Micro-seismic bad trace elimination method and device based on data statistics

Abstract

The invention discloses a micro-seismic bad trace elimination method and device based on data statistics. The method comprises the following steps: inputting micro-seismic monitoring data; counting the maximum value and the minimum value of each channel of monitoring data in the microseism monitoring data; on the basis of the maximum value and the minimum value of the monitoring data of each channel, constructing a bad channel elimination factor of each channel; rejecting bad trace data in the microseismic monitoring data through each bad trace rejecting factor; and outputting the microseism monitoring data after the bad traces are removed. According to the invention, automatic elimination of bad traces of micro-seismic monitoring data can be realized.

Description

Technical Field

[0001] This invention relates to the field of petroleum geophysics, and more specifically, to a method and apparatus for removing microseismic bad paths based on statistical data. Background Technology

[0002] In recent years, due to the strong demand for unconventional oil and gas reservoir development, hydraulic fracturing has been widely used in shale oil and gas development. Microseismic monitoring technology, which monitors micro-seismic events induced by hydraulic fracturing, is considered a key technology for evaluating the effectiveness of hydraulic fracturing. Microseismic monitoring involves deploying geophones at designated locations to monitor micro-seismic signals, and then obtaining the location of the microseismic source through preprocessing, noise removal, signal identification, source imaging, and source localization.

[0003] Currently, commonly used data acquisition devices in microseismic monitoring include SERCEL408 and SERCEL508. Equipment malfunctions and restarts manifest as bad channels in the data layer, specifically large step jumps in the signal mean. This can lead to misidentification of microseismic signals and also affect the accuracy of microseismic source location. Summary of the Invention

[0004] The purpose of this invention is to propose a method and apparatus for removing bad channels in microseismic data based on data statistics, so as to realize the automatic removal of bad channels in microseismic monitoring data.

[0005] To achieve the above objectives, in a first aspect, the present invention proposes a microseismic bad path removal method based on data statistics, comprising:

[0006] Input microseismic monitoring data;

[0007] The maximum and minimum values ​​of each channel of microseismic monitoring data were statistically analyzed.

[0008] A bad channel elimination factor for each channel is constructed based on the maximum and minimum values ​​of each channel's monitoring data.

[0009] Bad path data in microseismic monitoring data is removed by using various bad path removal factors;

[0010] Output microseismic monitoring data after removing bad paths.

[0011] Optionally, the maximum and minimum values ​​of each trace of the microseismic monitoring data can be calculated using the following formula:

[0012]

[0013] Where j is the seismic trace number, i is the sample point number in the seismic trace, M is the number of sample points in the seismic trace, and MAX is the maximum number of sample points in the seismic trace. j MIN represents the maximum value of the monitoring data in channel j. jLet A be the minimum value of the monitoring data in channel j. i,j This refers to the data at the j-th channel and the i-th sample point.

[0014] Optionally, the formula for calculating the bad sector rejection factor for each track is as follows:

[0015]

[0016] Among them, G j For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

[0017] Optionally, bad path data in microseismic monitoring data can be removed using the following calculation formula:

[0018] A′ i,j =A i,j G j

[0019] Among them, A i i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

[0020] Secondly, the present invention provides an electronic device, the electronic device comprising:

[0021] At least one processor; and,

[0022] A memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the data statistics-based microseismic bad path removal method described in the first aspect.

[0024] Thirdly, the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the microseismic bad path removal method based on data statistics described in the first aspect.

[0025] Fourthly, this invention proposes a microseismic bad path removal device based on data statistics, comprising:

[0026] The data input module is used to input microseismic monitoring data;

[0027] The data statistics module is used to calculate the maximum and minimum values ​​of each channel of the microseismic monitoring data.

[0028] The bad sector rejection factor construction module is used to construct bad sector rejection factors for each channel based on the maximum and minimum values ​​of the monitoring data for each channel.

[0029] The bad path data removal module is used to remove bad path data from microseismic monitoring data by using bad path removal factors for each path.

[0030] The data output module is used to output microseismic monitoring data after removing bad paths.

[0031] Optionally, the maximum and minimum values ​​of each trace of the microseismic monitoring data can be calculated using the following formula:

[0032]

[0033] Where j is the seismic trace number, i is the sample point number in the seismic trace, M is the number of sample points in the seismic trace, and MAX is the maximum number of sample points in the seismic trace. j MIN represents the maximum value of the monitoring data in channel j. j Let A be the minimum value of the monitoring data in channel j. i,j This refers to the data at the j-th channel and the i-th sample point.

[0034] Optionally, the formula for calculating the bad sector rejection factor for each track is as follows:

[0035]

[0036] Among them, G j For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

[0037] Optionally, bad path data in microseismic monitoring data can be removed using the following calculation formula:

[0038] A′ i,j =A i,j G j

[0039] Among them, A′ i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

[0040] The beneficial effects of this invention are as follows:

[0041] The method of this invention first statistically analyzes the maximum and minimum values ​​of each channel of the microseismic monitoring data. Then, based on the maximum and minimum values ​​of each channel, it constructs a bad channel removal factor for each channel. Next, it removes bad channel data from the microseismic monitoring data using the bad channel removal factor. Finally, it outputs the microseismic monitoring data after removing bad channels. This method constructs the bad channel removal factor through single-channel data statistics, which does not depend on the relationship between channels and has strong applicability. At the same time, constructing the bad channel removal factor through single-channel data statistics is simple to implement, has low computational load, and meets the needs of real-time microseismic monitoring.

[0042] 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

[0043] 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.

[0044] Figure 1 A flowchart of a microseismic bad path removal method based on data statistics according to an embodiment of the present invention is shown.

[0045] Figure 2 This illustration shows a raw microseismic data profile without removing bad channels in one embodiment of the present invention.

[0046] Figure 3 This invention illustrates a microseismic data profile after removing bad paths, according to one embodiment of the invention.

[0047] Figure 4 The diagram shows a profile of bad-path microseismic data that has been removed in one embodiment of the present invention. Detailed Implementation

[0048] 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.

[0049] Example 1

[0050] like Figure 1 As shown, this embodiment provides a microseismic bad path removal method based on data statistics, including:

[0051] S1: Input microseismic monitoring data;

[0052] Specifically, input microseismic monitoring data, where the number of traces is N, the trace number is j, the number of samples is M, the sample number is i, and the data on the j-th trace and the i-th sample is A. i,j .

[0053] S2: The maximum and minimum values ​​of each channel of microseismic monitoring data;

[0054] Specifically, the maximum and minimum values ​​of each trace of the microseismic monitoring data are calculated using the following formula:

[0055]

[0056] Among them, MAX j MIN represents the maximum value of the monitoring data in channel j. j This represents the minimum value of the monitoring data in the j-th channel.

[0057] S3: Construct bad channel elimination factors for each channel based on the maximum and minimum values ​​of each channel's monitoring data;

[0058] Specifically, the elimination factor G for each channel is calculated and constructed. j The formula for calculating the bad sector rejection factor is as follows:

[0059]

[0060] Among them, G j For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

[0061] S4: Remove bad path data from microseismic monitoring data using bad path removal factors for each path;

[0062] Specifically, bad paths in microseismic monitoring data are removed using a bad path removal factor, calculated as follows:

[0063] A′ i,j =A i,j G j

[0064] Among them, A′ i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

[0065] S5: Output microseismic monitoring data after removing bad paths.

[0066] Specifically, the final output is the microseismic monitoring data A′ after removing bad channels. i,j .

[0067] Example 2

[0068] Figure 2 This is the original microseismic data profile containing bad traces, with 200 traces, 2000 data samples, and a sampling interval of 2 ms. Figure 3 This is a microseismic data profile after bad paths have been removed using the method of this invention. Figure 4 These are the bad channels removed from the microseismic data profile. Test results show that the method of this invention effectively removes the vast majority of bad channels in the microseismic data in this example, thus avoiding misidentification of microseismic signals caused by bad channel data, improving the accuracy of subsequent microseismic source location, and ultimately meeting the needs of real-time microseismic monitoring.

[0069] Example 3

[0070] This embodiment provides a microseismic bad path removal device based on data statistics, including:

[0071] The data input module is used to input microseismic monitoring data;

[0072] The data statistics module is used to calculate the maximum and minimum values ​​of each channel of the microseismic monitoring data.

[0073] The bad sector rejection factor construction module is used to construct bad sector rejection factors for each channel based on the maximum and minimum values ​​of the monitoring data for each channel.

[0074] The bad path data removal module is used to remove bad path data from microseismic monitoring data by using bad path removal factors for each path.

[0075] The data output module is used to output microseismic monitoring data after removing bad paths.

[0076] In this embodiment, the maximum and minimum values ​​of each channel of the microseismic monitoring data are calculated using the following formula:

[0077]

[0078] Where j is the seismic trace number, i is the sample point number in the seismic trace, M is the number of sample points in the seismic trace, and MAX is the maximum number of sample points in the seismic trace. j MIN represents the maximum value of the monitoring data in channel j. j Let A be the minimum value of the monitoring data in channel j. i,j This refers to the data at the j-th channel and the i-th sample point.

[0079] In this embodiment, the formula for calculating the bad sector rejection factor is as follows:

[0080]

[0081] Among them, Gj For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

[0082] In this embodiment, bad path data in microseismic monitoring data is removed using the following calculation formula:

[0083] A′ i,j =A i,j G j

[0084] Among them, A′ i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

[0085] Example 4

[0086] This embodiment provides an electronic device, the electronic device comprising:

[0087] At least one processor; and,

[0088] A memory communicatively connected to the at least one processor; wherein,

[0089] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the data statistics-based microseismic bad path removal method described in the above embodiments.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0094] Example 5

[0095] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the microseismic bad path removal method based on data statistics described in the above embodiment.

[0096] 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.

[0097] 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).

[0098] 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 removing microseismic bad paths based on data statistics, characterized in that, include: Input microseismic monitoring data; The maximum and minimum values ​​of each channel of microseismic monitoring data were statistically analyzed. A bad channel elimination factor for each channel is constructed based on the maximum and minimum values ​​of each channel's monitoring data. Bad path data in microseismic monitoring data is removed by using various bad path removal factors; Output microseismic monitoring data after removing bad paths.

2. The microseismic bad path removal method based on data statistics according to claim 1, characterized in that, The maximum and minimum values ​​of each channel of the microseismic monitoring data are calculated using the following formula: Where j is the seismic trace number, i is the sample point number in the seismic trace, M is the number of sample points in the seismic trace, and MAX is the maximum number of sample points in the seismic trace. j MIN represents the maximum value of the monitoring data in channel j. j Let A be the minimum value of the monitoring data in channel j. i,j This refers to the data at the j-th channel and the i-th sample point.

3. The microseismic bad path removal method based on data statistics according to claim 2, characterized in that, The formula for calculating the bad sector rejection factor is as follows: Among them, G j For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

4. The microseismic bad path removal method based on data statistics according to claim 3, characterized in that, Bad paths in microseismic monitoring data are removed using the following calculation formula: A ′ i,j =A i,j G j Among them, A ′ i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

5. 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, which, when executed by the at least one processor, enables the at least one processor to perform the data statistics-based microseismic bad path removal method according to any one of claims 1-4.

6. 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 data statistics-based microseismic bad path removal method as described in any one of claims 1-4.

7. A microseismic bad path removal device based on data statistics, characterized in that, include: The data input module is used to input microseismic monitoring data; The data statistics module is used to calculate the maximum and minimum values ​​of each channel of the microseismic monitoring data. The bad sector rejection factor construction module is used to construct bad sector rejection factors for each channel based on the maximum and minimum values ​​of the monitoring data for each channel. The bad path data removal module is used to remove bad path data from microseismic monitoring data by using bad path removal factors for each path. The data output module is used to output microseismic monitoring data after removing bad paths.

8. The microseismic bad path removal device based on data statistics according to claim 7, characterized in that, The maximum and minimum values ​​of each channel of the microseismic monitoring data are calculated using the following formula: Where j is the seismic trace number, i is the sample point number in the seismic trace, M is the number of sample points in the seismic trace, and MAX is the maximum number of sample points in the seismic trace. j MIN represents the maximum value of the monitoring data in channel j. j Let A be the minimum value of the monitoring data in channel j. i,j This refers to the data at the j-th channel and the i-th sample point.

9. The microseismic bad path removal device based on data statistics according to claim 8, characterized in that, The formula for calculating the bad sector rejection factor is as follows: Among them, G j For each elimination factor, A j This represents the monitoring data of the j-th trace, N is the number of seismic traces, and TH represents the rejection threshold.

10. The microseismic bad path removal device based on data statistics according to claim 9, characterized in that, Bad paths in microseismic monitoring data are removed using the following calculation formula: A ′ i,j =A i,j G j Among them, A ′ i,j This refers to the data at the j-th track and the i-th sample point after removing bad tracks.

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