Mud Pulse Signal Research Articles

Integration method of compressed sensing with variational mode decomposition based on gray wolf optimization and its denoising effect in mud pulse signal.

The continuous wave mud pulse transmission holds great promise for the future of downhole data communication. However, significant noise interference during the transmission process poses a formidable challenge for decoding. In particular, effectively eliminating random noise with a substantial amplitude that overlaps with the pulse signal spectrum has long been a complex issue. To address this, an enhanced integration algorithm that merges variational mode decomposition (VMD) and compressed sensing (CS) to suppress high-intensity random noise is proposed in this paper. In response to the inadequacy of manually preset parameters in VMD, which often leads to suboptimal decomposition outcomes, the gray wolf optimization algorithm is designed to obtain the optimal penalty factor and decomposition mode number in VMD. Subsequently, the optimized parameter combination decomposes the signal into a series of intrinsic modes. The mode exhibiting a stronger correlation with the original signal is retained to enhance signal sparsity, thereby fulfilling the prerequisite for compressed sensing. The signal is then observed and reconstructed using the compressed sensing method to yield the final signal. The proposed algorithm has been compared with VMD, CS, and CEEMD; the results demonstrate that the method can enhance the signal-noise ratio by up to ∼20.55 dB. Furthermore, it yields higher correlation coefficients and smaller mean square errors. Moreover, the experimental results using real field data show that the useful pulse waveforms can be recognized effectively, assisting surface workers in acquiring precise downhole information, enhancing drilling efficiency, and significantly reducing the risk of engineering accidents.

Design and Application of Downhole Drilling Mud Pulse Signal Transmission System

Time-efficient measurement of undisturbed formation through measurement while drilling technology is an effective means to improve drilling efficiency in deep formations and increase the drilling-encounter ratio of reservoirs. Based on the application and development trend of existing downhole signal transmission technology, a pulse signal transmission system of downhole mud while drilling is designed. The system pulse generation device adopts the structure of a positive pulse generator, which is through the internal driving circuit and hydraulic circuit of the device to drive the valve head to move to generate a mud pulse signal while using the design of a limit switch to optimize the expansion spacing of the valve head so that the generated pulse signal amplitude is close. The system uses combined encoding as the encoding method, while the ground part of the system is designed to monitor and process the collected signals. Finally, the transmission capability and accuracy of the system are tested through field experiments, and the field results show that the system can work smoothly in the downhole environment to transmit downhole data to the wellhead with small errors, which meets the design requirements and field requirements. The field applications are carried out in several wells, such as the Shixi 105 well. The results show that the system has good downhole pulse signal transmission functions such as borehole inclination measurement, which helps to promote the development of existing drilling measurement technology.

Analysis on the change characteristics of waveform during the transmission of continuous wave mud pulse signal

Continuous wave mud pulse transmission is the most widely used information transmission technology in measurement while drilling (MWD). The increase in transmission distance will cause the signal attenuation and deformation during transmission, the affecting exploration efficiency. The waveform change law of continuous mud pulse wave in the process of downhole transmission, this paper establishes a mathematical analysis model, focuses on the modulation form of continuous wave signal, physical mud characteristics, and drill pipe characteristics, and discusses the transmission rules of various characteristics on continuous pressure wave. A simulation system of drill pipe transmission pressure wave is constructed. The transmission characteristics of continuous wave signals under different modulation forms are discussed. The positive correlation between the amplitude attenuation and waveform distortion of pulse signals caused by increased gas holdup in the mud under bubbly flow patterns is analyzed. The numerical calculation and simulation analysis results show that the viscosity frequency characteristic of mud is the main factor affecting the attenuation of continuous wave signal. The surface roughness, size and other physical characteristics of drill pipe can affect the transmission speed and attenuation of signals in long-distance transmission. The signal is prone to distortion at the position of modulation phase change.

Study on model-based pump noise suppression method of mud pulse signal

Mud pulse telemetry system plays a critical role in modern oil drilling engineering. However, the valid signal is complicated to be obtained because of the main disturbing of pump noises, especially in ultra-deep drilling operations. Therefore, four model-based noise suppression plans for the mud pulse signal pump are mainly proposed in this paper. First, three kinds of pump noise state-space models (linear time-invariant model, linear time-varying model, and nonlinear model) are constructed based on the detailed analysis of the pump noise characteristics. Then, the standard Kalman filter based on the linear system and the extended Kalman filter and unscented Kalman filter algorithm adopting the nonlinear system are respectively used to reconstruct and filter the pump noise. Moreover, the wavelet threshold denoising method based on a new power threshold function is specifically adopted to filter residual random noise after the removal of pump noise. Simultaneously, several noise-containing mud pulse signals with different SNRs (Signal-to-Noise Ratios) in the stable/unstable pump noise states were simulated. Besides, the noise suppression capability experiments of the four plans are deeply conducted. The results indicate that all the four plans can suppress and even remove the stable or unstable pump noise from the original signals: plan 1 and plan 2 have similar denoising effect, which can improve the SNR of signals with stable and unstable pump noise of different intensities by approximately 5–25 dB and 5–14 dB; while the plan 3 can increase the SNR of signals by about 4–20 dB and 2–8 dB; and plan 4 can increase that by about 4–20 dB and 1–8 dB, respectively. Under the comprehensive consideration of denoising ability, computation intensity and convergence speed, it is suggested that plan 1 integrating the time-invariant linear model and the standard Kalman filtering reveals superior performance. Furthermore, using the processing method consisting of plan 1 and wavelet threshold denoising method, the SNRs of the denoised signals can reach 6–7 dB even if the original SNR is as low as −30 dB. The proposed method is applied to the actual deep well mud pulse signal processing. The effective downhole pulse transmission waveform is successfully obtained, providing a highly reliable input signal for subsequent data restoration and contributing to an essential and practical significance for improving mud pulse transmission quality.

Drag‐Reduction Performance Evaluation of Controllable Hybrid Steering Drilling System

The excessive drag/torque and the backing pressure is an important factor that restricts the improvement of the penetration rate and the extension of the drilling in the sliding drilling process of extended‐reach wells and horizontal wells. To deal with this problem, this paper developed a novel controllable hybrid steering drilling system (CHSDS) based on the friction‐reducing principle of a rotating drill string. The CHSDS is composed of a gear clutch, hydraulic system, and measurement and control system. By controlling the meshing and separation of the clutch with the mud pulse signal, the CHSDS has two working states, which leads to two boundary conditions. Combined with the stiff‐string drag torque model, the effects of the drilling parameters on the friction‐reducing performance of the CHSDS are analyzed systematically. The results show that the friction reduction effect in the inclined section is the most significant, followed by that in the horizontal section, whereas there is almost no impact in the vertical section. Friction reduction increases with the rotary speed and the drilling fluid density, whereas it decreases with the increase in the surface weight‐on‐bit and the bit reaction torque. Field tests confirm the separation and meshing function of the CHSDS. The developed controllable hybrid steering and friction‐reducing technology provides an alternative approach for the safe and high‐efficiency drilling of horizontal wells.

Research on Mud Pulse MWD Device for Mines

As the core accoutrement of directional drilling construction, the measurement while drilling (MWD) device can be divided into three types due to the different data transmission methods: wired, mud pulse and electromagnetic wave. This paper used the mud pulse method to develop a mud pulse MWD device for mines, and the working principle of the mud pulse signal transmission, the signal encoding method and the structure of the device were described. Experimental research showed that the mud pulse wireless MWD device had the advantages of long transmission distance and strong working stability. At the same time, the device was not restricted by the drill pipe during operation, which could be combined with sliding orientation and rotary feed, and had great promotion and application value.

Noise cancellation for continuous wave mud pulse telemetry based on empirical mode decomposition and particle swarm optimization

Mud pulse telemetry (MPT) is the most widely implemented communication method in measurement while drilling (MWD) systems. However, the signal-to-noise ratio of received signals on surface in MPT systems is relatively low because of the various background noises the signals have been exposed to during the drilling process. Due to the reciprocating action of the mud pump, pump noise could be the most detrimental factor to the mud pulse signal. Hence, pump noise cancellation plays an important role in the mud pulse telemetry. This paper presents a novel approach for pump noise cancellation by making use of the optimal signal reconstruction based on the combination of the empirical mode decomposition (EMD) and the particle swarm optimization (PSO). The proposed method comprises the pump noise recovery and the pump noise subtraction. The pump noise recovery is implemented by EMD decomposing the pump noise into a series of subcomponents and by PSO searching for the optimal weights to reconstruct the pump noise. Then the pump noise is subtracted from the received signal. To validate the effectiveness of the proposed method, both simulation tests and field experiment were carried out. The noise cancellation results of the simulation tests showed a good agreement with the modulated signal. In the experiment, the downhole information data was successfully demodulated after the implementation of the EMD-PSO based noise cancellation method. Meanwhile, the proposed method could achieve a lower bit error rate than the pump noise filter method. The findings of this study indicate that the optimal signal reconstruction has a promising application in MPT systems in the future.

Measurement While Drilling Mud Pulse Signal Denoising and Extraction Approach Based on Particle-Swarm-Optimized Time-Varying Filtering Empirical Mode Decomposition

Summary At present, mud pulse transmission is widely used in underground wireless transmission. To extract more accurately the original drilling fluid pulse signals while drilling, in this paper, we developed an algorithm for optimal denoising shaping based on particle-swarm-optimized time-varying filtering empirical mode decomposition (TVFEMD). The performance of TVFEMD heavily depends on its parameters (i.e., B-spline order and bandwidth threshold). In the traditional TVFEMD method, the parameters are given in advance and may not be optimized, so it is difficult to achieve satisfactory decomposition results. To tackle this issue, the correlation coefficient was used as the objective function, and the particle-swarm-optimization algorithm was used to optimize the parameters of TVFEMD in this paper. First, the particle swarm optimization was used to search for the best combination of parameters. Then, the TVFEMD was applied to obtain a series of intrinsic mode functions (IMFs). Subsequently, the optimal denoising and shaping algorithm was used to determine the best reconstructed signal by low-pass filtering. Permutation entropy was taken as the evaluation index to obtain a reconstruction signal. Finally, the reconstructed signal was processed by square wave shaping to obtain accurate drilling fluid pulse signals. The approximation of the algorithm is 0.7581, and relevance is as high as 0.8535. The simulation signal and drilling fluid pulse signal analysis results showed that the proposed approach can extract the original pulse signal accurately.

Mud Pulse Signal Extraction and Noise Cancellation based on Adaptive LMS and NLMS Algorithms Performance

Mud Pulse Signal Extraction and Noise Cancellation based on Adaptive LMS and NLMS Algorithms Performance - written by Saleh M. Mwachaka published on 2020/05/27 download full article with reference data and citations

Mud pulse signal demodulation based on support vector machines and particle swarm optimization

Mud pulse telemetry is the most commonly used communication technology in measurement-while-drilling or logging-while-drilling systems. During the drilling process, the flowing drilling mud is the communication channel that carries the mud pulse signal to surface. Compared with the relatively benign conditions in the wireline communication method, the mud communication channel encounters high signal attenuation, low bandwidth, and numerous sources of noise. The harsh and volatile conditions of the mud channel make it difficult to demodulate the mud pulse signal. In this study, a novel signal demodulation technique based on support vector machines (SVM) is proposed. Meanwhile, the particle swarm optimization (PSO) algorithm is applied for selecting both the penalty parameter and the kernel parameter for the SVM classifier. To overcome the small training set problem and increase the demodulation accuracy, we introduce a training set expansion scheme which is implemented by enlarging the training set with correctly demodulated data based on the error detection method. Field experiment results prove the effectiveness of the training set expansion scheme and the superiority of the proposed methodology compared with the conventional adaptive fractionally-spaced decision-feedback equalizer (FS-DFE). Moreover, the computational time for the whole SVM demodulation procedure is much less than the signal duration. The proposed PSO–SVM method could be a potential solution for mud pulse signal demodulation in mud pulse telemetry systems.

The Process of High-Data-Rate Mud Pulse Signal in Logging While Drilling System

We applied a mud pulse signal to transmit the downhole measured parameters in a Logging While Drilling (LWD) system. The high-data-rate mud pulse signal was almost completely overwhelmed by noise and difficult to be identified because of the narrow pulse width, impacts of the pump noise, and the reflected wave. The wavelet transform’s multiresolution is suitable for signal denoising. In this paper, during the denoising process of the wavelet transform, we used a series of evaluation parameters to select the optimal parameter combination for denoising a mud pulse signal. We verified the feasibility of the wavelet transform denoising algorithm by analysing and processing an operational high-data-rate mud pulse signal. The decoding algorithm was available by applying self-correlation and bit synchronization. The application results through a field application showed that the processing algorithm was suitable for the high-data-rate mud pulse signal’s process.

Parametric study on pump noise processing method of continuous wave mud pulse signal based on dual-sensor

Continuous wave pulse transmission technology has become the forefront development direction of measurement while drilling and logging information transmission with its high transmission rate and strong anti-interference ability. In this paper, the denoising principle of time-domain delay differential method based on two pressure sensors is analyzed in detail. The acquisition signals of two sensors with different sensor intervals and SNRs (Signal-to-Noise Ratio) are constructed. The denoising ability of the time-domain delay differential method in different cases, i.e., stable and unstable pump noise, the amplitude and frequency distortion of the original pulse signal, and the structural change of the original pulse signal, are emphatically studied. Simulations show that this method can effectively suppress the pump noise even when there is a significant fluctuation in the fundamental frequency and amplitude of pump noise. This method can also obtain well-formed reconstructed signal when the pulse signal has high frequency and amplitude distortion. Besides, a general method for deriving an optimal dual-sensor installation interval is discussed for a BFSK (Binary Frequency Shift Keying) transmission signal, which consists of high-frequency and low-frequency carrier waves, and a reference installation interval is given. Finally, this method is applied to denoise the actual dual-sensor acquisition signal, and satisfactory results are obtained.

Auto measurement while drilling mud pulse signal recognition based on deep neural network

The noise intensity of drilling mud pulse signal is large. It is difficult to recognize signal at once because of the low signal to noise ratio (SNR) of the detected signals. Moreover, the SNR has a low accuracy. A method which is called stacked wavelet autoencoder (SWAE) for recognizing mud pulse signals based on deep learning is proposed in this paper. The model is composed of wavelet neural network and autoencoder, which are trained for mud pulse signal classification specifically. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. SWAE enhances the SNR of output signal by using signal detection method. And then the output detected signal is considered as a signal classification attribute. Finally, experimental results show that SWAE is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Experimental results confirm that the performance of SWAE is better than the methods in the state of the art under the same SNR.

SDA-based Neural Network Approach for MWD Mud Pulse Signal Recognition

The low signal to noise ratio (SNR) of the detected mud pulse signal leads to difficult to recognize signal at once. The recognize accuracy is low. So a stacked denoising autoencoder recognition model was constructed. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. The proposed method of recognizing mud pulse signal enhances the SNR of output signal by using signal detection method. And then we take the output detected signal as signal classification attribute. Test results also show that the proposed method is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Performance is better than pattern matching and support vector machine and other recognition methods under the same SNR.

Research on mud pulse signal detection based on adaptive stochastic resonance

The original data signals have small amplitude with a long distance signal transmission from the underground to the ground. And thousands of meters of drilling mud channel in the drilling process will contain a lot of noise sources. Therefore, the original mud pulse signals are submerged by the complex noise environment. Detecting the original signal after receiving the signal on the ground is still a serious issue. In connection with mud pulse signal detection under complex background in measurement while drilling (MWD), nonlinear bistable stochastic resonance theory and the production condition was analyzed. On the basis of this, a method of using genetic simulated annealing algorithm to adaptive optimize the parameters of stochastic resonance was proposed to realize the detection of mud pulse signal in the background of complex noise. Finally, through the simulation experiment and the field verification, this method realizes the signal detection even under the low signal-to-noise ratio (SNR). And the proposed method can overcome the shortcomings of existing detection method of signal detection performance in low SNR. It proves the proposed method's effectiveness and feasibility.

Differential signal extraction for continuous wave mud pulse telemetry

Continuous wave mud pulse telemetry system with a transmission rate of more than 12bps, is an irreplaceable high-speed data transmission technology for Measurement While Drilling (MWD) and Logging While Drilling (LWD) instruments used in modern oil drilling services. However, the valid signal is difficult to be obtained because of the disturbing of pump noise in the data transmission. Here, a differential signal extraction method for continuous wave mud pulse generator is applied to extract the effective mud pulse signal from pump noise. It utilizes dual sensor and a differential algorithm to extract the signal. To verify this method, the robustness was analyzed in the numerical simulation. In the actual test, a continuous wave mud pulse generator was designed, and a hydraulic circulation laboratory was built. The Signal to Noise Ratio (SNR) was risen from −13dB up to 25dB. The experimental results demonstrated the effectiveness of the designed mud pulse generator, and proved that this method can be a feasible way to solve the problem of signal extraction from pump noise.

Research on Manchester Coded Mud Pulse Signal Extraction

The mud pulse signal extracting in MWD(Measurement while drilling) was a core technology in the oil drilling developing process. In terms of the extraction and recognition problems of weak mud pulse signal in MWD, this paper analyzed the transmission format of Manchester coded down-hole datum and transmission characteristics of mud pulse signal and used morphological filtering algorithm to extract the mud pulse signal. According to the characteristics of the Manchester coded underground synchronization signal. After determining the location of the signal start time, It proposed a pattern similar waveform-recognition algorithm to detect the mud pulse signal. The field tests show that the algorithm can accurately define the mud pulse signal starting time and can effectively improve the decoding accuracy and meet the requirements of engineering applications.

Research on Mud Pulse Signal Starting Time Extracting Algorithm in MWD

The mud pulse signal extracting in MWD(Measurement while drilling) was a core technology in the oil drilling developing process. Determining how to accurately define the mud pulse signal starting time was a key issue affecting the decoding accuracy. This paper analyzed the transmission format of down-hole datum and transmission characteristics of mud pulse signal, using a wavelet multi-scale and related de-noising algorithm to extract mud pulse signal. On this basis, according to the characteristics of the underground encoded synchronization signal, It proposed an algorithm based on equal segmentations within a signal period to define the mud pulse signal starting time .The field tests show that the algorithm can accurately define the mud pulse signal starting time and can effectively improve the decoding accuracy and meet the requirements of engineering applications.

Research on MWD Mud Pulse Signal Extraction Algorithm

The extraction and correct recognition of mud pulse signal in Wireless Measurement While Drilling was a key technology in petroleum drilling process. It determined whether the well course in the petroleum drilling process was right or not. This article has carried out the numerical modeling of mud pulse signal and illustrated its signal feature. In terms of the problems of extraction and recognition of PLM encoded mud pulse signal, it has researched on the noise removing using the Wavelet multi-scale feature recognition and related de-noising algorithms. The location of PLM encoded mud pulse signal was discerned precisely by using the combinational algorithm of local feature and waveform character recognition and laid a foundation for the extraction and accurate recognition of mud pulse signal. Finally, the results of live tests indicate that this arithmetic is simple, useful and conform to the requirements of engineering application.

Research on MWD Mud Pulse Signal Waveform Identification Algorithm

Measurement while drilling (MWD) technology uses mud pulse signals in real-time observation bottom of the relevant data, the decoding system on earth will detect and recognize the information received from the shaft bottom. It is one of the core technology of MWD. However, there are all kinds of noise that exsit in the signal channel will create interfere of signal transmission with the date identification error .This article first offer a new way based on the analysis of the Manchester mud pulse signal noise code and attenuation model. At the same time it used NLMS(Normalizod least mean square) adaptive filter algorithm to wipe the pump noise and other interferences .It can more effectively decrease the influence by pump pulse base-value. Due to the features of mud pulse, we apply the waveform identification algorithm to the recognize research after get the data. Finally, field test results showed that the waveform identification algorithm can recognize the mud pulse correctly and the decoding process is easier, higher reliability, lower ber by the field test. All these comply with the requirements of the engineering application.