Continuous wave mud pulse signal denoising method based on VMD algorithm

Abstract

The invention relates to the technical field of oil and gas continuous wave mud signal processing, in particular to a continuous wave mud pulse signal denoising method based on a VMD algorithm, which comprises the following steps: constructing and solving a variational model for a mud signal containing continuous wave pulse by adopting the VMD algorithm, and directly carrying out self-adaptive solution on all IMF components; the IMF components are screened according to the center frequency and the pump stroke frequency of the IMF components in the first step, and pump noise is removed; and low-pass filtering is carried out on the signal data after the pump noise is removed through VMD decomposition to remove high-frequency random noise, and low-pass pulse signal data is obtained. According to the method, the ground decoding bit error rate can be reduced, efficient transmission of underground data is achieved, the method has adaptivity, continuous wave sinusoidal signals can be effectively locked, and the method has high anti-interference capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas continuous wave mud signal processing, and is a method for denoising continuous wave mud pulse signals based on the VMD algorithm. Background Art

[0002] As a non-renewable resource on the earth, oil, known as the "industrial blood", can not only be used to refine fuel oil and various lubricating oils, but also to manufacture products such as synthetic rubber, synthetic fiber, pesticides, and chemical fertilizers. With the development of China's industry, the demand for oil has been increasing year by year. After years of continuous exploitation, high-quality, shallow-layer, and relatively easy-to-develop oil reservoir resources are gradually drying up, and oil resource exploitation activities are gradually shifting to harsh environment areas, which poses new challenges to the oil industry.

[0003] With the development of oil and gas drilling types, the wellbore structure has become increasingly complex. During the construction process, in order to timely master various downhole index parameters, the Measurement While Drilling (MWD) technology and the Logging While Drilling (LWD) technology are usually used to measure downhole parameters in real time, and the measured data is transmitted to the pressure sensor on the ground through the mud medium, and then the ground decoding technology is applied to analyze and process the pressure signal, so as to obtain the downhole real-time measurement parameters. In actual drilling and production, there are mainly two types of data transmission channels: wireless transmission and wired transmission. Wireless transmission mainly includes electromagnetic waves, sound waves, mud pulses, etc., and wired transmission mainly includes cables, special drill pipes, optical fibers, etc.

[0004] In the MWD signal transmission system, mud pulse transmission not only has low cost and high reliability, but also the transmission depth basically meets the current signal transmission requirements, so it has been widely used in actual engineering. However, with the exploitation of complex oil and gas fields in China, the downhole environment has become more and more complex. In order to ensure the safety of downhole tools and avoid accidents, more and more data needs to be transmitted during drilling operations, and the requirements for the quality and speed of data transmission are also getting higher and higher. The transmission mode of mud pulses can be divided into continuous wave signals, positive pulse signals, and negative pulse signals. Among the three methods, the continuous wave signal not only has strong anti-interference ability, but also has a fast signal transmission speed and has good application prospects.

[0005] With the development of technologies such as computers and artificial intelligence, intelligence and digitization are the development trends of future measurement-while-drilling (MWD) technology. In the exploitation of complex oil and gas fields in China, the downhole environment is becoming increasingly complex. To ensure the safety of downhole tools and avoid accidents, more and more data need to be transmitted during drilling operations, and the requirements for the quality and speed of data transmission are also getting higher and higher. The existing positive pulse generators have limited transmission rates and fewer applicable encoding and decoding methods. The continuous wave pulse generator can not only overcome the above deficiencies and improve the downhole data upload rate, but also has high anti-interference ability, which is an important development trend for future downhole transmission. Due to the serious attenuation of the mud-transmitted signal and the influence of noise, it is extremely difficult to decode on the ground. Therefore, the research on the noise removal method of continuous wave mud pulse signals based on VMD is of great significance for improving the downhole data transmission rate and reducing the ground decoding error rate, and it is also the technical bottleneck restricting the development of high-speed mud pulse transmission technology. Summary of the Invention

[0006] The present invention provides a method for denoising continuous wave mud pulse signals based on the VMD algorithm, which overcomes the above-mentioned deficiencies of the prior art and can effectively solve the problem of difficult denoising of existing continuous wave mud pulse signals.

[0007] One of the technical solutions of the present invention is achieved by the following measures: A method for denoising continuous wave mud pulse signals based on the VMD algorithm includes the following steps:

[0008] Step 1, for a certain mud signal containing continuous wave pulses, use the VMD algorithm to directly and adaptively solve all IMF components by constructing and solving a variational model;

[0009] Step 2, screen each IMF component according to the center frequency of each IMF component and the pump stroke frequency in Step 1 to remove pump noise;

[0010] Step 3, for the signal data after removing pump noise through VMD decomposition, perform low-pass filtering to remove high-frequency random noise to obtain the low-pass filtered pulse signal data.

[0011] The following is a further optimization and / or improvement of one of the above-mentioned technical solutions of the invention:

[0012] In the above Step 1, constructing the variational model may include the following steps:

[0013] Specify the number of modes K vmd , and obtain the analytic signal of each mode component u k through the Hilbert transform

[0014]

[0015] where \(k = 1, 2, \ldots, K\) vmd , and \(j\) is an imaginary factor;

[0016] Multiply the analytic signal by Shift the spectrum of the IMF component to be obtained to the fundamental frequency band:

[0017]

[0018] Establish a variational model, estimate the bandwidth of each IMF component, calculate the square of the gradient norm of the signal, and the finally established variational model is:

[0019]

[0020] where is the set of IMF components, is the set of the center frequencies of all modal components.

[0021] In the above step 1, solving the variational model may include the following steps:

[0022] Introduce an augmented Lagrangian function to solve the variational constraint model, and in the solving process, alternately update and by applying the ADMM algorithm;

[0023] where the introduced augmented Lagrangian function is:

[0024]

[0025] where is updated as:

[0026]

[0027] where is updated as:

[0028]

[0029] where \(\{\lambda n+1 \}\) is updated as:

[0030]

[0031] In the above step 2, when calculating the pulse frequency of the pump noise in the mud according to the pump stroke parameters, the IMF component with the center frequency closest to the mud noise pulse frequency among each IMF component can be removed, and then the remaining IMF components are subjected to signal reconstruction to achieve noise removal.

[0032] In the above step 3, a low-pass filter can be used for low-pass filtering. The low-pass filter can be a FIR digital filter, a Butterworth digital filter, or a Chebyshev digital filter. The cut-off frequency of the low-pass filter is greater than the third harmonic frequency of the pump strokes of the mud pump used on-site while drilling.

[0033] In the above step 3, the cut-off frequency of the low-pass filter can be an integer value close to the third harmonic frequency of the pump strokes of the mud pump used on-site while drilling.

[0034] The above can use mud as the medium to transmit signals, and apply a signal denoising algorithm to perform signal denoising and decoding on the ground mud pressure pulse signal.

[0035] The second technical solution of the present invention is achieved by the following measures: A terminal device includes a memory and a processor. A program that can run on the processor is stored on the memory. When the processor executes the program, the above-mentioned method for denoising continuous wave mud pulse signals based on the VMD algorithm is implemented.

[0036] The third technical solution of the present invention is achieved by the following measures: A storage medium stores one or more programs. The one or more programs can be executed by one or more processors to implement the above-mentioned method for denoising continuous wave mud pulse signals based on the VMD algorithm.

[0037] The present invention overcomes the deficiencies of the prior art, reduces the ground decoding error rate, and realizes efficient downhole data transmission. On the basis of fully analyzing the noise characteristics of the continuous wave mud pulse transmission signal, the present invention adaptively decomposes the signal according to the mud pressure signal while drilling of the single-riser pressure sensor, analyzes and removes the pump noise according to the pump strokes, then reconstructs the remaining useful signals, and finally uses a ground hole filter for low-pass filtering to eliminate high-frequency noise, thereby obtaining useful pulse signals and improving the signal-to-noise ratio. The present invention has simple processing, is self-adaptive, can effectively lock the continuous wave sine signal, reduce the error rate, and has high anti-interference ability. The present invention sequentially performs three steps of VMD signal decomposition, IMF component screening and signal reconstruction, and low-pass filtering on the original continuous wave mud pulse, can accurately and effectively remove the noise components in the original signal, improve the signal-to-noise ratio of the signal, and reduce the error rate. Field actual data tests show that the present invention is simple and reliable, can effectively lock the positive pulse signal, reduce the error rate, and has great practical value. Description of the Drawings

[0038] Att Figure 1 is the flowchart of the method of the embodiment of the present invention.

[0039] Att Figure 2 is the flowchart of the VMD algorithm of the embodiment of the present invention.

[0040] AttFigure 3 This is the original signal diagram of the mud pressure wave collected at the surface riser in the embodiment of the present invention.

[0041] Appendix Figure 4 This is the spectrum feature diagram of the positive pulse signal, pump noise and other noises in the embodiment of the present invention.

[0042] Appendix Figure 5 This is the schematic diagram of the VMD decomposition result of the mud signal in the embodiment of the present invention.

[0043] Appendix Figure 6 This is the effective signal diagram of removing pump noise in the embodiment of the present invention.

[0044] Appendix Figure 7 This is the pulse signal diagram after low-pass filtering to remove other noises in the embodiment of the present invention.

[0045] Appendix Figure 8 This is the schematic diagram of the data comparison of code elements, theoretical modulation signals and pulse signals after noise reduction in the embodiment of the present invention.

[0046] As shown in the figure: 1 is the mud signal, 2 is the spectrum feature, 3 is the IMF component, 4 is the effective signal component of removing pump noise, 5 is the pulse signal data after low-pass filtering, and 6 is the pulse signal after noise reduction. Specific implementation manner

[0047] The present invention is not limited by the following embodiments, and the specific implementation manner can be determined according to the technical solution of the present invention and the actual situation.

[0048] The present invention will be further described below in conjunction with embodiments:

[0049] Embodiment 1: As shown in the appendix Figure 1 The continuous wave mud pulse signal denoising method based on the VMD algorithm includes the following steps:

[0050] Step 1, for a certain mud signal containing continuous wave pulses, use the VMD algorithm to directly solve all IMF components adaptively by constructing and solving a variational model;

[0051] Step 2, screen each IMF component according to the center frequency of each IMF component and the pump stroke frequency in Step 1 to remove pump noise;

[0052] Step 3, for the signal data after removing pump noise by VMD decomposition, perform low-pass filtering to remove high-frequency random noise to obtain the pulse signal data after low-pass filtering.

[0053] In the embodiment of the present invention, the VMD algorithm in Step 1 includes the construction and solution of variational problems.

[0054] Among them, in step one, constructing the variational model includes the following steps:

[0055] First, the number of modes K needs to be given vmd , and the analytic signal of each mode component u is obtained through the Hilbert analytic transform k

[0056]

[0057] where k = 1, 2,..., K vmd , and j is the imaginary factor;

[0058] Then multiply the analytic signal by Shift the spectrum of the IMF component to be solved to the base frequency band:

[0059]

[0060] Finally, establish the variational model, estimate the bandwidth of each IMF component, calculate the square of the gradient norm of the signal, and the finally established variational model is:

[0061]

[0062] where is the set of IMF components, is the set of the center frequencies of all mode components.

[0063] Among them, in step one, solving the variational model includes the following steps:

[0064] In the process of solving the variational problem, an augmented Lagrangian function is introduced to solve the variational constraint model. In the solving process, the ADMM algorithm is applied to and {λ n+1} for alternating updates;

[0065] Among them, the introduced augmented Lagrangian function is:

[0066]

[0067] where The update method of

[0068]

[0069] where The update method of

[0070]

[0071] where {λ n+1 ​} The update method is as follows:

[0072]

[0073] In the embodiment of the present invention, as a preferred technical solution, in step two, the pulse frequency of the pump noise in the mud is calculated according to the pump stroke parameter, and the IMF component with the center frequency closest to the mud noise pulse frequency among each IMF component is removed, and then the remaining IMF components are subjected to signal reconstruction, thereby realizing noise removal.

[0074] In the embodiment of the present invention, as a preferred technical solution, in step three, low-pass filtering is performed using a low-pass filter, and the low-pass filter uses a FIR digital filter or a Butterworth digital filter or a Chebyshev digital filter. The cut-off frequency of the low-pass filter is greater than the third harmonic frequency of the pump stroke of the mud pump used on-site while drilling.

[0075] In the embodiment of the present invention, as a preferred technical solution, the cut-off frequency of the low-pass filter in step three is an integer value close to the third harmonic frequency of the pump stroke of the mud pump used on-site while drilling.

[0076] Based on the full analysis of the noise characteristics of the continuous wave mud pulse transmission signal, the embodiment of the present invention adaptively decomposes the signal according to the mud pressure signal while drilling of the single-riser pressure sensor, analyzes and removes the pump noise according to the pump stroke, then reconstructs the remaining useful signal, and finally performs low-pass filtering using a low-pass filter to eliminate high-frequency noise, thereby obtaining a useful pulse signal, improving the signal-to-noise ratio. This method is simple to process, has self-adaptability, can effectively lock the continuous wave sine signal, reduce the error rate, and has a high anti-interference ability.

[0077] Embodiment 2: In the continuous wave mud pulse signal denoising method based on the VMD algorithm, mud is used as the medium for signal transmission, and a signal denoising algorithm is applied to perform signal denoising and decoding on the ground mud pressure pulse signal. Its advantages compared with the traditional signal denoising algorithm are mainly as follows: (1) The signal decomposition algorithm based on VMD has good self-adaptability and can adaptively decompose the pressure wave collected by the ground mud pressure sensor based on the frequency domain. There will be no modal aliasing phenomenon in each sub-band after decomposition; (2) Using mud as the signal transmission medium does not change the original positive pulse signal transmission mechanism and is easy to promote; (3) The signal denoising algorithm can use a variety of signal encoding and decoding algorithms, and can further improve the mud pulse signal transmission rate after optimizing the encoding and decoding algorithms.

[0078] Embodiment 3: The embodiment of the present invention provides a continuous wave mud pulse signal denoising method based on the VMD algorithm. The flowchart of the embodiment of the present invention is as Figure 1 shown, and the signal decomposition process of the VMD algorithm is as Figure 2As shown. For the original signal of the mud pressure wave collected at the surface riser as shown in Figure 3 The original signal of the mud pressure wave collected at the surface riser as shown in Figure 3 is subjected to three steps of VMD signal decomposition, IMF component screening and signal reconstruction, and low-pass filtering in sequence to obtain the original signal of the mud pressure wave containing useful information. The specific steps are as follows:

[0079] Step 1, VMD signal decomposition.

[0080] Perform FFT transform (fast Fourier transform) on a certain mud signal 1 containing continuous wave pulses (as shown in Figure 3 ), obtain the spectral characteristics 2 of the positive pulse signal, pump noise and other noises (as shown in Figure 4 ), and then use the VMD algorithm to directly solve all IMF components adaptively by constructing and solving a variational model to obtain 4 IMF components 3 obtained after the VMD decomposition of the mud signal 1 (as shown in Figure 5 ). It can be seen from Figure 5 that the IMF1 component and the IMF2 component are the two components of the first carrier frequency and the second carrier frequency, IMF3 is other noise components, and IMF4 is the pump pressure pulse. It can be seen that the VMD algorithm can completely decompose the two carrier frequencies and has a good signal decomposition effect.

[0081] When the VMD algorithm is used to decompose a certain mud signal 1 containing continuous wave pulses in the embodiment of the present invention, the construction process of the variational model is as follows:

[0082] First, it is necessary to specify the number of modes K vmd , and obtain the analytic signal of each mode component u k through the Hilbert transform

[0083]

[0084] where k = 1, 2,..., K vmd , and j is the imaginary factor.

[0085] Then multiply the analytic signal by to shift the spectrum of the IMF component to be solved to the baseband:

[0086]

[0087] Finally, establish a variational model, estimate the bandwidth of each IMF component, calculate the square of the gradient norm of the signal, and the finally established variational model is:

[0088]

[0089] Among them, is the set of IMF components, is the set of the center frequencies of all modal components.

[0090] When the VMD algorithm is used to decompose a certain mud signal 1 containing continuous wave pulses in the embodiment of the present invention, the solution process of the variational model is as follows:

[0091] In the solution process of the variational problem, an augmented Lagrangian function is introduced to solve the variational constraint model. In the solution process, the ADMM algorithm is applied to and {λ n+1} for alternating updates.

[0092] Among them, the introduced augmented Lagrangian function is:

[0093]

[0094] Among them, The update method of is:

[0095]

[0096] Among them, The update method of is:

[0097]

[0098] Among them, the update method of {λ n+1} is:

[0099]

[0100] Step 2, IMF component screening and signal reconstruction.

[0101] According to the center frequencies of the IMF components in Step 1 and the pump stroke frequency, each IMF component is screened, and the IMF component with the center frequency closest to the mud noise pulse frequency among each IMF component is removed. As Figure 5 shown, for Figure 5 the two IMF components of the first carrier frequency and the second carrier frequency in, that is, IMF1 and IMF2, signal reconstruction is performed to obtain the effective signal component 4 after removing the pump noise (as Figure 6 shown).

[0102] Step 3, low-pass filtering.

[0103] For the signal data after removing the pump noise by VMD decomposition, an FIR digital filter is used for low-pass filtering to remove high-frequency random noise. The cut-off frequency of the low-pass filter is an integer value close to the third harmonic frequency of the mud pump stroke used at the drilling site, and the low-pass filtered pulse signal data 5 is obtained (as Figure 7as shown

[0104] Compare the signal sent by the theoretical pulse generator with the pulse signal 6 after noise cancellation (as Figure 8 shown). It can be seen from the figure that when the symbol is 1, the modulation signal frequency is the first carrier frequency, and when the symbol is 0, the modulation signal frequency is the second carrier frequency. After denoising the continuous wave mud raw signal 1 by the continuous wave mud pulse signal denoising method based on VMD proposed in the embodiment of the present invention, the similarity between the denoised pulse signal 5 and the theoretical modulation signal is relatively high. This method is simple to process, has self-adaptability, can effectively lock the continuous wave sine signal, reduce the bit error rate, and has high anti-interference ability.

[0105] The embodiment of the present invention sequentially performs three steps of VMD signal decomposition, IMF component screening and signal reconstruction, and low-pass filtering on the original continuous wave mud pulse, which can accurately and effectively remove the noise components in the original signal, improve the signal-to-noise ratio of the signal, and reduce the bit error rate. Field actual data tests show that this method is simple and reliable, can effectively lock the positive pulse signal, reduce the bit error rate, and has great practical value.

[0106] Embodiment 4: The embodiment of the present invention provides a terminal device, which includes a memory, a processor, a communication interface, and a communication bus. A program that can run on the processor is stored in the memory. When the processor executes the program, it implements the continuous wave mud pulse signal denoising method based on the VMD algorithm described in the above embodiment.

[0107] The processor can be a central processing unit, and the processor can also be other general-purpose processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips.

[0108] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to the above method embodiments of the present invention. The processor executes various functional applications and work data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, that is, implements the continuous wave mud pulse signal denoising method based on the VMD algorithm described in the above embodiment.

[0109] The memory may include a program storage area and a data storage area. Among them, the program storage area can store the operating system and application programs required for at least one function; the data storage area can store data created by the processor and the like. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. The memory may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the processor through a network. The one or more programs are stored in the memory and, when executed by the processor, implement the continuous wave mud pulse signal denoising method based on the VMD algorithm described in the above embodiments.

[0110] Embodiment 5: An embodiment of the present invention provides a storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the continuous wave mud pulse signal denoising method based on the VMD algorithm described in any one of the above method embodiments.

[0111] Among them, the storage medium can be an internal storage unit of the terminal device, such as the hard disk or memory of the terminal device. The storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal device.

[0112] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effects. Non-essential technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A method for denoising continuous wave mud pulse signals based on the VMD algorithm, characterized in that it includes the following steps: Step 1, for a certain mud signal containing continuous wave pulses, use the VMD algorithm to directly solve all IMF components adaptively by constructing and solving a variational model; Step 2, screen each IMF component according to the center frequency of each IMF component and the pump stroke frequency in Step 1 to remove pump noise; Step 3, for the signal data after removing pump noise by VMD decomposition, perform low-pass filtering to remove high-frequency random noise to obtain the low-pass pulse signal data.

2. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1, characterized in that in Step 1, constructing the variational model includes the following steps: Given the number of modes K vmd , the analytical signal of each mode component u k is obtained through Hilbert transform where k = 1, 2, ···, K vmd , and j is an imaginary factor; Parse the signal Multiply by Shift the spectrum of the IMF component to be obtained to the fundamental frequency band: Establish a variational model, estimate the bandwidth of each IMF component, calculate the square of the gradient norm of the signal, and the finally established variational model is: Among them, is the set of IMF components, is the set of the central frequencies of all modal components.

3. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1 or 2, characterized in that in Step 1, solving the variational model includes the following steps: The augmented Lagrangian function is introduced to solve the variational constraint model. During the solution process, the ADMM algorithm is applied to and {λ n+1} for alternative updates; Among them, the introduced augmented Lagrangian function is: Among them, The update method is as follows: Among them, The update method is as follows: Among them, the update method of {λ n+1} is as follows:

4. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1 or 2, characterized in that in Step 2, calculate the pulse frequency of pump noise in the mud according to the pump stroke parameters, remove the IMF component with the center frequency of each IMF component closest to the pulse frequency of mud noise, and then reconstruct the signal of the remaining IMF components to achieve noise removal.

5. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 3, characterized in that in Step 2, calculate the pulse frequency of pump noise in the mud according to the pump stroke parameters, remove the IMF component with the center frequency of each IMF component closest to the pulse frequency of mud noise, and then reconstruct the signal of the remaining IMF components to achieve noise removal.

6. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1 or 2 or 5, characterized in that in Step 3, perform low-pass filtering using a low-pass filter. The low-pass filter uses a FIR digital filter or a Butterworth digital filter or a Chebyshev digital filter, and the cut-off frequency of the low-pass filter is greater than the third harmonic frequency of the pump stroke of the mud pump used in the drilling site.

7. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1 or 2 or 5, characterized in that the cut-off frequency of the low-pass filter in Step 3 is an integer value close to the third harmonic frequency of the pump stroke of the mud pump used in the drilling site.

8. The method for denoising continuous wave mud pulse signals based on the VMD algorithm according to claim 1 or 2 or 5, characterized in that use mud as the medium to transmit signals, and apply a signal denoising algorithm to denoise and decode the ground mud pressure pulse signals.

9. A terminal device, including a memory and a processor, and a program that can run on the processor is stored on the memory, characterized in that, When the processor executes the program, it implements the continuous wave mud pulse signal denoising method based on the VMD algorithm according to any one of claims 1 to 8.

10. A storage medium, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the continuous wave mud pulse signal denoising method based on the VMD algorithm according to any one of claims 1 to 8.

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