Ground monitoring microseism event identification method, electronic equipment, storage medium and device
By performing dynamic correction and cross-correlation calculation on perforation data and ground microseismic monitoring data, and combining the long-short time window energy ratio method, the problem of weak signal identification in ground microseismic monitoring was solved, achieving higher identification accuracy and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2026-03-24
AI Technical Summary
Weak signals are difficult to identify in ground microseismic monitoring, which makes it easy to miss microseismic events. Existing methods have low accuracy when there is a lot of noise interference and large signal differences.
By performing dynamic correction on perforation data and ground microseismic monitoring data, a microseismic seed trace sequence is constructed, the maximum cross-correlation coefficient is calculated, a stacked single-trace data is constructed, and the long-short time window energy ratio method is used to identify microseismic events.
It improves the accuracy of identifying ground microseismic events, reduces computational load, increases identification efficiency, and enhances the ability to identify weak signals.
Smart Images
Figure CN121721726A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of geophysical exploration technology, and more specifically, relates to a method, electronic device, storage medium and apparatus for identifying microseismic events on the ground. Background Technology
[0002] In the field of oil extraction, monitoring the microseismic signals generated during hydraulic fracturing, known as microseismic monitoring, allows for real-time monitoring of the fracturing process, evaluation of fracturing effectiveness, and optimization of fracturing process parameters. Microseismic technology was already a common method in fields such as mine disaster monitoring and geothermal development in the 1970s and 80s. Microseismic fracturing monitoring technology has now become a commonly used monitoring technique in the development of tight reservoir oil and gas fields. Since the beginning of the 21st century, due to the rapid development of unconventional oil and gas, especially shale gas, microseismic technology has played a crucial role in optimizing fracturing schemes and well network deployment, leading to the rapid development of microseismic monitoring technology in oil and gas field development.
[0003] Currently, the commonly used microseismic monitoring methods are mainly surface microseismic monitoring and borehole microseismic monitoring. Surface microseismic monitoring is similar to conventional seismic acquisition, using conventional single-component geophones arranged in a radial or grid pattern on the surface. During fracturing, microseismic signals are received, and acquisition, processing, and interpretation are completed. Borehole microseismic monitoring is similar to inter-well VSP acquisition, using VSP three-component geophones placed underground via cables to monitor microseismic signals in real time, also completing acquisition, processing, and interpretation. Surface microseismic signals differ from borehole microseismic signals, mainly due to greater interference from surface noise, resulting in weaker microseismic event signals. These are single-component data, but their advantages include a large number of geophones and a wide coverage area. Borehole microseismic monitoring, on the other hand, uses three-component acquisition. Microseismic event signals are only affected by the formation propagation path, and can receive microseismic signals of various energy levels, from strong to weak. Its disadvantages include a smaller number of geophones and a shorter multi-stage geophone string length. Therefore, compared to surface microseismic monitoring, weak signal identification is one of the key aspects of surface microseismic processing.
[0004] In addition to various denoising methods, a better event identification method can also improve the identification capability of weak signals in ground microseismic monitoring. The most commonly used identification method is the long-short time window energy ratio method or its improved methods. However, due to the large number of ground monitoring receivers, and the fact that each acquisition line exhibits different signal-to-noise ratios at different locations, and even significant differences in the effective signal amplitudes of different detectors on the same line, directly superimposing and identifying microseismic events using all dynamically calibrated seismic traces from all lines can easily lead to the omission of weak events.
[0005] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention
[0006] The purpose of this invention is to propose a method, electronic device, storage medium, and apparatus for identifying ground-based microseismic events, thereby achieving accurate identification of ground-based microseismic events and improving the accuracy of ground-based microseismic event identification.
[0007] To achieve the above objectives, the present invention proposes a method, electronic device, storage medium, and apparatus for identifying microseismic events during ground monitoring.
[0008] According to a first aspect of the present invention, a method for identifying microseismic events by ground monitoring is proposed, comprising:
[0009] Dynamic corrections were performed on both the perforation data and the ground microseismic monitoring data.
[0010] A microseismic seed tunnel sequence was constructed based on dynamically corrected perforation data and ground microseismic monitoring data;
[0011] Microseismic wavelets are constructed based on the microseismic seed trace sequence of the microseismic data.
[0012] Calculate the maximum cross-correlation coefficient between each sub-channel in the microseismic seed channel sequence and the microseismic wavelet;
[0013] Construct overlay single-channel data based on the maximum cross-correlation number;
[0014] Ground-based microseismic events are identified using the long-short time window energy ratio method based on the superimposed single-channel data.
[0015] Optionally, the construction of the microseismic seed channel sequence based on dynamically corrected perforation data and surface microseismic monitoring data includes:
[0016] Input the dynamically corrected perforation data, pick up the perforation signals in all survey lines whose amplitude meets the intensity requirements, and form a strong amplitude seed line sequence;
[0017] Input the dynamically corrected microseismic monitoring data, and select the corresponding microseismic seed trace sequence based on the strong amplitude seed trace sequence.
[0018] Optionally, constructing a microseismic wavelet based on the microseismic seed trace sequence includes:
[0019] Custom time window;
[0020] Based on the known automatically picked first arrival time positions, the corresponding microseismic sub-data is extracted from the microseismic seed channel sequence through the time window;
[0021] Microseismic wavelets are constructed based on the microseismic sub-data.
[0022] Optionally, constructing the overlay single-channel data based on the maximum cross-correlation coefficient includes:
[0023] Set a value between 0 and 1;
[0024] The maximum cross-correlation coefficients of the microseismic seed trace sequences are compared with the numerical values in ascending order of seed trace number.
[0025] When there is a seed trace with a maximum cross-correlation coefficient greater than or equal to the value, then all seed traces with a maximum cross-correlation coefficient greater than or equal to the value are combined into a new microseismic seed trace sequence.
[0026] When all the maximum cross-correlation coefficients are less than the stated value, the microseismic seed trace sequence is a new microseismic seed trace sequence.
[0027] Single-channel data stacking is calculated based on microseismic seed trace sequences.
[0028] Optionally, the identification of ground-based microseismic events using the long-short time window energy ratio method based on the stacked single-channel data includes:
[0029] Define long-term windows and short-term windows;
[0030] Energy ratio is calculated based on the superimposed single-channel data, long time window, and short time window.
[0031] Ground-based microseismic events are identified based on the energy ratio and event occurrence criteria.
[0032] Optionally, the criteria for determining the occurrence of an event are:
[0033] When j = t KA And ERA stk,j ≥K A At time point t KA Microseismic events exist;
[0034] Among them, K A To set a threshold value, j is the sampling point, and t KA For time samples, ERA stk,j This refers to the energy ratio.
[0035] Optionally, the expression for calculating the maximum cross-correlation coefficient is:
[0036] R i,max=max{∑ m (Wave k *A i,k-m )};
[0037] Where m,k = 1, 2, ..., N, i = 1, 2, ..., M, N is the window length, M is the total number of seed channels and is less than the total number of microseismic monitoring data channels, Wave k For microseismic wavelets, * represents discrete convolution operation, A i,k-m For the microseismic data from the k-th sampling point to the m-th sampling point in the i-th seed channel, R i,max Let be the maximum number of cross-correlation numbers corresponding to the i-th seed path.
[0038] According to a second aspect of the present invention, a ground monitoring microseismic event identification device is provided, comprising:
[0039] The dynamic correction module is used to perform dynamic correction on perforation data and ground microseismic monitoring data, respectively.
[0040] The first construction module is used to construct a microseismic seed channel sequence based on dynamically corrected perforation data and ground microseismic monitoring data;
[0041] The second construction module is used to construct microseismic wavelets based on the microseismic seed trace sequence of microseismic data;
[0042] The calculation module is used to calculate the maximum cross-correlation coefficient between various sub-channels in the microseismic seed channel sequence and the microseismic wavelet;
[0043] The third construction module is used to construct superimposed single-channel data based on the maximum cross-correlation coefficient;
[0044] The identification module is used to identify ground-based microseismic events based on the superimposed single-channel data using the long-short time window energy ratio method.
[0045] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:
[0046] At least one processor; and,
[0047] A memory communicatively connected to the at least one processor; wherein,
[0048] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the ground monitoring microseismic event identification method according to any of the first aspects.
[0049] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, characterized in that the non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the ground monitoring microseismic event identification method described in any of the first aspects.
[0050] The beneficial effects of this invention are as follows: This invention utilizes dynamically corrected perforation monitoring data to form a strong amplitude seed channel sequence, and then constructs a microseismic seed channel sequence using dynamically corrected ground microseismic monitoring data. Microseismic wavelets are then constructed using the microseismic seed channel sequence, and the maximum cross-correlation coefficient between the seed channel and the microseismic wavelet is calculated. After selection, a new seed channel sequence is constructed, forming a new superimposed channel. Finally, based on the new superimposed channel, the long-short time window energy ratio method is used to accurately identify ground microseismic events, improving the accuracy of ground microseismic event identification, reducing computational load, and increasing identification efficiency.
[0051] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description
[0052] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.
[0053] Figure 1 A flowchart illustrating the steps of a ground monitoring microseismic event identification method according to the present invention is shown.
[0054] Figure 2 A flowchart illustrating the steps of a ground monitoring microseismic event identification method according to Embodiment 2 of the present invention is shown.
[0055] Figure 3 A schematic diagram of a strong perforation signal according to Embodiment 3 of the present invention is shown.
[0056] Figure 4 a and Figure 4 b shows a schematic diagram of a weak seismic signal and a long-short time window energy ratio curve for event identification according to Embodiment 3 of the present invention.
[0057] Figure 5 A schematic diagram of a strong perforation signal according to Embodiment 4 of the present invention is shown.
[0058] Figure 6 a and Figure 6b shows a schematic diagram of a weak seismic signal and a long-short time window energy ratio curve for event identification according to Embodiment 4 of the present invention. Detailed Implementation
[0059] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
[0060] like Figure 1 According to a method for identifying microseismic events by ground monitoring according to the present invention, the method includes:
[0061] Dynamic corrections were performed on both the perforation data and the ground microseismic monitoring data.
[0062] A microseismic seed tunnel sequence was constructed based on dynamically corrected perforation data and ground microseismic monitoring data;
[0063] Microseismic wavelets are constructed from microseismic seed trace sequences;
[0064] Calculate the maximum cross-correlation coefficients between various sub-channels and microseismic wavelets in the microseismic seed channel sequence;
[0065] Construct overlay single-track data based on the maximum cross-correlation coefficient;
[0066] Ground-based microseismic events are identified using the energy ratio method based on stacked single-channel data.
[0067] Specifically, the system inputs known dynamic-calibrated perforation signals, manually picks up all high-amplitude perforation signals from the receiver channels of the survey lines, marks them as seed trace sequences, inputs dynamic-calibrated microseismic monitoring data, and outputs the corresponding microseismic seed trace sequence data A based on the seed trace sequences. i,j (i = 1, 2, ..., M, j = 1, 2...samp, where M is the total number of seed traces and is less than the total number of microseismic monitoring data traces, and samp is the total number of sample points); then, calculate the maximum cross-correlation coefficient of microseismic data based on the seed trace sequence. Define a time window W. k (k = 1, 2, ..., N, where N is the time window length), based on the known automatically picked initial arrival time position t i i = 1, 2, ..., M, from microseismic seed trace sequence data A i,j Extracting microseismic data from seed trace sequence A i,k A i,k =A i,jt (jt=t i+k, i = 1, 2,..M, k = 1, 2,..N); According to the seed trace sequence microseismic data A i,k Construct a microseismic wavelet Cross-correlate the microseismic wavelet with the intercepted seed trace microseismic data, and calculate the maximum cross-correlation coefficient R for each seed trace i,max : R i,max = max{∑ m (Wave k *A i,k-m )}(m, k = 1, 2,..N, i = 1, 2,..M, where * is the discrete convolution operation); Next, construct the stacked single trace data for event recognition. Set a 0-1 value P0, and compare the size of P0 with the maximum cross-correlation coefficient R of each seed trace i,max : Compare them in ascending order of the seed trace numbers. When R i,max ≥P0, mark all the seed traces with R i,max ≥P0 as the new seed trace sequence (i = 1, 2,..M * and M * <M), and the corresponding microseismic data of the new microseismic seed trace sequence is denoted as A * i,j (i = 1, 2,..M * , j = 1, 2,..samp); When all R i,max <P0, the microseismic seed trace sequence remains unchanged, M * = M, and the corresponding microseismic data also remains unchanged A * i,j = A i,j . Calculate the stacked single trace data (i = 1, 2,..M * , j = 1, 2,..samp, samp is the total number of samples), Finally, implement the surface microseismic event recognition. Define the long time window L1 and the short time window L2, and calculate the long-short time window energy ratio ERA stk,j for the stacked single trace data A stk,j data: (j = 1, 2,..samp, samp is the total number of samples); Then define the set threshold value K A , and the event judgment criterion is: when j = t KA and ERA stk,j ≥K A , there is a microseismic event at the time point t KA ; If there exists such a t KA that satisfies the above event judgment criterion, it means that there is a microseismic event at this time position, that is, the surface microseismic event recognition is achieved.
[0068] In one example, the microseismic seed channel sequence constructed based on dynamically corrected perforation data and surface microseismic monitoring data includes:
[0069] Input the dynamically corrected perforation data, pick up the perforation signals in all survey lines whose amplitude meets the intensity requirements, and form a strong amplitude seed line sequence;
[0070] Input the dynamically corrected microseismic monitoring data, and select the corresponding microseismic seed trace sequence based on the strong amplitude seed trace sequence.
[0071] In one example, constructing a microseismic wavelet based on microseismic seed trace sequences of microseismic data includes:
[0072] Custom time window;
[0073] Based on the known automatically picked first arrival time positions, the corresponding microseismic sub-data are extracted from the microseismic seed channel sequence through time windows;
[0074] Microseismic wavelets are constructed based on the data from each microseismic sub-seismic data point.
[0075] Specifically, define a time window W k (k = 1, 2, ..., M, where N is the time window length), based on the known automatically picked initial arrival time position t i i = 1, 2, ..., M, from microseismic seed trace sequence data A i,j Extracting microseismic data from seed trace sequence A i,k A i,k =A i,jt (jt=t i +k, i=1,2,..M, k=1,2,..N); based on the seed trace sequence microseismic data A i,k Constructing microseismic wavelets (k = 1, 2, ... N).
[0076] In one example, constructing overlay single-channel data based on the maximum cross-correlation number includes:
[0077] Set a value between 0 and 1;
[0078] The maximum cross-correlation coefficients of the microseismic seed trace sequences are compared with the numerical values in ascending order of seed trace number.
[0079] When there is a seed trace with a maximum cross-correlation coefficient greater than or equal to the value, then all seed traces with a maximum cross-correlation coefficient greater than or equal to the value are combined into a new microseismic seed trace sequence.
[0080] When all maximum cross-correlation coefficients are less than the numerical value, the microseismic seed trace sequence is the new microseismic seed trace sequence;
[0081] Calculate the stacked single trace data from the microseismic data based on the new microseismic seed trace sequence.
[0082] Specifically, set a 0-1 value P0 (default value 0.5), and compare P0 with the maximum cross-correlation coefficient R of each seed trace i,max in terms of magnitude: compare them in ascending order of the serial numbers of the seed traces. When R i,max ≥P0, mark all the seed traces with R i,max ≥P0 as the new seed trace sequence (i = 1, 2,..M * and M * < M), and the microseismic data corresponding to the new microseismic seed trace sequence is denoted as A * i,j (i = 1, 2,..M * , j = 1, 2,..samp); when all R i,max < P0, the microseismic seed trace sequence remains unchanged, M * = M, and the corresponding microseismic data also remains unchanged A * i,j = A i,j . Calculate the stacked single trace data (i = 1, 2,..M * , j = 1, 2,..samp, where samp is the total number of samples).
[0083] In one example, the identification of ground monitoring microseismic events based on the stacked single trace data by the long-short time window energy ratio method includes:
[0084] Define the long time window and the short time window;
[0085] Calculate the energy ratio based on the stacked single trace data, the long time window and the short time window;
[0086] Identify the ground monitoring microseismic events based on the energy ratio and the event occurrence judgment criterion.
[0087] Specifically, define the long time window L1 and the short time window L2, and calculate the long-short time window energy ratio ERA stk,j for the stacked single trace data A stk,j data: (j = 1, 2,..samp, where samp is the total number of samples). <00003(
[0088] In one example, the event occurrence judgment criterion is:
[0089] When j = t KA and ERA stk,j ≥K A , there is a microseismic event at the time point t KA ;
[0090] Among them, K A To set a threshold value, j is the sampling point, and t KA For time samples, ERA stk,j This refers to the energy ratio.
[0091] In one example, the expression for calculating the maximum cross-correlation coefficient is:
[0092] R i,max =max{∑ m (Wave k *A i,k-m )};
[0093] Where m,k = 1, 2, ..., N, i = 1, 2, ..., M, N is the window length, M is the total number of seed channels and is less than the total number of microseismic monitoring data channels, Wave k For microseismic wavelets, * represents discrete convolution operation, A i,k-m For the microseismic data from the k-th sampling point to the m-th sampling point in the i-th seed channel, R i,max Let be the maximum number of cross-correlation numbers corresponding to the i-th seed path.
[0094] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.
[0095] Example 1
[0096] This embodiment provides a method for identifying microseismic events during ground monitoring, including:
[0097] Dynamic corrections were performed on both the perforation data and the ground microseismic monitoring data.
[0098] A microseismic seed tunnel sequence was constructed based on dynamically corrected perforation data and ground microseismic monitoring data;
[0099] Microseismic wavelets are constructed from microseismic seed trace sequences;
[0100] Calculate the maximum cross-correlation coefficients between various sub-channels and microseismic wavelets in the microseismic seed channel sequence;
[0101] Construct overlay single-track data based on the maximum cross-correlation coefficient;
[0102] Ground-based microseismic events are identified using the energy ratio method based on stacked single-channel data.
[0103] The microseismic seed channel sequence constructed based on dynamically corrected perforation data and surface microseismic monitoring data includes:
[0104] Input the dynamically corrected perforation data, pick up the perforation signals in all survey lines whose amplitude meets the intensity requirements, and form a strong amplitude seed line sequence;
[0105] Input the dynamically corrected microseismic monitoring data, and select the corresponding microseismic seed trace sequence based on the strong amplitude seed trace sequence.
[0106] Constructing microseismic wavelets from microseismic seed trace sequences includes:
[0107] Custom time window;
[0108] Based on the known automatically picked first arrival time positions, the corresponding microseismic sub-data are extracted from the microseismic seed channel sequence through time windows;
[0109] Microseismic wavelets are constructed based on the data from each microseismic sub-seismic data point.
[0110] The superimposed single-track data constructed based on the maximum cross-correlation coefficient includes:
[0111] Set a value between 0 and 1;
[0112] The maximum cross-correlation coefficients of the microseismic seed trace sequences are compared with the numerical values in ascending order of seed trace number.
[0113] When there is a seed trace with a maximum cross-correlation coefficient greater than or equal to the value, then all seed traces with a maximum cross-correlation coefficient greater than or equal to the value are combined into a new microseismic seed trace sequence.
[0114] When all maximum cross-correlation coefficients are less than the numerical value, the microseismic seed trace sequence is the new microseismic seed trace sequence;
[0115] Single-channel data stacking is calculated based on microseismic seed trace sequences.
[0116] Ground-based microseismic event identification based on stacked single-channel data using the long-short time-window energy ratio method includes:
[0117] Define long-term windows and short-term windows;
[0118] Energy ratio is calculated based on superimposed single-channel data, long time windows, and short time windows;
[0119] Identification of microseismic events based on energy ratio and event occurrence criteria.
[0120] The criteria for determining the occurrence of an event are as follows:
[0121] When j = t KA And ERA stk,j ≥K A At time point t KA Microseismic events exist;
[0122] Among them, K A To set a threshold value, j is the sampling point, and t KA For time samples, ERA stk,j This refers to the energy ratio.
[0123] The expression for calculating the maximum cross-correlation coefficient is:
[0124] R i,max =max{∑ m (Wave k *A i,k-m )};
[0125] Where m,k = 1, 2, ..., N, i = 1, 2, ..., M, N is the window length, M is the total number of seed channels and is less than the total number of microseismic monitoring data channels, Wave k For microseismic wavelets, * represents discrete convolution operation, A i,k-m For the microseismic data from the k-th sampling point to the m-th sampling point in the i-th seed channel, R i,max Let be the maximum number of cross-correlation numbers corresponding to the i-th seed path.
[0126] The calculation expression for superimposed single-channel data is:
[0127]
[0128] Where i = 1, 2, ..., M * j = 1, 2, ..., sample, where sample is the total number of sample points, M * ≤M, M * This represents the total number of seed channels in the new microseismic seed channel sequence.
[0129] The formula for calculating the energy ratio is:
[0130]
[0131] L1 is the long time window, and L2 is the short time window.
[0132] Example 2
[0133] like Figure 2 As shown, this embodiment provides a method for identifying microseismic events through ground monitoring, including:
[0134] The first step involves inputting dynamically calibrated perforation monitoring data, picking out perforation signals with strong amplitudes to form a strong amplitude seed channel sequence, and then inputting dynamically calibrated microseismic monitoring data to filter out the corresponding seed channel sequence of dynamically calibrated microseismic data. The second step defines a time window, using the automatically picked microseismic first arrival time as the starting point, to extract the seed channel sequence microseismic data, further superimpose them to construct a microseismic wavelet, and cross-correlate it with various seed channel microseismic data to calculate the corresponding maximum cross-correlation coefficient. The third step sets a 0-1 value (default value is 0.5). When the maximum cross-correlation coefficient in the second step is greater than or equal to the set value, the seed channel number is counted to form a new seed channel sequence; otherwise, the seed channel number remains unchanged. Based on the seed channel sequence, the dynamically calibrated microseismic data are superimposed to construct a superimposed single-channel data. The fourth step uses the long-short time window energy ratio method to perform event identification and judgment on the superimposed data from the third step, ultimately achieving accurate identification of ground microseismic events.
[0135] Input the known dynamic-calibrated perforation signals, manually pick up all high-amplitude perforation signals from the receiver channels of the survey lines, and mark them as seed channel sequences. Then input the dynamic-calibrated microseismic monitoring data, and output the corresponding microseismic seed channel sequence data A based on the seed channel sequences. i,j (i = 1, 2, ..., M, j = 1, 2, ..., sample, where M is the total number of seed traces and is less than the total number of microseismic monitoring data traces, and sample is the total number of sample points);
[0136] Then, the maximum cross-correlation coefficient of microseismic data based on the seed trace sequence is calculated. A time window W is defined. k (k = 1, 2, ..., N, where N is the time window length), based on the known automatically picked initial arrival time position t i i = 1, 2, ..., M, from microseismic "seed" trace sequence data A i,j Extracting microseismic data from seed trace sequence A i,k A i,k =A i,jt (jt=t i +k, i=1,2,..M, k=1,2,..N);
[0137] Based on seed tunnel sequence microseismic data A i,k Construct a microseismic wavelet wave k :
[0138]
[0139] The microseismic wavelet is cross-correlated with the truncated seed trace microseismic data, and the maximum cross-correlation coefficient R for each seed trace is calculated. i,max :
[0140] R i,max =max{∑ m(Wave k *A i,k-m )}(m,k = 1,2,..N, i = 1,2,..M, where * is the discrete convolution operation);
[0141] Next, construct the stacked single-channel data for event recognition. Set a 0-1 value P0 (default value 0.5), and compare P0 with the maximum cross-correlation coefficient R i,max of each seed channel: Compare them in ascending order of the seed channel numbers. When R i,max ≥ P0, mark all seed channels with R i,max ≥ P0 as a new seed channel sequence (i = 1,2,..M * and M * < M), and the microseismic data corresponding to the new microseismic seed channel sequence is denoted as A * i,j (i = 1,2,..M * , j = 1,2,..samp); when all R i,max < P0, the microseismic seed channel sequence remains unchanged, M * = M, and the corresponding microseismic data also remains unchanged A * i,j = A i,j .
[0142] Calculate the stacked single-channel data A stk,j :
[0143] (i = 1,2,..M * , j = 1,2,..samp, where samp is the total number of samples).
[0144] Finally, implement ground microseismic event recognition. Define a long time window L1 and a short time window L2, and calculate the energy ratio ERA stk,j of the stacked single-channel data A stk,j data:
[0145] (j = 1,2,..samp, where samp is the total number of samples);
[0146] Then define a set threshold value K A , and the criterion for event occurrence is: when j = t KA and ERA stk,j ≥ K A , there is a microseismic event at time point t KA ; if there exists such a t KA that satisfies the above event criterion, it means there is a microseismic event at this time position, that is, ground microseismic event recognition is achieved.
[0147] Example 3
[0148] This embodiment provides a method for identifying microseismic events during ground monitoring, including:
[0149] like Figure 3 , Figure 4 The images show strong perforation signals and weak microseismic signals from the same fracturing section, respectively. Firstly, from... Figure 3 The seed path sequence picked up (marked by black lines) is correspondingly from... Figure 4 (a) Microseismic data based on seed trace sequences are obtained and directly superimposed to construct a microseismic wavelet. k Then, the microseismic wavelet is cross-correlated with the intercepted seed trace microseismic data, and the maximum cross-correlation coefficient R for each seed trace is calculated and statistically analyzed. i,max Next, a value of 0.5 is set, and the maximum cross-correlation coefficient is compared with the above. Seed channels with a value greater than or equal to 0.5 are re-identified to form a new seed channel sequence. Figure 4 (a) Black line markings are overlaid to output single-channel data for event recognition. Finally, long and short time window energy ratio event recognition is performed, and a threshold value is set. Because the signal-to-noise ratio of the overlaid single-channel data is high, it is easy to identify events from... Figure 4 (b) determines the existence of a microseismic event, thus achieving effective identification of ground microseismic events.
[0150] Example 4
[0151] This embodiment provides a method for identifying microseismic events during ground monitoring, including:
[0152] like Figure 5 , Figure 6 The images show strong perforation signals and extremely weak microseismic signals from the same fracturing section, respectively. Figure 5 The seed path sequence picked up (marked by black lines) is correspondingly from... Figure 6 (a) Microseismic data based on seed trace sequences are obtained and directly superimposed to construct a microseismic wavelet. k Then, the microseismic wavelet is cross-correlated with the intercepted seed trace microseismic data, and the maximum cross-correlation coefficient R for each seed trace is calculated and statistically analyzed. i,max Next, a value of 0.5 is set, and the maximum cross-correlation coefficient is compared with the above. Seed channels with a value greater than or equal to 0.5 are re-identified to form a new seed channel sequence. Figure 6 (a) Black line markings are overlaid to output single-channel data for event recognition. Finally, long-short time window energy ratio event recognition is performed, and a threshold value is set. Due to the high signal-to-noise ratio of the overlaid single-channel data, from... Figure 6 (b) accurately identifies extremely weak events, further verifying the good application effect of the present invention.
[0153] Example 5
[0154] This embodiment provides a ground monitoring microseismic event identification device, including:
[0155] The dynamic correction module is used to perform dynamic correction on perforation data and ground microseismic monitoring data, respectively.
[0156] The first construction module is used to construct a microseismic seed channel sequence based on dynamically corrected perforation data and ground microseismic monitoring data;
[0157] The second building module is used to construct microseismic wavelets based on microseismic seed trace sequences;
[0158] The calculation module is used to calculate the maximum cross-correlation coefficient between various sub-channels and microseismic wavelets in the microseismic seed channel sequence;
[0159] The third building module is used to construct overlay single-channel data based on the maximum cross-correlation coefficient;
[0160] The identification module is used to identify microseismic events on the ground based on superimposed single-channel data using the long-short time window energy ratio method.
[0161] Example 6
[0162] This disclosure also provides an electronic device, which includes:
[0163] At least one processor; and,
[0164] A memory communicatively connected to the at least one processor; wherein,
[0165] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the ground monitoring microseismic event identification method in Embodiment 1.
[0166] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0167] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.
[0168] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.
[0169] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.
[0170] Example 7
[0171] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the ground monitoring microseismic event identification method in Embodiment 1.
[0172] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.
[0173] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
[0174] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A method for identifying microseismic events via ground monitoring, characterized in that, include: Dynamic corrections were performed on both the perforation data and the ground microseismic monitoring data. A microseismic seed tunnel sequence was constructed based on dynamically corrected perforation data and ground microseismic monitoring data; Microseismic wavelets are constructed based on the microseismic seed trace sequence of the microseismic data. Calculate the maximum cross-correlation coefficient between each sub-channel in the microseismic seed channel sequence and the microseismic wavelet; Construct overlay single-channel data based on the maximum cross-correlation number; Ground-based microseismic events are identified using the long-short time window energy ratio method based on the superimposed single-channel data.
2. The method for identifying microseismic events by ground monitoring according to claim 1, characterized in that, The microseismic seed channel sequence constructed based on dynamically corrected perforation data and surface microseismic monitoring data includes: Input the dynamically corrected perforation data, pick up the perforation signals in all survey lines whose amplitude meets the intensity requirements, and form a strong amplitude seed line sequence; Input the dynamically corrected microseismic monitoring data, and select the corresponding microseismic seed trace sequence based on the strong amplitude seed trace sequence.
3. The method for identifying microseismic events by ground monitoring according to claim 1, characterized in that, The construction of microseismic wavelets based on the microseismic seed trace sequence includes: Custom time window; Based on the known automatically picked first arrival time positions, the corresponding microseismic sub-data is extracted from the microseismic seed channel sequence through the time window; Microseismic wavelets are constructed based on the microseismic sub-data.
4. The method for identifying microseismic events by ground monitoring according to claim 1, characterized in that, The construction of superimposed single-channel data based on the maximum cross-correlation coefficient includes: Set a value between 0 and 1; The maximum cross-correlation coefficients of the microseismic seed trace sequences are compared with the numerical values in ascending order of seed trace number. When there is a seed trace with a maximum cross-correlation coefficient greater than or equal to the value, then all seed traces with a maximum cross-correlation coefficient greater than or equal to the value are combined into a new microseismic seed trace sequence. When all the maximum cross-correlation coefficients are less than the stated value, the microseismic seed trace sequence is a new microseismic seed trace sequence. Single-channel data stacking is calculated based on microseismic seed trace sequences.
5. The method for identifying microseismic events by ground monitoring according to claim 1, characterized in that, The identification of ground-based microseismic events using the long-short time-window energy ratio method based on the superimposed single-channel data includes: Define long-term windows and short-term windows; Energy ratio is calculated based on the superimposed single-channel data, long time window, and short time window. Ground-based microseismic events are identified based on the energy ratio and event occurrence criteria.
6. The method for identifying microseismic events by ground monitoring according to claim 5, characterized in that, The criteria for determining the occurrence of an event are as follows: When j = t KA And ERA stk,j ≥K A At time point t KA Microseismic events exist; Among them, K A To set a threshold value, j is the sampling point, and t KA For time samples, ERA stk,j This refers to the energy ratio.
7. The method for identifying microseismic events by ground monitoring according to claim 1, characterized in that, The expression for calculating the maximum cross-correlation coefficient is as follows: R i,max =max{∑ m (Wave k *A i,k-m )}; Where m,k = 1, 2, ..., N, i = 1, 2, ..., M, N is the window length, M is the total number of seed channels and is less than the total number of microseismic monitoring data channels, Wave k For microseismic wavelets, * represents discrete convolution operation, A i,k-m For the microseismic data from the k-th sampling point to the m-th sampling point in the i-th seed channel, R i,max Let be the maximum number of cross-correlation numbers corresponding to the i-th seed path.
8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the ground monitoring microseismic event identification method according to any one of claims 1-7.
9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the ground monitoring microseismic event identification method according to any one of claims 1-7.
10. A ground-based microseismic event identification device, characterized in that, include: The dynamic correction module is used to perform dynamic correction on perforation data and ground microseismic monitoring data, respectively. The first construction module is used to construct a microseismic seed channel sequence based on dynamically corrected perforation data and ground microseismic monitoring data; The second construction module is used to construct microseismic wavelets based on the microseismic seed trace sequence of microseismic data; The calculation module is used to calculate the maximum cross-correlation coefficient between various sub-channels in the microseismic seed channel sequence and the microseismic wavelet; The third construction module is used to construct superimposed single-channel data based on the maximum cross-correlation coefficient; The identification module is used to identify ground-based microseismic events based on the superimposed single-channel data using the long-short time window energy ratio method.