- Open Access
Research on mud pulse signal data processing in MWD
© Tu et al.; licensee Springer. 2012
Received: 9 May 2012
Accepted: 31 July 2012
Published: 22 August 2012
Wireless measure while drilling (MWD) transmits data by using mud pulse signal ; the ground decoding system collects the mud pulse signal and then decodes and displays the parameters under the down-hole according to the designed encoding rules and the correct detection and recognition of the ground decoding system towards the received mud pulse signal is one kind of the key technology of MWD. This paper introduces digit of Manchester encoding that transmits data and the format of the wireless transmission of data under the down-hole and develops a set of ground decoding systems. The ground decoding algorithm uses FIR (Finite impulse response) digital filtering to make de-noising on the mud pulse signal, then adopts the related base value modulating algorithm to eliminate the pump pulse base value of the denoised mud pulse signal, finally analyzes the mud pulse signal waveform shape of the selected Manchester encoding in three bits cycles, and applies the pattern similarity recognition algorithm to the mud pulse signal recognition. The field experiment results show that the developed device can make correctly extraction and recognition for the mud pulse signal with simple and practical decoding process and meet the requirements of engineering application.
Data transmission under down-hole and data receiving on the ground are the key techniques in the wireless measure while drilling. At present the signal transmission manners used in MWD mainly include the electromagnetic wave and mud drilling fluid pressure wave. The signal attenuation degree of the electromagnetic wave transmission signal becomes serious with the increase of the depth of the stratum, and the difference of the geological structure leads to different attenuation extent of signal amplitude, thus the signal transmission rate can only be send with a low frequency and also in a short transmission distance. The transmission rate of mud drilling fluid pulse signal possesses the characteristics of higher reliability and further transmission distance compared with that of electromagnetic wave signal, so using mud drilling fluid pressure wave to communicate is currently a common method used in MWD[3, 4]. However, MWD signal transmission medium is susceptible to be affected by all kinds of the outside noise, it’s a problem needing to be solved as soon as possible to extract useful signal from signal flooded by all kinds of noise. Literature makes analysis of the pump noise, well drilling noise, pulse noise and transmitting noise in mud pulse signal. Literature processes the mud pulse signal with wavelet transform and compares the signal by choosing different parameters to decompose and reconstruct seven kinds of common wavelet basic functions with the original signal, and choose the best wavelet base function proper to process the signal and its parameters according to the size of correlated coefficient. Literature adopts the method of reversing pulse signal by linear filter algorithm, and based on this, uses a nonlinear “flat-roofed elimination” method to process the mud pulse signal. Literature[8, 9] adopts related filtering wave processing method. The methods used in the above literature mainly focus on signal denoising, or rather mainly aim at processing signal of the PLM (pulse location managerment). Although the scheme using Manchester encode values is not a new idea,our contributions mostly lie in giving detailed signal flow,applying FIR filtering and pump impulse noise elimination algorithm, introducing the pattern similarity recognition algorithm to the mud pulse signal recognition.
Wireless measure while drilling system
Down-hole data processing
Down-hole data encoding
Data encoding length and the corresponding physical value
Date binary effective
50 ~ 308.53(°C)
−0.585 ~ +0.585(Gause)
−0.585 ~ +0.585(Gause)
−0.585 ~ +0.585(Gause)
−0.138 ~ +0.138G
−0.138 ~ +0.138G
+1.1 ~ -1.1G
Down-hole data transmission principle
The ground data processing of wireless MWD system
Signal filtering wave
y(n) is filter output, x(n) represents input of the mud pulse signal, n b = 200, b(i) = 1/200. In the program design of VC++6.0, choosing the filtering data length as 200, i.e. the displayed waveform after filtering of the collected data is the pulse waveform collected one second before; if filter to signal processing is in one second, it can satisfy the real-time requirement. In Figure4 the waveform after wave filter of mud pressure wave is the waveform after FIR de-noising, and it can be seen clearly from the de-noised waveform that the high frequency noise mixed in the signal gets eliminated.
Pump impulse base-value adjustment
Modelling of mud pulse shape
Mud pulse signal sample model corresponding the binary number
Pulse waveform recognition
It can be read from Figure9, Figure10 and Figure11 that after three kinds of model similarity measure calculation, the minimum is got from No.16 waveform D ij model, and the maximum in S and T. And the binary value of Figure8 waveform data is “101”, through the above three kinds of mode similarity measure it can make effective recognition for the mud pulse signal.
The field experiment
The field experiment data
4 // mode
50rpm //rotate speed
5 // mode
Make introduction of the whole system of MWD, down-hole Manchester encoding, and data transmission format underground mud pulse signal.
Adopt the FIR filter algorithm to process the mud pulse signal with de-noising, and based on this make use of related algorithm to eliminate the de-noised pump impulse base value.
Set up the recognition model of the mud pulse signal model similarity, and adopt the model similarity recognition algorithm to recognize the mud pulse signal of Manchester encode in the three bit cycle.
Through the field test verification, it can accurately solve all kinds of signal at the bottom with the characteristics of low rate code error and convenient decoding operation which has a broad prospect in the mud pulse signal processing.
- Su YN, Dou XR: Measurement while drilling, logging while drilling and logging instrument (in Chinese). Oil Drill. Prod. Technol. 2005, 27(1):74-78.Google Scholar
- Han J, Chai QZ: Digital signal processing method of signal detection for remote-sensed mud-pulse measurement while drilling (in Chinese). J. Univ. PetroleumChina 1994, 18(2):96-101.Google Scholar
- Zhang H, Li AZ, Li CW, Qu JH, Liao QM: Signal Processing of Wireless Measurement While Drilling Based on Discrete Stationary Wavelet Transform[J]. Petroleum Drilling Techniques 2007, 35(2):49-51. in ChineseGoogle Scholar
- Zhao JH, Wang LY, Sheng LM, Wang JJ: Anonlinear method for filtering noise and interference of pulse signal in measurement while drilling[J]. Acta Petrolei sinica 2008, 29(4):596-600. in ChineseGoogle Scholar
- Zhang Heng LAZ, Li CW, Qu JH, Liao QM: Comparative study on mud-pulse signal processing methods[J]. Oil Drilling&Production Technology 2007, 29(2):84-90. in ChineseGoogle Scholar
- Noureldin A, Irviner Halliday D, Mintchev MP: Measurement while drilling surveying of highly inclined and horizontal well sections utilizing single axis gyrosensing system Meas. Sci. Technol 2004, 15(12):2426-2434.Google Scholar
- Wen Yuan C, Bin F, Yi W: MWD drilling mud signal De-noising and signal extraction research based on the pulse-code information. Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Identification (Qingdao, 11-14 July 2010); pp. 244-249.Google Scholar
- Wang W, Zhang GM, Wang XH: Voltage fluctuation detection and tracking based on adaptive filtering algorithm (in Chinese). Electric. Meas. Instrum. 2011, 7(48):20-23.Google Scholar
- Feng DQ, Sun CF, Fei MR: Research on a new LMS algorithm with variable steplength (in Chinese). Process Autom. Instrum. 2007, 28(8):67-69.Google Scholar
- Zhao QJ, Zhao BJ, Wang W: Data processing techniques for a wireless data transmission application via mud. EURASIP J. Adv. Signal Process 2011, 1: 45.View ArticleGoogle Scholar
- Wang WY, Rong YH, Gong YM, et al.: Fuzzy clustering based identification and simulation for dynamic wave pattern of mould breakout. J. Syst. Simul. 2003, 4(15):472-475.Google Scholar
- Shen Y, Zhu J, Su YN, Sheng LM, Li L: Transmission characteristics of the drilling fluid pressure quadrature phase shift keying signal along a directional wellbore[J]. Acta Petrolei sinica 2011, 2(32):340-345. in ChineseGoogle Scholar
- Ni YZ, Tian Y: A kind of fast template matching algorithm for wave identification (in Chinese). Sensor World 2006, 4: 31-33.Google Scholar
- He SS, Liu XS: Analysis of signal attenuation for positive drilling fluid pulse (in Chinese). Oil Drill. Prod. Technol. 2001, 6(24):1-5.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.