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# Research on mud pulse signal data processing in MWD

- Bing Tu
^{1}Email author, - De Sheng Li
^{1}, - En Huai Lin
^{2}and - Miao Miao Ji
^{1}

**2012**:182

https://doi.org/10.1186/1687-6180-2012-182

© Tu et al.; licensee Springer. 2012

**Received: **9 May 2012

**Accepted: **31 July 2012

**Published: **22 August 2012

## Abstract

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.

## Keywords

## Introduction

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[1]. 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[2]. 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[5], 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[5] makes analysis of the pump noise, well drilling noise, pulse noise and transmitting noise in mud pulse signal. Literature[6] 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[7] 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[10] (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

### System function

### Down-hole data processing

### Down-hole data encoding

**Data encoding length and the corresponding physical value**

No | Data name | Date binary effective | Measuring range |
---|---|---|---|

1 | Temperature | 7 | 50 ~ 308.53(°C) |

2 | X-magnetometer Base(Bx) | 12 | −0.585 ~ +0.585(Gause) |

3 | Y-magnetometer Base(By) | 12 | −0.585 ~ +0.585(Gause) |

4 | Z-magnetometer Base(Bz) | 12 | −0.585 ~ +0.585(Gause) |

5 | X-accelerometer Base(Gx) | 9 | −0.138 ~ +0.138G |

6 | Y-accelerometer Base(Gy) | 9 | −0.138 ~ +0.138G |

7 | Z-accelerometer Base(Gz) | 12 | +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

*N*is the sample point per second 200;

*y(n)*is the value getting from the FIR digital filter algorithm;

*z(k)*is output value of mud pulse signal after adjustment of base value.

*s(n)*is related value of square wave. Figure5 is waveform figure after adjustment of base value of waveform after filter of mud pressure wave signal to Figure4. It can be seen from Figure6 that signal base value undulation has been effectively eliminated.

#### Modelling of mud pulse shape

*A(x,t)*

*B(x,t)*

*C(x,t)*

*D(x,t)*. where

*P(x)*is mud pulse amplitude of

*x*meters of mud pulse signal transmission length; it shows that the attenuation of mud pulse signal amplitude is related to transmitting velocity, mud density, air content, drill post parameter and other factors, corresponding to four shapes of A, B, C, D in a bit cycle in Figure6. Randomly select signal model in three bits cycle of A, B, C, D as a kind of combination value, then 16 kinds of combinations of values can be acquired, and thus 16 different mathematical models can be acquired. Signal mathematical model in any bit period can be expressed by formula (9)[12]. Analyze the mud pulse signal in the period of three bits, and signals have 16 kinds of sample models as shown in Figure7. Table2 is the 16 kinds sample models binary data,

**Mud pulse signal sample model corresponding the binary number**

Category | Binary data | Category | Binary data |
---|---|---|---|

Model-0 | “000” | Model-8 | “010” |

Model-1 | “000” | Model-9 | “011” |

Model-2 | “111” | Model-A | “100” |

Model-3 | “111” | Model-B | “101” |

Model-4 | “001” | Model-C | “010” |

Model-5 | “001” | Model-D | “011” |

Model-6 | “110” | Model-E | “100” |

Model-7 | “110” | Model-F | “101” |

### Pulse waveform recognition

*X*

_{ i }= (

*x*

_{ i1 }

*x*

_{ i2 }…

*x*

_{ in })

^{ T }, after de-noising and pump impulse base value elimination, characteristic vector of the mud pulse is

*X*

_{ j }= (

*x*

_{ j1 }

*x*

_{ j2 }…

*x*

_{ jn }). The method T of Euclidean distance

*D*

_{ ij }, nip angle cosine

*S*featuring value two and Tanimoto with value two characteristic

*are adopted*to calculat degree between the two types of data.

*D*

_{ ij }and the larger

*S*and

*T*denote the more similar waveform between the two kinds of data. Based on the above theory using three kinds of recognition algorithms to recognize the mud pulse waveform; Figure8 is intercepted from data waveform after filter and base value processing; Figure9, Figure10, Figure11 are got by calculating sample model separately with the Figure9 test waveform with model similarity calculation value.

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

*Pa*·

*s*; the experiment starts its directional measurement from 2 km. Table3 is part of field experiment data.

**The field experiment data**

Date | Data name | Data value | |
---|---|---|---|

2011-12-03 | 10:02:14 | FLAG | |

2011-12-03 | 10:02:30 | TAG | 4 // mode |

2011-12-03 | 10:02:38 | GX | −0.1776 //gravity-x |

2011-12-03 | 10:02:48 | GY | −0.9850 //gravity-y |

2011-12-03 | 10:03:30 | GZ | −0.0201 //gravity-z |

2011-12-03 | 10:03:56 | BX | −0.3410 //magnetic-x |

2011-12-03 | 10:04:22 | BY | −0.38529 //magnetic-x |

2011-12-03 | 10:04:48 | BZ | −0.1469 //magnetic-x |

2011-12-03 | 10:05:14 | TEMP | 35° //temperature |

2011-12-03 | 10:05:34 | INC | 88.84° |

2011-12-03 | 10:05:54 | RPM | 50rpm //rotate speed |

2011-12-03 | 10:06:10 | FLAG | |

2011-12-03 | 10:06:18 | TAG | 5 // mode |

2011-12-03 | 10:06:42 | INC | 88.85° |

2011-12-03 | 10:07:06 | AZ | 117.33° |

## Conclusion

- (1)
Make introduction of the whole system of MWD, down-hole Manchester encoding, and data transmission format underground mud pulse signal.

- (2)
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.

- (3)
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.

- (4)
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.

## Declarations

## Authors’ Affiliations

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## Copyright

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.