Distributed optical fiber acoustic logging signal processing method and related equipment
By extracting noise features and building a model in the shut-in state, and performing real-time noise cancellation in the production state, the problem of insufficient signal-to-noise ratio in complex downhole environments is solved, and the accuracy of logging data is improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-11-20
- Publication Date
- 2026-05-22
AI Technical Summary
Existing technologies struggle to effectively utilize noise characteristics during shut-in operations in complex downhole environments for dynamic noise cancellation, resulting in insufficient signal-to-noise ratios and impacting the accuracy of logging results.
By extracting noise features in the shut-in state and establishing a noise model, and then using an adaptive algorithm to adjust the noise cancellation parameters in the production state, real-time noise cancellation is achieved and signal output is optimized.
It significantly improves the signal-to-noise ratio during production, reduces the impact of environmental noise, and improves the accuracy of logging data, making it particularly suitable for complex downhole environments.
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Figure CN122071938A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of distributed optical fiber logging technology, specifically to a distributed optical fiber acoustic logging signal processing method and related equipment. Background Technology
[0002] Well logging is a crucial technique in oil and gas extraction. With the development of distributed fiber optic acoustic technology (DAS), acquiring downhole vibration and acoustic signals using fiber optic sensors has become increasingly sophisticated. However, the downhole environment is complex and variable, especially during production. Factors such as equipment operation and fluid flow introduce significant noise into the measurement signals, reducing the signal-to-noise ratio and affecting the accuracy of logging results. Traditional noise reduction techniques often struggle to adapt to the complex downhole noise environment and fail to fully utilize the static noise characteristics acquired during well shut-in. Therefore, how to achieve dynamic and effective noise reduction by utilizing the noise characteristics during well shut-in is a pressing issue that needs to be addressed.
[0003] CN201810427365.0 discloses a signal processing method for a distributed optical fiber sensing system based on autocorrelation analysis. This method improves the signal-to-noise ratio of vibration signals by performing autocorrelation calculations on the acquired signals. This invention can effectively solve the problem of identifying weak vibration signals and can detect vibration signals in raw signals with low signal-to-noise ratios.
[0004] CN200810090816.2 A signal processing method for removing known noise from an analyzed signal. Its principle and function are as follows: noise is separated from the analyzed signal using wavelet transform; the separated noise is analyzed and compared with known noise; the noise separation depth is adjusted until the separated noise is similar to or equal to the known noise; then, the separated noise is removed from the analyzed signal, thereby achieving the goal of removing only the known noise while retaining the basic signal and noise signal of the analyzed object.
[0005] The existing technology has the following shortcomings: 1) Lack of prior noise model: Most patents fail to make full use of noise data in the shut-in state for modeling, resulting in less than ideal noise elimination effect in the production state; 2) Limited adaptive capability: The adaptive algorithms used in the existing technology mostly rely on real-time data, and the adjustment in the initial state is slow, especially when the production state changes; 3) Insufficient non-steady-state noise processing capability: Traditional time-frequency analysis methods are prone to performance degradation when processing dynamically changing noise and fail to adapt to complex downhole environments. Summary of the Invention
[0006] The purpose of this invention is to provide a distributed fiber optic acoustic logging signal processing method and related equipment to solve the technical problem in the prior art of how to utilize the noise characteristics of the well shut-in state during production to achieve dynamic and effective noise elimination.
[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a distributed fiber optic acoustic logging signal processing method, comprising: Extract noise characteristics under well shut-in conditions and establish a noise model based on these characteristics; Acquire acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; The noise-removed signal is optimized and then output to complete the distributed fiber optic acoustic logging signal processing.
[0008] Preferably, the specific process of extracting noise features under well-shutdown conditions and establishing a noise model based on these features is as follows: In the shut-in state, distributed fiber optic acoustic sensors are used to acquire acoustic signals. The acquired acoustic signals are converted into the frequency domain through fast Fourier transform, and noise features are extracted in the frequency domain. A noise model is then established based on the noise features.
[0009] Furthermore, noise characteristics include frequency characteristics, amplitude characteristics, and phase characteristics.
[0010] Preferably, the step of acquiring acoustic signals during production and inputting these signals into the noise model for calculation to obtain the noise-removed signal is as follows: In production mode, acoustic signals are acquired using distributed fiber optic acoustic sensors. After detrending processing and frequency domain conversion, the signals are input into the noise model under shut-in conditions for calculation to obtain the noise-removed signal.
[0011] Preferably, the acoustic signal is used within the noise model to adjust the noise cancellation parameters using an adaptive algorithm through minimum mean square error or recursive least squares method to obtain the optimal signal-to-noise ratio.
[0012] Preferably, in the step of optimizing the output of the noise-removed signal, the noise-removed signal is restored to a time-domain signal by inverse Fourier transform, and the time-domain signal is output after filtering and smoothing.
[0013] Secondly, the present invention provides a distributed fiber optic acoustic logging signal processing system, comprising: The model building module is used to extract noise features under well shut-in conditions and build a noise model based on these features. The signal processing module is used to acquire acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; The signal optimization module is used to optimize the noise-removed signal before outputting it, thus completing the distributed fiber optic acoustic logging signal processing.
[0014] Thirdly, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the distributed fiber optic acoustic logging signal processing method described above.
[0015] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the distributed fiber optic acoustic logging signal processing method described above.
[0016] Fifthly, the present invention also provides a computer program product, including computer instructions that instruct a computing device to perform operations corresponding to the distributed fiber optic acoustic logging signal processing method described above.
[0017] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a distributed fiber optic acoustic logging signal processing method. It extracts noise features during well shut-in operation and establishes a noise model based on these features. Acoustic signals during production are acquired and input into the noise model for computation to obtain a noise-removed signal. The noise-removed signal is then optimized and output, completing the distributed fiber optic acoustic logging signal processing. By utilizing the environmental noise features extracted during well shut-in operation, real-time noise cancellation is performed on the acoustic signals during production, significantly improving the signal-to-noise ratio during production, effectively reducing the impact of environmental noise during production, and improving the accuracy of logging data. This method is particularly suitable for acoustic logging in complex downhole environments.
[0018] Furthermore, by extracting noise features and establishing a noise model while the well is shut in, background noise in the downhole environment can be accurately identified and simulated. The acoustic signal collected during production can be effectively processed by the noise model to remove noise components that match the noise model, thereby significantly improving the signal-to-noise ratio. The acoustic signal after noise removal is closer to the real geological information, which significantly improves the accuracy of well logging data.
[0019] Furthermore, this invention, through real-time noise cancellation technology, can dynamically adapt to changes in the downhole noise environment, effectively reducing the impact of these noises on the acoustic signal, making the logging data purer and more accurate. Attached Figure Description
[0020] Figure 1 This is a flowchart of the distributed fiber optic acoustic logging signal processing method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the acoustic data for well opening and closing before applying noise reduction technology in an embodiment of the present invention; Figure 3 This is a schematic diagram of the acoustic data for well opening and well closing after applying noise reduction technology in an embodiment of the present invention; Figure 4 This is a schematic diagram of a distributed fiber optic acoustic logging system in an embodiment of the present invention; Figure 5 This is a schematic diagram of the distributed fiber optic acoustic logging signal processing system in an embodiment of the present invention; In the diagram: 1. Model building module; 2. Signal processing module; 3. Signal optimization module. Detailed Implementation
[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0022] The present invention will now be described in further detail with reference to the accompanying drawings: The purpose of this invention is to provide a distributed fiber optic acoustic logging signal processing method and related equipment to solve the technical problem in the prior art of how to utilize the noise characteristics of the well shut-in state during production to achieve dynamic and effective noise elimination.
[0023] Example 1 See Figure 1 In one embodiment of the present invention, a distributed fiber optic acoustic logging signal processing method is provided, comprising: Step 1: Extract noise features under well shut-in condition and establish a noise model based on the noise features; Specifically, the steps of extracting noise features under well-shutdown conditions and establishing a noise model based on these features are as follows: In the shut-in state, distributed fiber optic acoustic sensors are used to acquire acoustic signals. The acquired acoustic signals are converted into the frequency domain through fast Fourier transform, and noise features are extracted in the frequency domain. A noise model is then established based on the noise features.
[0024] In this embodiment, the acquired sound wave signal is converted from the time domain to the frequency domain using Fast Fourier Transform (FFT). The sound wave is sampled, and the sampling process includes measuring the amplitude of the sound wave's vibrations within equal time intervals. The sampling frequency must be greater than twice the highest frequency of the sound wave to avoid aliasing.
[0025] In this embodiment, an appropriate noise model type, such as a white noise model or a colored noise model, is selected based on the complexity and distribution characteristics of the noise features. The extracted noise features are used to estimate the parameters of the noise model, ensuring that the model accurately reflects the noise environment under shut-in conditions. The accuracy and reliability of the model are verified by comparing the model output with the actual noise signal.
[0026] In this embodiment, the noise characteristics include frequency characteristics, amplitude characteristics, and phase characteristics.
[0027] Step 2: Collect the acoustic signal during production, input the acoustic signal into the noise model for calculation to obtain the signal after noise removal; Specifically, the process of collecting acoustic signals during production and inputting these signals into the noise model for processing to obtain the noise-removed signal is as follows: In production mode, acoustic signals are acquired using distributed fiber optic acoustic sensors. After detrending processing and frequency domain conversion, the signals are input into the noise model under shut-in conditions for calculation to obtain the noise-removed signal.
[0028] In this process, the acoustic signal is used to adjust the noise cancellation parameters within the noise model using an adaptive algorithm, either by minimizing the mean square error or by recursive least squares, to obtain the optimal signal-to-noise ratio.
[0029] The specific process for minimizing the mean square error is as follows: Set the initial coefficients of the filter; Use noisy sound wave signals as input signals; Provide a desired, pure sound wave signal; Calculate the error between the filter output and the desired signal; Based on the error signal and the input signal, adjust the filter coefficients according to the rules of the LMS algorithm; Repeat the above steps until the filter output is close to the desired signal, that is, the mean square value of the error is minimized.
[0030] The specific process of the recursive least squares method is as follows: Set the initial coefficients and associated matrices of the filter; Input a noisy sound wave signal and the desired clean sound wave signal; Calculate the error between the filter output and the desired signal; Update the autocorrelation matrix and cross-correlation vector based on the error signal and the input signal; The filter coefficients are adjusted using the updated matrix and the rules of the RLS algorithm; Repeat the above steps until the filter output is close to the desired signal.
[0031] Step 3: Optimize the noise-removed signal and output it to complete the distributed fiber optic acoustic logging signal processing.
[0032] Specifically, in the step of optimizing the output of the noise-removed signal, the noise-removed signal is restored to a time-domain signal through inverse Fourier transform, and the time-domain signal is output after filtering and smoothing.
[0033] This invention was applied downhole in well 69-1XX of Liaohe Oilfield. To improve the signal-to-noise ratio of logging data, a distributed fiber optic acoustic logging signal processing technology based on noise feature extraction during well shut-in was introduced. Figure 2 As shown, the acoustic data for well opening and shut-in before applying noise reduction technology; according to Figure 3 As shown, this is the acoustic data from well opening and shut-in after applying noise reduction technology; the application of this technology significantly improved the quality of well logging data in this oilfield. After implementation, the signal-to-noise ratio of the oilfield's well logging data increased by more than 20%, and the accuracy of data interpretation was greatly improved. Based on more accurate formation flow information, the oil well can better plan production operations, optimize oil production strategies, and ultimately increase oil and gas production.
[0034] according to Figure 4 As shown, the distributed fiber optic acoustic logging system of this invention includes a downhole fiber optic sensor, a model building module 1, a signal processing module 2, and a signal optimization module 3. First, acoustic signals are acquired using DAS technology under both shut-in and production states. The model building module 1 transmits the signals to the signal processing module 2. Next, the signal processing module 2 analyzes the noise spectrum under shut-in conditions and applies this spectrum to noise removal from the signal under production conditions. An adaptive algorithm dynamically adjusts the noise cancellation parameters to adapt to changes under different operating conditions. Finally, the processed high signal-to-noise ratio signal is output for subsequent analysis.
[0035] In summary, this invention provides a distributed fiber optic acoustic logging signal processing method. It extracts noise features during well shut-in operation and establishes a noise model based on these features. Acoustic signals during production are acquired and input into the noise model for computation to obtain a noise-removed signal. The noise-removed signal is then optimized and output, completing the distributed fiber optic acoustic logging signal processing. By utilizing the environmental noise features extracted during well shut-in operation, real-time noise cancellation is performed on the acoustic signals during production, significantly improving the signal-to-noise ratio during production, effectively reducing the impact of environmental noise during production, and improving the accuracy of logging data. This method is particularly suitable for acoustic logging in complex downhole environments.
[0036] Example 2 according to Figure 5 As shown, the present invention also provides a distributed fiber optic acoustic logging signal processing system, comprising: Model building module 1 is used to extract noise features under well shut-in conditions and build a noise model based on the noise features; Signal processing module 2 is used to collect acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; Signal optimization module 3 is used to optimize the noise-removed signal before outputting it, thus completing the distributed fiber optic acoustic logging signal processing.
[0037] Example 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a distributed fiber optic acoustic logging signal processing program.
[0038] When the processor executes the computer program, it implements the steps of the above-described distributed fiber optic acoustic logging signal processing method, for example: Extract noise characteristics under well shut-in conditions and establish a noise model based on these characteristics; Acquire acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; The noise-removed signal is optimized and then output to complete the distributed fiber optic acoustic logging signal processing.
[0039] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Model building module 1 is used to extract noise features under well shut-in conditions and build a noise model based on the noise features; Signal processing module 2 is used to collect acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; Signal optimization module 3 is used to optimize the noise-removed signal before outputting it, thus completing the distributed fiber optic acoustic logging signal processing.
[0040] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the mobile terminal.
[0041] For example, the computer program can be divided into a model building module 1, a signal processing module 2, and a signal optimization module 3; The specific functions of each module are as follows: Model building module 1 is used to extract noise features under well shut-in conditions and build a noise model based on the noise features; Signal processing module 2 is used to collect acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; Signal optimization module 3 is used to optimize the noise-removed signal before outputting it, thus completing the distributed fiber optic acoustic logging signal processing.
[0042] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.
[0043] The processor can be a Central Processing Unit (CPU), or 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 components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.
[0044] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.
[0045] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0046] Example 4 The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the distributed fiber optic acoustic logging signal processing method.
[0047] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0048] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.
[0049] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.
[0050] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.
[0051] Example 5 A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the distributed fiber optic acoustic logging signal processing method described above.
[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A distributed fiber optic acoustic logging signal processing method, characterized in that, include: Extract noise characteristics under well shut-in conditions and establish a noise model based on these characteristics; Acquire acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; The noise-removed signal is optimized and then output to complete the distributed fiber optic acoustic logging signal processing.
2. The distributed fiber optic acoustic logging signal processing method according to claim 1, characterized in that, The specific process for extracting noise features under well-shutdown conditions and establishing a noise model based on these features is as follows: In the shut-in state, distributed fiber optic acoustic sensors are used to acquire acoustic signals. The acquired acoustic signals are converted into the frequency domain through fast Fourier transform, and noise features are extracted in the frequency domain. A noise model is then established based on the noise features.
3. The distributed fiber optic acoustic logging signal processing method according to claim 2, characterized in that, The noise characteristics include frequency characteristics, amplitude characteristics, and phase characteristics.
4. The distributed fiber optic acoustic logging signal processing method according to claim 1, characterized in that, The specific process of collecting acoustic signals during production and inputting these signals into the noise model for processing to obtain the noise-removed signal is as follows: In production mode, acoustic signals are acquired using distributed fiber optic acoustic sensors. After detrending processing and frequency domain conversion, the signals are input into the noise model under shut-in conditions for calculation to obtain the noise-removed signal.
5. The distributed fiber optic acoustic logging signal processing method according to claim 1, characterized in that, The acoustic signal is used to adjust the noise cancellation parameters within the noise model using an adaptive algorithm, either by minimizing the mean square error or by recursive least squares, to obtain the optimal signal-to-noise ratio.
6. The distributed fiber optic acoustic logging signal processing method according to claim 1, characterized in that, In the step of optimizing the output of the noise-removed signal, the noise-removed signal is restored to a time-domain signal by inverse Fourier transform, and the time-domain signal is output after filtering and smoothing.
7. A distributed fiber optic acoustic logging signal processing system, characterized in that, include: The model building module is used to extract noise features under well shut-in conditions and build a noise model based on these features. The signal processing module is used to acquire acoustic signals during production, input the acoustic signals into the noise model for calculation to obtain the noise-removed signal; The signal optimization module is used to optimize the noise-removed signal before outputting it, thus completing the distributed fiber optic acoustic logging signal processing.
8. A mobile terminal, 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 steps of the distributed fiber optic acoustic logging signal processing method as described in any one of claims 1-6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed fiber optic acoustic logging signal processing method as described in any one of claims 1-6.
10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operations corresponding to the distributed fiber optic acoustic logging signal processing method as described in any one of claims 1-6.