Distributed fiber optic acoustic wave sensing bandband energy extraction method, device and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for extracting frequency band energy from distributed optical fiber acoustic wave sensors, belonging to the field of optical fiber sensing technology. The method includes: acquiring raw DAS data generated during hydraulic fracturing operation monitoring; segmenting the raw DAS data according to a target time length to obtain segmented DAS data; performing a Fourier transform on the segmented DAS data to obtain Fourier transformed DAS data; truncating the Fourier transformed DAS data according to a target frequency band range to obtain a frequency band signal within the target frequency band range; averaging the frequency band signal within the target frequency band range according to a signal downsampling coefficient to obtain a segmented averaged frequency band signal; calculating the mean square value of the segmented averaged frequency band signal, and using the mean square value as the frequency band energy of the frequency band signal. This application's solution can improve the extraction efficiency of frequency band energy.

Description

Technical Field

[0001] This application relates to the field of fiber optic sensing technology, specifically to a method, apparatus, and electronic device for extracting frequency band energy from distributed fiber optic acoustic wave sensing. Background Technology

[0002] Currently, in the oil and gas sector, DAS (Distributed Acoustic Sensor) technology has application potential in multiple stages, and various forms of application exploration have been carried out. Therefore, frequency division processing of the raw DAS data according to the signal frequency characteristics and application requirements is an important step, namely frequency band energy (FBE) extraction.

[0003] In the existing technology, FBE extraction of raw DAS data requires the following three steps: downsampling the raw DAS data, bandpass filtering the downsampled DAS data, and obtaining the FBE value after downsampling.

[0004] However, the above methods have low efficiency in extracting bandwidth energy. Therefore, how to improve the efficiency of bandwidth energy extraction remains an unsolved problem. Summary of the Invention

[0005] The purpose of this application is to provide a distributed fiber optic acoustic wave sensing band energy extraction method, which can solve the problem of low efficiency in the extraction of band energy in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for extracting frequency band energy from a distributed optical fiber acoustic wave sensor, the method comprising: Acquire raw DAS data generated during the monitoring of hydraulic fracturing operations; The original DAS data is segmented according to the target time length to obtain the segmented DAS data. Perform a Fourier transform on the segmented DAS data to obtain the Fourier transformed DAS data. Based on the target frequency band range, the frequency band of the DAS data after Fourier transform is trunculated to obtain the frequency band signal within the target frequency band range; Based on the signal downsampling coefficient, the frequency band signal within the target frequency band range is segmented and averaged to obtain the segmented averaged frequency band signal; Calculate the mean square value of the frequency band signal after segmented averaging, and use the mean square value as the frequency band energy of the frequency band signal.

[0007] In one possible implementation of the first aspect, the raw DAS data includes multiple DAS data files, each containing N distance channels and M sampling time points, and the raw DAS data is a two-dimensional matrix. , express OK, express The list, , The original DAS data is segmented according to the target time length to obtain segmented DAS data, including: The distance channels of each DAS data file are segmented along the time dimension to obtain the segmented DAS data. The specific expression is as follows:

[0008] in, This represents the DAS data after signal segmentation. This indicates the number of signal segments after signal division. , This indicates the total duration of the data contained in each DAS data file. , This indicates the total distance of data contained in each DAS data file. , , Indicates the target time length. Indicates time resolution.

[0009] In one possible implementation of the first aspect, a Fourier transform is performed on the segmented DAS data to obtain the Fourier-transformed DAS data, including: right Perform a Fourier transform on each row and each signal segment. The expression for the number of points in the Fourier transform is as follows:

[0010] in, Indicates the number of points in the Fourier transform; The expression for the DAS data after Fourier transform is as follows:

[0011] in, This represents the DAS data after Fourier transform. express OK, Indicates to conduct Point Fourier transform operation, It represents the imaginary unit.

[0012] In one possible implementation of the first aspect, the target frequency band range is , This represents the minimum value of the target frequency band range. This represents the maximum value within the target frequency band range. Based on the target frequency band range, the frequency band of the Fourier-transformed DAS data is truncated to obtain the frequency band signal within the target frequency band range, including: right The frequency band of the DAS data after Fourier transform is trunculated to obtain... The frequency band signal within; Among them, matrix The first dimension is the frequency dimension, and the expression for the frequency resolution in this dimension is as follows:

[0013] in, Indicates frequency resolution in the frequency dimension; For matrix The expression for the index in its first dimension is as follows:

[0014]

[0015] in, express The corresponding index, express The corresponding index, This represents the rounding function.

[0016] In one possible implementation of the first aspect, the signal downsampling coefficient is the segment length; based on the signal downsampling coefficient, the frequency band signal within the target frequency band is segmented and averaged to obtain the segmented averaged frequency band signal, including: Based on the segment length, on the first dimension The data between segments is averaged to obtain the averaged frequency band signal. The specific calculation formula is as follows:

[0017] in, This represents the frequency band signal after segmented averaging. express OK, Indicates the segment length.

[0018] In one possible implementation of the first aspect, calculating the mean square value of the segmented averaged frequency band signal and using the mean square value as the frequency band energy of the frequency band signal includes: The bandwidth energy of the frequency band signal can be calculated using the following formula:

[0019] in, Indicates frequency band energy. This represents the mean square value.

[0020] In one possible implementation of the first aspect, after calculating the mean square value of the segmented averaged frequency band signal and using the mean square value as the frequency band energy of the frequency band signal, the method further includes: A two-dimensional median filter is used to remove fading noise from the frequency band energy. The expression for the frequency band energy sequence after noise removal is as follows:

[0021] in, Indicates the frequency band energy sequence. Represents the median filtering function. This represents the window size of the median filter in the time dimension. This indicates the window size of the median filter in the spatial dimension.

[0022] Secondly, embodiments of this application provide a distributed fiber optic acoustic wave sensing bandband energy extraction device, the device comprising: The acquisition unit is used to acquire raw DAS data generated during the monitoring of hydraulic fracturing operations; The signal segmentation unit is used to segment the original DAS data according to the target time length to obtain the segmented DAS data. The Fourier transform unit is used to perform Fourier transform on the DAS data after signal segmentation to obtain the Fourier transformed DAS data. The frequency band truncation unit is used to truncate the Fourier transform DAS data according to the target frequency band range to obtain the frequency band signal within the target frequency band range; The segmented averaging unit is used to segment and average the frequency band signal within the target frequency band range according to the signal downsampling coefficient, so as to obtain the segmented averaged frequency band signal. The energy calculation unit is used to calculate the mean square value of the frequency band signal after segmented averaging, and uses the mean square value as the frequency band energy of the frequency band signal.

[0023] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the distributed fiber optic acoustic wave sensing band energy extraction method described in any of the first aspects above.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the distributed fiber optic acoustic wave sensing band energy extraction method of any one of the first aspects described above.

[0025] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the distributed fiber optic acoustic wave sensing band energy extraction method described in any of the first aspects above.

[0026] The proposed solution first segments the acquired raw DAS data into signals, then performs a Fourier transform on the segmented DAS data, then performs frequency band truncation on the Fourier transformed DAS data, then performs segmented averaging on the frequency band signal within the target frequency band range, and finally uses the mean square value of the calculated segmented averaged frequency band signal as the frequency band energy of the frequency band signal.

[0027] This application's solution can simultaneously complete the downsampling process and DAS signal energy extraction within a specific frequency range in the frequency domain, eliminating the need for downsampling and bandpass filter parameter design. It also eliminates the need to consider the data continuity between adjacent DAS files, simplifying the signal processing of DAS data, accelerating its data processing speed, and thus improving the efficiency of frequency band energy extraction. It has strong ease of use and practicality.

[0028] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the steps of the distributed optical fiber acoustic wave sensing band energy extraction method provided in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the specific process of the DAS data FBE extraction method provided in the embodiments of this application; Figure 3 This is a schematic diagram of a signal waterfall provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the distributed optical fiber acoustic wave sensing band energy extraction device provided in the embodiments of this application; Figure 5This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0031] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0032] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or photovoltaic modules, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, photovoltaic modules and / or combinations thereof.

[0033] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0036] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0037] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in some other embodiments," "in other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0038] DAS utilizes Rayleigh Backward Scattering (RBS) in optical fibers to continuously acquire vibration information along the fiber with a spatial resolution of 1m to 10m. The total sensing distance can reach more than 10 km. It also has advantages such as high temperature resistance, electromagnetic interference resistance, and convenient deployment.

[0039] The intensity and phase of the received Rayleigh scattered light are altered by vibration signals along the optical fiber. Various modulation and demodulation schemes can be used to recover the intensity and phase information carried in the Rayleigh scattering beam (RBS). In particular, the phase information is linearly related to the vibration experienced at various points along the fiber. Since pulsed light is emitted, the vibration information at each point along the fiber can be derived from the correspondence between the received time and location.

[0040] Thanks to the many advantages of DAS technology, it has been widely applied in various fields such as seismic wave detection, perimeter security, underwater acoustic monitoring, and oil and gas resource exploration and development. In particular, in the oil and gas field, DAS technology has application potential in many aspects and has been explored in various forms, including: (1) monitoring wellbore activity during production enhancement operations; (2) monitoring fluids during fracturing and production processes; (3) monitoring microseismic events during fracturing; and (4) monitoring of strain at the well site.

[0041] Different application scenarios require targeted signal processing of the raw DAS data because different types of events occupy different main frequency ranges of the signal. For example, the signal frequency of fluid flow signals in the fracturing process is concentrated in the tens to hundreds of Hz; for strain monitoring in the well, the signal is concentrated in the low frequency part, and existing studies often limit it to below 0.05 Hz.

[0042] Therefore, frequency division processing of the original DAS data, based on the signal frequency characteristics and application requirements, is an important step, namely, frequency band energy extraction.

[0043] FBE processing of DAS signals can further improve the signal-to-noise ratio of the target signal, laying the foundation for the next step of combining data with field fracturing curves, distributed temperature sensors (DTS), and other data to evaluate the effectiveness of oilfield operations.

[0044] On the other hand, raw DAS data is often quite large. For example, for a DAS configuration with 2000 channels and a 10 kHz sampling rate, one minute of data can exceed 1 GB. FBE processing can significantly compress the DAS data volume. For the DAS data in the example above, if the energy of the 50-100 Hz frequency band is extracted, the data volume can be compressed by at least 20 times, which is highly beneficial for real-time analysis of DAS monitoring results.

[0045] Therefore, researching the FBE extraction method for DAS data in the oilfield field is essential, serving as the foundation and prerequisite for further analysis and interpretation of DAS data. Simultaneously, this method can also compress DAS data, greatly benefiting its real-time processing and interpretation. Finally, the FBE extraction method discussed here also holds promise for application in other scenarios, such as seismic wave detection and underwater acoustic signal monitoring, where vibration signals exhibit significant frequency domain characteristics.

[0046] Currently, FBE extraction from raw DAS data requires the following three steps: (1) Downsampling the original DAS data: In order to ensure the most comprehensive DAS data features, the original DAS data sampling rate is often high. For example, for a 10 km long DAS optical cable, the original sampling rate is generally 10 kHz. However, in most application scenarios, the frequency of the vibration events of interest is mostly below 1 kHz.

[0047] Such a high sampling rate leads to information redundancy, increasing the burden of subsequent data processing and hindering further data analysis and real-time implementation. Therefore, the original DAS data needs to be downsampled. Based on experience, downsampling to 2 kHz or 4 kHz is generally reasonable. It is important to note that an anti-aliasing filter should be used before downsampling to prevent spectral aliasing during the downsampling process.

[0048] (2) Bandpass filtering of downsampled DAS data: Design a targeted bandpass filter based on the event frequency characteristics of the application scenario. Note that a finite impulse response (FIR) filter is used here to ensure that the DAS waveform is not distorted. The bandpass filtered DAS data is obtained by convolving the downsampled DAS data with the coefficients of the designed bandpass filter.

[0049] (3) Obtain the FBE value after downsampling: Calculate the energy value of the DAS data after bandpass filtering according to the required output time length (e.g., 1 s, 10 s), and the FBE value of the DAS data can be extracted. Plot the FBE values ​​of each DAS channel according to the time series to obtain the signal waterfall diagram under a specific FBE. Further analysis can obtain information such as flow rate during DAS monitoring of downhole fracturing.

[0050] The above process is a traditional method for extracting FBE from DAS raw data. The main problems are as follows: (1) The process is cumbersome: additional downsampling processing is required for the original DAS, which increases the complexity and running time of the processing; (2) The bandpass filter needs to be designed reasonably: the bandpass filter is the core of the above traditional FBE extraction method. Its parameters need to be comprehensively considered and carefully designed, such as passband cutoff frequency, stopband cutoff frequency, passband ripple amplitude, stopband attenuation, etc.; (3) The data continuity between adjacent files needs to be considered.

[0051] The convolution operation between the bandpass filter and the downsampled DAS data will introduce some unusable data at the beginning and end of each DAS data segment. The length of this unusable data is related to the order of the designed bandpass filter. When processing multiple files, the data continuity between adjacent files needs to be carefully considered; otherwise, the amount of unusable data will increase, affecting the effectiveness of FBE extraction. However, this operation will also increase the complexity of the entire processing flow.

[0052] To address the aforementioned deficiencies, this application provides a method for extracting the frequency band energy of a distributed optical fiber acoustic wave sensor. First, the acquired raw DAS data is segmented. Second, the segmented DAS data undergoes a Fourier transform. Third, the Fourier-transformed DAS data is truncated. Then, the frequency band signal within the target frequency band is averaged segmentally. Finally, the mean square value of the calculated segmented averaged frequency band signal is used as the frequency band energy of the signal.

[0053] This application's solution can simultaneously complete the downsampling process and DAS signal energy extraction within a specific frequency range in the frequency domain, eliminating the need for downsampling and bandpass filter parameter design. It also eliminates the need to consider the data continuity between adjacent DAS files, simplifying the signal processing of DAS data, accelerating its data processing speed, and thus improving the efficiency of frequency band energy extraction. It has strong ease of use and practicality.

[0054] The specific process implemented in this application is described below through specific embodiments.

[0055] Please see Figure 1 , Figure 1This is a schematic diagram illustrating the steps of the distributed optical fiber acoustic wave sensing band energy extraction method provided in this application embodiment. For example... Figure 1 As shown, the method may include the following steps: S101, acquire raw DAS data generated during the monitoring of hydraulic fracturing operations.

[0056] In some embodiments, the data acquired by the DAS system can be viewed as a two-dimensional matrix. , express OK, express The list, , The row direction represents the time dimension, and the column direction represents the spatial dimension.

[0057] Each row represents a sampling time point, and the interval between sampling times of two adjacent rows is the time resolution of the DAS, denoted as . Each column represents the RBS phase information obtained at a certain spatial location in the DAS fiber. The distance between two adjacent columns is the spatial resolution of the DAS, denoted as . .

[0058] Suppose that the total duration of the data contained in a DAS raw data file to be processed is... The total distance is Then we have:

[0059]

[0060] For example, a DAS device, paired with a 5000 m long optical fiber, can be used to monitor fracturing operations. Assume each data file stores all the phase data demodulated from the 5000 m fiber, corresponding to a time interval of 1 minute. If the spatial resolution Δz = 1 m and the temporal resolution Δt = 0.1 ms, then the file contains a total of 5000 distance channels (…). N = 5000), with 6×10 in each channel. 5 Each sampling time point ( M = 6×10 5 ).

[0061] S102, the original DAS data is segmented according to the target time length to obtain the segmented DAS data.

[0062] According to one embodiment of this application, the original DAS data includes multiple DAS data files, each DAS data file containing: N distance channels and M sampling time points; the original DAS data is segmented according to the target time length to obtain segmented DAS data, including: The distance channels of each DAS data file are segmented along the time dimension to obtain the segmented DAS data. The specific expression is as follows:

[0063] in, This represents the DAS data after signal segmentation. This indicates the number of signal segments after signal division. , This indicates the total duration of the data contained in each DAS data file. , This indicates the total distance of data contained in each DAS data file. , , Indicates the target time length. Indicates time resolution.

[0064] Under the above DAS system configuration, L = 60. =1×10 4 , the initial φ(6×10 5 A two-dimensional matrix of size 5000 is transformed into a φsec(1×10) matrix. 4 A three-dimensional matrix (5000, 60).

[0065] S103 performs a Fourier transform on the DAS data after signal segmentation to obtain the Fourier transformed DAS data.

[0066] In one embodiment, the Fourier transform is the FFT (Fast Fourier Transform).

[0067] According to one embodiment of this application, a Fourier transform is performed on the DAS data after signal segmentation to obtain the Fourier transformed DAS data, including: right Perform a Fourier transform on each row and each signal segment. The expression for the number of points in the Fourier transform is as follows:

[0068] in, This indicates the number of points in the Fourier transform; in one embodiment, = 2 14= 16384. This expression guarantees that the number of points used for the FFT transformation is a power of 2, and that the time-domain data used for the FFT transformation is not truncated, thus ensuring the efficiency of the FFT operation and preventing spectral leakage.

[0069] The expression for the DAS data after Fourier transform is as follows:

[0070] in, This represents the DAS data after Fourier transform. express OK, Indicates to conduct Point Fourier transform operation, It represents the imaginary unit.

[0071] S104. Based on the target frequency band range, the frequency band of the Fourier transform-derived DAS data is trunculated to obtain the frequency band signal within the target frequency band range.

[0072] According to one embodiment of this application, the target frequency band range is: , This represents the minimum value of the target frequency band range. This represents the maximum value within the target frequency band range. Based on the target frequency band range, the frequency band of the Fourier-transformed DAS data is truncated to obtain the frequency band signal within the target frequency band range, including: right The frequency band of the DAS data after Fourier transform is trunculated to obtain... The frequency band signal within; Among them, matrix The first dimension is the frequency dimension, and the expression for the frequency resolution in this dimension is as follows:

[0073] in, This represents the frequency resolution in the frequency dimension; after DAS data is transformed from the time domain to the frequency domain using an FFT, the matrix... The first dimension is the frequency dimension. In one embodiment, Δf = 0.61 Hz.

[0074] For matrix The expression for the index in its first dimension is as follows:

[0075]

[0076] in, express The corresponding index, express The corresponding index, This represents the rounding function.

[0077] S105, based on the signal downsampling coefficient, the frequency band signal within the target frequency band range is segmented and averaged to obtain the segmented averaged frequency band signal.

[0078] In one embodiment, due to the existence of downsampling requirements, for In the first dimension Not all data points are needed; the data can be averaged in segments.

[0079] According to one embodiment of this application, the signal downsampling coefficient is the segment length; based on the signal downsampling coefficient, the frequency band signal within the target frequency band is segmented and averaged to obtain the segmented averaged frequency band signal, including: Based on the segment length, on the first dimension The data between segments is averaged to obtain the averaged frequency band signal. The specific calculation formula is as follows:

[0080] in, This represents the frequency band signal after segmented averaging. express OK, Indicates the segment length.

[0081] S106, calculate the mean square value of the frequency band signal after segmented averaging, and use the mean square value as the frequency band energy of the frequency band signal.

[0082] According to one embodiment of this application, calculating the mean square value of the segmented averaged frequency band signal and using the mean square value as the frequency band energy of the frequency band signal includes: The bandwidth energy of the frequency band signal can be calculated using the following formula:

[0083] in, Indicates frequency band energy. This represents the mean square value. The formula expresses the idea of ​​transforming energy calculations, which would normally be performed in the time domain, into operations in the frequency domain. The underlying principle is Passevar's theorem.

[0084] According to one embodiment of this application, after calculating the mean square value of the segmented averaged frequency band signal and using the mean square value as the frequency band energy of the frequency band signal, the method further includes: A two-dimensional median filter is used to remove fading noise from the frequency band energy. The expression for the frequency band energy sequence after noise removal is as follows:

[0085] in, Indicates the frequency band energy sequence. This represents the median filtering function, which searches a two-dimensional array. The middle value arranged by size. This represents the window size of the median filter in the time dimension. This indicates the window size of the median filter in the spatial dimension, such as 3×3, 5×5, etc.

[0086] In some embodiments, a three-dimensional matrix Converted into a two-dimensional energy signal matrix The data size is L×N. Due to the inherent fading characteristics of DAS data, the FBE energy waterfall plot obtained at this time still contains some fading noise. This fading noise can be filtered out using a two-dimensional median filter to obtain the final FBE sequence.

[0087] This application provides a simple and easy-to-implement method for DAS data FBE extraction, including data processing principles and procedures. Using the proposed DAS data FBE extraction method, the downsampling process and DAS signal energy extraction within a specific frequency range can be completed simultaneously in the frequency domain, eliminating the need for downsampling and bandpass filter parameter design.

[0088] Please see Figure 2 , Figure 2 This is a schematic diagram illustrating the specific process of the DAS data FBE extraction method provided in the embodiments of this application. For example... Figure 2 As shown, the method includes the following steps: First, raw DAS data is obtained through hydraulic fracturing monitoring operations. Note that the signal sampling rate is often high at this time. Next, the FBE signal is extracted by performing six steps on the raw DAS data: signal segmentation, fast Fourier transform, frequency band truncation, piecewise averaging, energy calculation, and median filtering.

[0089] The signal segmentation process involves dividing the original signal into segments according to the required FBE time length, based on actual needs. The segmented signals are then transferred to the frequency domain, typically using an FFT algorithm. Next, the start and end frequencies in the frequency domain are determined based on the required bandwidth, thus dividing the frequency band signal. Then, based on the signal downsampling coefficients, segmented averaging is performed on the signal to further reduce computational complexity. The mean square value is calculated for the averaged data points within the extracted frequency band, representing its band energy. Finally, the results contain some fading noise along the time and distance axes; this fading noise is filtered out using a median filter.

[0090] This application provides a simple and easy-to-implement method that achieves real-time monitoring and analysis by performing downsampling and signal energy extraction within a specific frequency range in the frequency domain. This is of great significance for oilfield hydraulic fracturing monitoring and other fields that require real-time monitoring of underground structures.

[0091] Please see Figure 3 , Figure 3 This is a schematic diagram of a signal waterfall provided in an embodiment of this application. Figure 3 Figure (a) in the diagram is a waterfall plot of the raw DAS signal. Figure 3 Figure (b) is a waterfall plot of the FBE sequence of DAS data after the method proposed in the embodiments of this application.

[0092] In a hydraulic fracturing operation at an oilfield site, DAS was used to monitor the downhole hydraulic fracturing process. The raw data had a frequency band of 0-5 kHz, a distance resolution of Δz ≈ 1 m, and a time resolution of Δt = 0.1 ms. The FBE sequence was calculated at 10-second output time intervals, with the frequency band limited to 50-200 Hz. A downsampling factor K = 5 was used. The resulting raw DAS data waterfall plot is shown below. Figure 3 As shown in Figure (a), the processed FBE sequence is as follows: Figure 3 As shown in Figure (b).

[0093] It can be clearly seen that after processing by the method proposed in this application embodiment, multiple fracturing signals are clearly displayed within a depth range of 4550 m to 4750 m. The solution of this application simplifies the data processing flow, uses signal processing technology to extract the frequency band energy within a specific frequency range in the DAS data in the frequency domain, and simultaneously completes the downsampling process, avoiding complex parameter design and data continuity issues.

[0094] This makes the monitoring process more efficient and reduces the time and cost required for manual processing. The proposed solution accelerates data processing; in practical applications, the method extracts the required FBE signal from raw DAS data through a series of steps, improving processing speed. This is crucial for applications requiring rapid response to changes in subsurface structures, such as real-time optimization in oilfield hydraulic fracturing monitoring.

[0095] The method proposed in this application eliminates the need to consider data continuity issues between adjacent DAS files, simplifying the signal processing of DAS data and accelerating its processing speed. Furthermore, by setting the downsampling coefficient K, this application allows for flexible adjustment of the output time interval, making the monitoring results more consistent with actual needs.

[0096] In oilfield hydraulic fracturing monitoring, the proposed solution can clearly display multi-stage fracturing signals, providing important reference for optimizing the fracturing process. This helps engineers better understand the downhole situation, adjust operations in a timely manner, and improve production efficiency and resource utilization.

[0097] In addition to improving the efficiency and accuracy of hydraulic fracturing monitoring in the oilfield industry, the proposed solution also has broad application prospects. It can be applied to fields where vibration signals have obvious frequency domain characteristics, such as seismic wave detection and underwater acoustic signal monitoring, providing reliable monitoring technology support for engineering construction and resource development.

[0098] The distributed optical fiber acoustic wave sensing frequency band energy extraction method provided in this application firstly segments the acquired raw DAS data into signals, then performs Fourier transform on the segmented DAS data, then performs frequency band truncation on the Fourier transformed DAS data, then performs segmented averaging on the frequency band signal within the target frequency band range, and finally uses the mean square value of the calculated segmented averaged frequency band signal as the frequency band energy of the frequency band signal.

[0099] This application's solution can simultaneously complete the downsampling process and DAS signal energy extraction within a specific frequency range in the frequency domain, eliminating the need for downsampling and bandpass filter parameter design. It also eliminates the need to consider the data continuity between adjacent DAS files, simplifying the signal processing of DAS data, accelerating its data processing speed, and thus improving the efficiency of frequency band energy extraction. It has strong ease of use and practicality.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0101] Corresponding to the method in the above embodiments, Figure 4This is a schematic diagram of the distributed fiber optic acoustic wave sensing band energy extraction device provided in an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown.

[0102] Reference Figure 4 The device includes: Acquisition unit 401 is used to acquire raw DAS data generated during the monitoring of hydraulic fracturing operations; The signal segmentation unit 402 is used to segment the original DAS data according to the target time length to obtain the segmented DAS data. Fourier transform unit 403 is used to perform Fourier transform on the DAS data after signal segmentation to obtain the Fourier transformed DAS data. The frequency band truncation unit 404 is used to truncate the frequency band of the Fourier transform DAS data according to the target frequency band range to obtain the frequency band signal within the target frequency band range; The segmented averaging unit 405 is used to perform segmented averaging on the frequency band signal within the target frequency band range according to the signal downsampling coefficient, so as to obtain the segmented averaged frequency band signal. The energy calculation unit 406 is used to calculate the mean square value of the frequency band signal after segmented averaging, and uses the mean square value as the frequency band energy of the frequency band signal.

[0103] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0105] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of the electronic device 5 provided in an embodiment of this application. Figure 5 As shown, the electronic device 5 of this embodiment includes: at least one processor 501 ( Figure 5 Only one is shown in the diagram), memory 503, and computer program 502 stored in memory 503 and executable on at least one processor 501, wherein processor 501 executes computer program 502 to implement the steps in the above method embodiments.

[0106] Electronic device 5 can be a desktop computer, laptop, handheld computer, or mobile phone, etc. This electronic device 5 may include, but is not limited to, a processor 501 and a memory 503. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0107] The processor 501 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware photovoltaic modules, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0108] In some embodiments, memory 503 may be an internal storage unit of electronic device 5, such as a hard disk or memory of electronic device 5. In other embodiments, memory 503 may be an external storage device of electronic device 5, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on electronic device 5. Furthermore, memory 503 may include both internal and external storage units of electronic device 5. Memory 503 is used to store operating system, application programs, boot loader, data, and other programs, such as program code of computer programs. Memory 503 may also be used to temporarily store data that has been output or will be output.

[0109] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, when implementing all or part of the processes in the methods of the above embodiments of this application, it can be accomplished by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps applied to the method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a computing device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk. In some jurisdictions, according to legislation and patent practice, a computer-readable storage medium cannot be an electrical carrier signal or a telecommunication signal.

[0110] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various method embodiments described above.

[0111] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the steps described in the various method embodiments above.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. The device / electronic device embodiments described above are merely illustrative, and the division of modules or units described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or photovoltaic modules may be combined or integrated into another system, and some features may be ignored. Furthermore, the indirect coupling, direct coupling, or communication connection shown or discussed may be through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0115] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the above embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A distributed fiber optic acoustic wave sensing band energy extraction method, characterized by, The method includes: Acquire raw DAS data generated during the monitoring of hydraulic fracturing operations; The original DAS data is segmented according to the target time length to obtain the segmented DAS data. Perform a Fourier transform on the segmented DAS data to obtain the Fourier transformed DAS data. Based on the target frequency band range, the frequency band of the DAS data after Fourier transform is trunculated to obtain the frequency band signal within the target frequency band range; Based on the signal downsampling coefficient, the frequency band signal within the target frequency band range is segmented and averaged to obtain the segmented averaged frequency band signal; Calculate the mean square value of the frequency band signal after segmented averaging, and use the mean square value as the frequency band energy of the frequency band signal.

2. The distributed optical fiber acoustic wave sensing band energy extraction method according to claim 1, characterized in that, The original DAS data includes a plurality of DAS data files, each DAS data file containing: N a distance channel and M a sampling time point, the original DAS data being a two-dimensional matrix , representing a row of a column of , , ; The original DAS data is segmented according to the target time length to obtain segmented DAS data, including: The distance channels of each DAS data file are segmented along the time dimension to obtain the segmented DAS data. The specific expression is as follows: wherein, represents the DAS data after signal division, represents the number of signal segments after signal division, represents the total length of data time contained in each DAS data file, represents the total length of data distance contained in each DAS data file, represents the target time length, represents the time resolution.​​​​ 3. The distributed optical fiber acoustic wave sensing band energy extraction method according to claim 2, characterized in that, Perform a Fourier transform on the segmented DAS data to obtain the Fourier transformed DAS data, including: right Perform a Fourier transform on each row and each signal segment. The expression for the number of points in the Fourier transform is as follows: in, Indicates the number of points in the Fourier transform; The expression for the DAS data after Fourier transform is as follows: in, This represents the DAS data after Fourier transform. express OK, Indicates to conduct Point Fourier transform operation, It represents the imaginary unit.

4. The distributed fiber optic acoustic wave sensing band energy extraction method according to claim 3, characterized in that, The target frequency band range is , This represents the minimum value of the target frequency band range. Indicates the maximum value of the target frequency band range; Based on the target frequency band range, the frequency band of the Fourier transform-derived DAS data is trunculated to obtain the frequency band signal within the target frequency band range, including: right The frequency band of the DAS data after Fourier transform is trunculated to obtain... The frequency band signal within; Among them, matrix The first dimension is the frequency dimension, and the expression for the frequency resolution in this dimension is as follows: in, Indicates frequency resolution in the frequency dimension; For matrix The expression for the index in its first dimension is as follows: in, express The corresponding index, express The corresponding index, This represents the rounding function.

5. The distributed optical fiber acoustic wave sensing band energy extraction method according to claim 4, characterized in that, The signal downsampling coefficient is the segment length; based on the signal downsampling coefficient, the frequency band signal within the target frequency band is segmented and averaged to obtain the segmented averaged frequency band signal, including: Based on the segment length, on the first dimension The data between segments is averaged to obtain the averaged frequency band signal. The specific calculation formula is as follows: in, This represents the frequency band signal after segmented averaging. express OK, Indicates the segment length.

6. The distributed optical fiber acoustic wave sensing band energy extraction method according to claim 5, characterized in that, Calculating the mean square value of the frequency band signal after segmented averaging, and using the mean square value as the frequency band energy of the frequency band signal, includes: The bandwidth energy of the frequency band signal can be calculated using the following formula: in, Indicates frequency band energy. This represents the mean square value.

7. The distributed optical fiber acoustic wave sensing band energy extraction method according to claim 6, characterized in that, After calculating the mean square value of the segmented averaged frequency band signal and using the mean square value as the frequency band energy of the frequency band signal, the method further includes: A two-dimensional median filter is used to remove fading noise from the frequency band energy. The expression for the frequency band energy sequence after noise removal is as follows: in, Indicates the frequency band energy sequence. Represents the median filtering function. This represents the window size of the median filter in the time dimension. This indicates the window size of the median filter in the spatial dimension.

8. A distributed fiber optic acoustic wave sensing bandband energy extraction device, characterized in that, The device includes: The acquisition unit is used to acquire raw DAS data generated during the monitoring of hydraulic fracturing operations; The signal segmentation unit is used to segment the original DAS data according to the target time length to obtain the segmented DAS data. The Fourier transform unit is used to perform Fourier transform on the DAS data after signal segmentation to obtain the Fourier transformed DAS data. The frequency band truncation unit is used to truncate the Fourier transform DAS data according to the target frequency band range to obtain the frequency band signal within the target frequency band range; The segmented averaging unit is used to segment and average the frequency band signal within the target frequency band range according to the signal downsampling coefficient, so as to obtain the segmented averaged frequency band signal. The energy calculation unit is used to calculate the mean square value of the frequency band signal after segmented averaging, and to use the mean square value as the frequency band energy of the frequency band signal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the distributed fiber optic acoustic wave sensing band energy extraction method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the distributed fiber optic acoustic wave sensing band energy extraction method according to any one of claims 1-7.

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