Microseismic event discrimination method and system based on multi-channel superposition morphological characteristics

Abstract

The invention provides a microseism event discrimination method and system based on multichannel superposition morphological characteristics, and belongs to the field of microseism monitoring data processing. The method comprises the following steps: step 1, acquiring corrected multi-channel microseismic data; 2, calculating multi-channel superposition features and picking up events; 3, calculating a kurtosis value in an event time window; and 4, calculating a threshold value and judging an event. According to the method, after event detection is carried out by utilizing a conventional automatic detection method, kurtosis values of multiple superposed characteristic curves are introduced to further judge whether the event is true or false, so that the false detection rate of automatic identification of the microseism event is reduced, the workload of effective microseism event screening in a subsequent artificial quality control link is reduced, and the accuracy of the microseism event identification is improved. And the processing interpretation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of microseismic monitoring data processing, and in particular relates to a microseismic event discrimination method and system based on multi-channel superposition morphological features. Background Art

[0002] In the field of unconventional oil and gas extraction, microseismic monitoring technology monitors underground fracturing events in real time, describes the real-time distribution characteristics of hydraulic fracturing cracks, and then evaluates the fracturing effect and guides the optimization of engineering parameters.

[0003] Direct wave fields are usually used to locate microseismic events, and event identification is a key step before microseismic positioning. The accuracy of event identification directly affects the positioning accuracy. For single-component microseismic data, automatic identification methods include short-time window energy ratio method (STA / LTA), fractal dimension method, and Akaike information criterion method (AIC). STA / LTA is the most commonly used method for automatic identification of microseismic events because of its high processing efficiency and meeting the requirements of real-time monitoring.

[0004] However, due to the stability of the picking algorithm, the actual data is complex and the signal-to-noise ratio is low. In order to ensure the detection capability of weak signals, the picking threshold is usually lowered, which may cause the false picking of events, and it is necessary to conduct a secondary identification of microseismic "events". Manual identification is highly subjective, and in areas with natural fractures, due to the large number of events, the efficiency is low and the requirements of real-time processing cannot be taken into account. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a method and system for distinguishing microseismic events based on multi-channel superposition morphological features, so as to reduce human intervention and perform secondary distinction of microseismic events when the picking threshold is low and the false picking rate is high.

[0006] One of the purposes of the present invention is to provide a method for distinguishing microseismic events based on multi-channel superposition morphological features.

[0007] A second object of the present invention is to provide a microseismic event identification system based on multi-channel superposition morphological features.

[0008] A third object of the present invention is to provide a computer-readable storage medium.

[0009] A fourth object of the present invention is to provide a computer device.

[0010] The present invention is achieved through the following technical solutions:

[0011] The first aspect of the present invention provides a method for distinguishing microseismic events based on multi-channel stacking morphological features, comprising the following steps:

[0012] Step 1, obtaining corrected multi-channel microseismic data;

[0013] Step 2, calculating multi-channel superposition features and event picking;

[0014] Step 3, calculate the kurtosis value within the event time window;

[0015] Step 4: Calculate the threshold and identify the event.

[0016] A further improvement of the present invention is:

[0017] The step 1 of obtaining the corrected multi-channel microseismic data includes:

[0018] Acquire single-component multi-channel microseismic data required for event discrimination Wherein, i = 1, 2, ... N, N is the number of detectors; j = 1, 2, ..., K, K is the number of sampling points, and the single-component multi-channel microseismic data is corrected for the monitoring target segment.

[0019] A further improvement of the present invention is:

[0020] The time difference correction is specifically for single-component multi-channel microseismic data Time shift is performed, and the time shift amount of each channel is Δt i , Δt i is the estimated propagation time from the source location to the detector i.

[0021] A further improvement of the present invention is:

[0022] In step 2, the multi-channel superposition features and event picking are calculated, and the specific operations include:

[0023] Step 21: Calculate the multi-channel superposition characteristic curve of the microseismic signal as the reference channel S j :

[0024]

[0025] Where N is the number of detectors, T i is the corrected multi-channel microseismic data;

[0026] Step 22: Under threshold triggering, the time P corresponding to the peak point of the reference trace is considered as the picked event time.

[0027] A further improvement of the present invention is:

[0028] The step 21 includes: considering the influence of polarity change, firstly calculating the envelope of each channel of multi-channel microseismic data by Hilbert transform, and then superimposing them by using the above formula.

[0029] A further improvement of the present invention is:

[0030] In step 3, the kurtosis value within the event time window is calculated, and the specific operations include:

[0031] Step 31, according to the wavelength of the direct wave of the event, determine a time window length 2L, where L refers to the length of half a wavelength;

[0032] Step 32: Calculate the kurtosis value K of the event characteristic curve in the time window. The calculation formula is as follows:

[0033]

[0034] Among them, start is pL, end is p+L, S is the reference track, is the average value of the reference trace.

[0035] A further improvement of the present invention is:

[0036] In step 4, the threshold is calculated and the event is identified, and the specific operations include:

[0037] Step 41, set the threshold discriminant function T, the formula is as follows:

[0038]

[0039] Step 42: Perform event discrimination. When the T calculated by the picked event is 1, it is judged as a true event, otherwise it is a false event.

[0040] The second aspect of the present invention provides a microseismic event identification system based on multi-channel stacking morphological features, comprising:

[0041] An acquisition unit, used for acquiring corrected multi-channel microseismic data;

[0042] An event picking unit, used for calculating multi-channel superposition features and event picking;

[0043] A calculation unit, used for calculating the kurtosis value within the event time window;

[0044] The time discrimination unit is used to calculate the threshold and discriminate events.

[0045] The third object of the present invention is to provide a computer-readable storage medium, which stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the microseismic event identification method based on multi-channel superposition morphological features as described above.

[0046] A fourth aspect of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the microseismic event identification method based on multi-channel superposition morphological features as described above.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] After using conventional automatic detection methods to detect events, the present invention introduces the kurtosis value of multiple superimposed characteristic curves to further distinguish the truth or falsity of events, thereby reducing the false positive rate of automatic identification of microseismic events, reducing the workload of effective microseismic event screening in subsequent manual quality control links, and improving processing and interpretation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention provides a flow chart of a microseismic event discrimination method based on multi-channel stacking morphological features;

[0050] Figure 2 It is a set of actual microseismic monitoring records;

[0051] Figure 3 is based on Figure 2 Multi-channel superposition curve calculated from the data;

[0052] Figure 4 It is another set of actual microseismic monitoring records;

[0053] Figure 5 is based on Figure 4 Multi-channel superposition curve calculated from the data;

[0054] Figure 6 This is an example of event identification results. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0056] The present invention provides a method for distinguishing microseismic events based on multi-channel superposition morphological features, such as Figure 1 As shown, the method comprises the following steps:

[0057] Step 1, obtaining corrected multi-channel microseismic data;

[0058] Specific operations include:

[0059] Acquire single-component multi-channel microseismic data required for event discrimination Wherein, i = 1, 2, ... N, N is the number of detectors; j = 1, 2, ..., K, K is the number of sampling points, and the single-component multi-channel microseismic data is corrected for the monitoring target segment.

[0060] The single-component multi-channel microseismic data are data with fixed time window length containing events detected by conventional event automatic method (STA / LTA).

[0061] The time difference correction is specifically for single-component multi-channel microseismic data Time shift is performed, and the time shift amount of each channel is Δt i , Δt i is the estimated propagation time from the source location to the detector i.

[0062] The time difference correction is used to flatten the phase axis. The original ground microseismic data has a low signal-to-noise ratio. The corrected data can suppress random noise and improve the signal-to-noise ratio after superposition.

[0063] Step 2, calculating multi-channel superposition features and event picking;

[0064] Specific operations include:

[0065] (1) Calculate the multi-channel superposition characteristic curve of the microseismic signal as the reference channel S j :

[0066]

[0067] Where N is the number of detectors, T i It is the corrected multi-channel microseismic data.

[0068] Considering the influence of polarity change, the envelope of each channel of multi-channel microseismic data is first calculated using Hilbert transform, and then the above formula is used to superimpose them to reduce the influence of polarity change;

[0069] Hilbert transform is a prior art method and will not be described in detail here;

[0070] (2) Under threshold triggering (the threshold is the set value), the time P corresponding to the peak point of the reference track (that is, the maximum value of the reference track curve) is considered to be the picked event time.

[0071] Step 3, calculate the kurtosis value within the event time window;

[0072] Specific operations include:

[0073] (1) According to the wavelength of the direct wave of the event, a time window length of 2L is determined, where L refers to the length of half a wavelength;

[0074] (2) Calculate the kurtosis value K of the event characteristic curve within the time window. The calculation formula is as follows:

[0075]

[0076] Among them, start is pL, end is p+L, S is the reference track, is the average value of the reference trace.

[0077] Step 4, calculate the threshold and identify the event;

[0078] Specific operations include:

[0079] (1) Set the threshold discriminant function T, the formula is as follows:

[0080]

[0081] (2) To identify the event, when the T calculated by the picked event is 1, it is judged as a true event, otherwise it is a false event.

[0082] The present invention introduces the statistical features of multi-channel superposition features for secondary discrimination of events, which has good stability and enables discrimination under low signal-to-noise ratio of data. It reduces the false detection rate of automatic identification of microseismic events, avoids the process of manual screening, and improves the processing and interpretation efficiency.

[0083] Figure 2 This is an actual microseismic monitoring record, with a total of 1268 receiving channels and 8000 sampling points per channel. It can be seen that Figure 2 There is a microseismic event at sampling point 1220 (true), Figure 3 is the calculated superposition characteristic curve (reference trace).

[0084] Figure 4 is another actual microseismic record with the same acquisition parameters. Figure 2 It can be seen that Figure 4 At sampling point 6823, there is a false microseismic event that is mistakenly picked up due to noise. Figure 5 is the calculated multi-channel superposition curve. According to formula (2), the kurtosis characteristics of the two in a given time window are calculated, and the discriminant function is used to distinguish true and false events.

[0085] from Figure 6 The results show that true and false events can be distinguished well.

[0086] [Example 2]

[0087] The embodiment of the present invention provides a microseismic event identification system based on multi-channel stacking morphological features, comprising:

[0088] The acquisition unit is used to acquire the corrected multi-channel microseismic data and perform the following operations:

[0089] Acquire single-component multi-channel microseismic data required for event discrimination Wherein, i = 1, 2, ... N, N is the number of detectors; j = 1, 2, ..., K, K is the number of sampling points, and the single-component multi-channel microseismic data is corrected for the monitoring target segment.

[0090] The single-component multi-channel microseismic data are data with fixed time window length containing events detected by conventional event automatic method (STA / LTA).

[0091] The time difference correction is specifically for the single-component multi-channel microseismic data T j i is time-shifted, and the time shift of each channel is Δt i , Δt i is the estimated propagation time from the source location to the detector i.

[0092] The time difference correction is used to flatten the phase axis. The original ground microseismic data has a low signal-to-noise ratio. The corrected data can suppress random noise and improve the signal-to-noise ratio after superposition.

[0093] The event picking unit is used to calculate multi-channel superposition features and event picking, and performs the following operations:

[0094] (1) Calculate the multi-channel superposition characteristic curve of the microseismic signal as the reference channel S j :

[0095]

[0096] Where N is the number of detectors, T i It is the corrected multi-channel microseismic data.

[0097] Considering the influence of polarity change, the envelope of each channel of multi-channel microseismic data is first calculated using Hilbert transform, and then the above formula is used to superimpose them to reduce the influence of polarity change;

[0098] Hilbert transform is a prior art method and will not be described in detail here;

[0099] (2) Under threshold triggering (the threshold is the set value), the time P corresponding to the peak point of the reference track (that is, the maximum value of the reference track curve) is considered to be the picked event time.

[0100] The calculation unit is used to calculate the kurtosis value within the event time window and perform the following operations:

[0101] (1) According to the wavelength of the direct wave of the event, a time window length of 2L is determined, where L refers to the length of half a wavelength;

[0102] (2) Calculate the kurtosis value K of the event characteristic curve within the time window. The calculation formula is as follows:

[0103]

[0104] Among them, start is pL, end is p+L, S is the reference track, is the average value of the reference trace.

[0105] The time discrimination unit is used to calculate the threshold and discriminate events, performing the following operations:

[0106] (1) Set the threshold discriminant function T, the formula is as follows:

[0107]

[0108] (2) To identify the event, when the T calculated by the picked event is 1, it is judged as a true event, otherwise it is a false event.

[0109] [Example 3]

[0110] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the microseismic event identification method based on multi-channel superposition morphological features as described above.

[0111] [Example 4]

[0112] An embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the microseismic event identification method based on multi-channel superposition morphological features as described above.

[0113] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate

[0114] SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0115] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A method for distinguishing microseismic events based on multi-channel stacking morphological features, characterized in that: The following steps are involved: Step 1, obtaining corrected multi-channel microseismic data; Step 2, calculating multi-channel superposition features and event picking; Step 3, calculate the kurtosis value within the event time window; Step 4: Calculate the threshold and identify the event.

2. The microseismic event identification method based on multi-channel superposition morphological features according to claim 1 is characterized in that: The step 1 of obtaining the corrected multi-channel microseismic data includes: Acquire single-component multi-channel microseismic data required for event discrimination Wherein, i = 1, 2, ... N, N is the number of detectors; j = 1, 2, ..., K, K is the number of sampling points, and the single-component multi-channel microseismic data is corrected for the monitoring target segment.

3. The microseismic event identification method based on multi-channel superposition morphological features according to claim 2 is characterized in that: The time difference correction is specifically for single-component multi-channel microseismic data Time shift is performed, and the time shift amount of each channel is Δt i , Δt i is the estimated propagation time from the source location to the detector i.

4. The microseismic event identification method based on multi-channel superposition morphological features according to claim 3 is characterized in that: In step 2, the multi-channel superposition features and event picking are calculated, and the specific operations include: Step 21: Calculate the multi-channel superposition characteristic curve of the microseismic signal as the reference channel S j : Where N is the number of detectors, T i is the corrected multi-channel microseismic data; Step 22: Under threshold triggering, the time P corresponding to the peak point of the reference trace is considered as the picked event time.

5. The microseismic event identification method based on multi-channel superposition morphological features according to claim 4 is characterized in that: The step 21 includes: Taking into account the influence of polarity change, the envelope of each channel of multi-channel microseismic data is first calculated using Hilbert transform, and then the above formula is used to superimpose them.

6. The microseismic event identification method based on multi-channel superposition morphological features according to claim 4 is characterized in that: In step 3, the kurtosis value within the event time window is calculated, and the specific operations include: Step 31, according to the wavelength of the direct wave of the event, determine a time window length 2L, where L refers to the length of half a wavelength; Step 32: Calculate the kurtosis value K of the event characteristic curve in the time window. The calculation formula is as follows: Among them, start is pL, end is p+L, S is the reference track, is the average value of the reference trace.

7. The microseismic event identification method based on multi-channel superposition morphological features according to claim 6 is characterized in that: In step 4, the threshold is calculated and the event is identified, and the specific operations include: Step 41, set the threshold discriminant function T, the formula is as follows: Step 42: Perform event discrimination. When the T calculated by the picked event is 1, it is judged as a true event, otherwise it is a false event.

8. A microseismic event identification system based on multi-channel stacking morphological features, characterized in that: include: An acquisition unit, used for acquiring corrected multi-channel microseismic data; An event picking unit, used for calculating multi-channel superposition features and event picking; A calculation unit, used for calculating the kurtosis value within the event time window; The time discrimination unit is used to calculate the threshold and discriminate events.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the microseismic event identification method based on multi-channel superposition morphological features as described in any one of claims 1 to 7.

10. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the microseismic event discrimination method based on multi-channel superposition morphological features as described in any one of claims 1 to 7.

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