. From the processing results, the dispersion curve after hierarchical clustering can complete the automatic classification of effective slowness scatter points and noise scatter points, and achieve 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 curve greater than the preset resolution is used as the effective dispersion curve.

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

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

[0089] S40: 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.

[0090] S50: Determine the formation fracturing degree according to the time difference change value at the lowest frequency, and obtain the formation fracturing detection result.

[0091] Specifically, after obtaining the time difference change value at the lowest frequency, determine the formation fracturing degree according to the time difference change value at the lowest frequency. The method for determining the formation fracturing degree according to the time difference change value at the lowest frequency in this embodiment includes, but is not limited to, querying the formation fracturing degree table and obtaining the mapping relationship between the time difference change value and the formation fracturing degree.

[0092] After obtaining the formation fracturing degree, determine the formation fracturing grade based on the formation fracturing degree, and use the formation fracturing grade as the formation fracturing detection result.

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

[0094] Among them, the blue scatter points in the second and third graphs represent the SFA analysis results after the first fracturing operation, the gray scatter points in the second graph represent the SFA analysis results of the open-hole well, and the orange scatter points in the third graph represent the SFA analysis results after the second fracturing operation. By comparing the processing results before and after the fracturing operation in the target interval X535 - X550m, it can be seen that the SFA distributions and morphological characteristics of the open-hole well and after the first fracturing are almost the same, indicating that the effect of the first fracturing operation is not good and no large-scale complex network fractures are formed. However, the SFA distribution characteristics after the second fracturing have changed significantly. The formation fracturing effect in the target depth interval is remarkable, and the increase degree of its time difference distribution range is proportional to the fracturing degree, which is consistent with the production conclusion.

[0095] This formation fracturing detection method utilizes array waveform data, combines the dispersion analysis algorithm based on linear prediction theory with hierarchical clustering, and simultaneously considers the inverted acoustic wave amplitude and slowness during the density clustering process. It has less human intervention and a high degree of automation, can effectively solve the problem that traditional array acoustic logging cannot accurately obtain reservoir characteristics, can effectively extract the dispersion curve of slowness scatter points within three iterations, significantly improve the accuracy of dipole flexural wave dispersion processing, and improve the reliability of SFA analysis results. This method can quickly and accurately distinguish the formation fracturing degree caused by fracturing, further expanding the application scope of acoustic logging in formation fracturing detection.

[0096] Figure 4 It is a block diagram of a formation fracturing detection device provided by an embodiment of the present invention. As Figure 4 shown, an embodiment of the present invention provides a formation fracturing detection device, which includes an original 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 original data processing module 10 is used to obtain the original array waveform data and preprocess the original array waveform data to obtain a spectrum array;

[0098] The spectrum array processing module 20 is used to calculate the slowness values of each acoustic wave mode through the spectrum array, and obtain an effective dispersion curve by using the hierarchical clustering method and the threshold division method;

[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 by using the time difference - frequency projection method to obtain a 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, overlap the time difference - frequency projection curves before and after fracturing, and obtain the time difference change value at the lowest frequency;

[0101] 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.

[0102] An embodiment of the present invention also provides a machine - readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above - mentioned formation fracturing detection method is implemented.

[0103] An embodiment of the present invention also provides a machine - readable storage medium, on which instructions are stored. When the instructions are executed by an electronic governor, the processor can be configured to execute the above - mentioned formation fracturing detection method.

[0104] Those skilled in the art can understand that all or part of the steps in the methods for implementing the above embodiments can be completed by instructing relevant hardware through a program. This program is stored in a storage medium and includes several instructions to enable a single-chip microcomputer, a chip, or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0105] The optional embodiments of the present invention have been described in detail above 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 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. Additionally, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention do not separately describe various possible combination methods.

[0106] Furthermore, any combination can be made between various different embodiments of the present invention, as long as it does not violate the idea of the embodiments of the present invention, and it 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: Obtain the original array waveform data, and preprocess the original array waveform data to obtain a spectrum array; Calculate the slowness values of each acoustic mode through the spectrum array, and use the hierarchical clustering method and the threshold division method to obtain effective dispersion curves; Use the 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; 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; Determine the formation fracturing degree according to the time difference change value at the lowest frequency, and obtain the formation fracturing detection result.

2. The method for detecting formation fracturing according to claim 1, characterized in that, the preprocessing of the original array waveform data to obtain a spectrum array includes: Perform depth correction on the original array waveform data, and splice the curves of the depth-corrected original array waveform data to obtain effective array waveform data; Perform Fourier transform on the effective array waveform data to obtain a spectrum array.

3. The method for detecting formation fracturing according to claim 1, characterized in that, The matrix pencil formed by the original array waveform data is specifically Y 2 -zY 1 ; wherein, Z 0 = diag[z 1 z 2 …z p ​ B = diag[b 1 b 2 …b p ​ i = 1, 2... p where z i is a complex exponential term, p represents the number of acoustic mode shapes in the original array waveform, and N represents the number of receivers that receive the original array waveform.

4. The method for detecting formation fracturing according to claim 1, characterized in that, The slowness value calculation formula is: k i = arctan[Im(z i ) / Re(z i ) / 2πd] i = 1, 2…p. where s i represents the slowness value of the i-th acoustic wave mode, k i represents the wave number of the i-th acoustic wave mode, ω represents the angular frequency, and zi is the complex exponential term.

5. The method for detecting formation fracturing according to claim 1, characterized in that, the calculation of the slowness values of each acoustic mode through the spectrum array and the use of the hierarchical clustering method and the threshold division method to obtain effective dispersion curves includes: Use the hierarchical clustering algorithm to repeatedly perform hierarchical clustering on the slowness values of each acoustic mode to obtain effective slowness scatter points; Based on the threshold division method, divide the effective slowness scatter points, and use the dispersion curve greater than the preset resolution as the effective dispersion curve.

6. The method for detecting formation fracturing according to claim 5, characterized in that, the use of The specific process of repeatedly performing hierarchical clustering on the slowness values of each acoustic mode by the hierarchical clustering algorithm is: 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 point, and d represents the noise scatter point.

7. The method for detecting formation fracturing according to claim 1, characterized in that, the determination of the formation fracturing degree according to the time difference change value at the lowest frequency and the obtaining of the formation fracturing detection result include: Determine the formation fracturing degree according to the lowest frequency change value; Based on the formation fracturing degree, determine the formation fracturing grade, and use the formation fracturing grade as the formation fracturing detection result.

8. A device for detecting formation fracturing, characterized in that, the device includes: An original data processing module, configured to obtain the original array waveform data, and preprocess the original array waveform data to obtain a spectrum array; A spectrum array processing module, configured to calculate the slowness values of each acoustic mode through the spectrum array, and use the hierarchical clustering method and the threshold division method to obtain effective dispersion curves; An effective dispersion curve processing module, which is used to project the time difference and frequency of the effective dispersion curve onto the time difference axis by using the time difference - frequency projection method to obtain the time difference - frequency projection curve after fracturing; A time difference change value calculation module, which 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; A fracturing evaluation result acquisition module, which 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, it is configured to execute the formation fracturing detection method according to any one of claims 1 to 7.

10. A machine - readable storage medium, on which instructions are stored, characterized in that, when the instructions are executed by a processor, the processor is configured to execute the formation fracturing detection method according to any one of claims 1 to 7.

Images