Technologies and application of pipeline centerline and bending strain of In-line inspection based on inertial navigation

Inertial mapping unit (IMU) in-line inspection (ILI) has become routine practice for long-distance buried transport pipelines of oil and gas. It is capable of measuring the pipeline centerline position coordinates and locating the pipeline anomalies, features and fittings to help the oil company manage it. The IMU inspection data also can be used to compute the pipeline bending strain and assess the potential deviation from the original position where endures the extra stress. This paper introduces the main principle, measurement and data processing for IMU ILI. As a key point of calculation for centerline and bending strain, the identification and optimization of the signal are also discussed. At the end of this paper, the developments of IMU ILI are presented. The IMU ILI becomes an important and effective method for pipeline integrity management and safe operation of buried oil and gas pipelines.

[1]  Fengyu Xu,et al.  Inspection method of cable-stayed bridge using magnetic flux leakage detection: principle, sensor design, and signal processing , 2012 .

[2]  Ross Gagliano,et al.  Review of , 2006, UBIQ.

[3]  James D. Hart,et al.  3rd Party Review of Geometry Pig Inertial Survey Data at the Colville River HDD , 2008 .

[4]  Reynald Hoskinson,et al.  A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications , 2015 .

[5]  Morteza Dardel,et al.  Vibration control of a nonlinear beam with a nonlinear energy sink , 2016 .

[6]  Jang Gyu Lee,et al.  Development of inspection gauge system for gas pipeline , 2004 .

[7]  V. E. Shcherbinin,et al.  Analytical model of a pipe magnetization by two parallel linear currents , 2011 .

[8]  Tomas Ramirez,et al.  Navigation system for automation of pipelines inspection missions , 2001 .

[9]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[10]  Lalita Udpa,et al.  Use of higher order statistics for enhancing magnetic flux leakage pipeline inspection data , 2007 .

[11]  Jin Shijiu,et al.  The study of detection technology and instrument of buried pipeline-coating defects , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[12]  Maria Forsyth,et al.  An overview of major methods for inspecting and monitoring external corrosion of on-shore transportation pipelines , 2015 .

[13]  Eun-Hwan Shin,et al.  Navigation kalman filter design for pipeline pigging , 2005 .

[14]  Andy Young,et al.  Predicting Pipeline Performance in Geohazard Areas Using ILI Mapping Techniques , 2012 .

[15]  James D. Hart,et al.  "DIGITAL PIGGING" AS A BASIS FOR IMPROVED PIPELINE STRUCTURAL INTEGRITY EVALUATIONS , 2006 .

[16]  M. Esashi,et al.  Electrostatically Levitated Ring-Shaped Rotational-Gyro/Accelerometer , 2003 .

[17]  M. Kasevich,et al.  Long-term stability of an area-reversible atom-interferometer Sagnac gyroscope. , 2005, Physical Review Letters.

[18]  Roee Diamant,et al.  A Machine Learning Approach for Dead-Reckoning Navigation at Sea Using a Single Accelerometer , 2014, IEEE Journal of Oceanic Engineering.

[19]  Yu Shi-feng Improvement of Magnetic Flux Leakage Equipment for Oil Pipe Testing , 2007 .

[20]  Gérard Lachapelle,et al.  Using an Accelerometer Configuration to Improve the Performance of a MEMS IMU: Feasibility Study with a Pedestrian Navigation Application , 2009 .

[21]  M. S. Safizadeh,et al.  Corrosion detection of internal pipeline using NDT optical inspection system , 2012 .

[22]  E. R. El-Zahar,et al.  On controlling the vibrations and energy transfer in MEMS gyroscope system with simultaneous resonance , 2016 .

[23]  Lei Guo,et al.  Multi-objective robust initial alignment algorithm for Inertial Navigation System with multiple disturbances☆ , 2012 .

[24]  Sung-Ho Cho,et al.  Design and implementation of 30„ geometry PIG , 2003 .

[25]  Jose A. Antonino-Daviu,et al.  A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform , 2008, IEEE Transactions on Industrial Electronics.

[26]  Yang Lijia The Pipeline Defect Location Technology Based on Cubature Kalman Smooth Filter , 2015 .

[27]  Ulrich Marewski Approach to assessing Pipeline Displacements , 2012 .

[28]  Ping Lu,et al.  All-Depolarized Interferometric Fiber-Optic Gyroscope Based on Optical Compensation , 2014, IEEE Photonics Journal.

[29]  Han Xiaoming Application of pipeline integrity technology in earthquake disaster , 2010 .

[30]  Spyros A. Karamanos,et al.  Finite element analysis of buried steel pipelines under strike-slip fault displacements , 2010 .

[31]  A. Barbian High Resolution Ultrasonic In-Line Inspection: Added Value and Special Applications , 2011 .

[32]  Jaroslaw A. Czyz,et al.  Multi-Pipeline Geographical Information System Based on High Accuracy Inertial Surveys , 2000 .

[33]  Eugene Paperno,et al.  A miniature and ultralow power search coil optimized for a 20 mHz to 2 kHz frequency range , 2009 .

[34]  Johan A. K. Suykens,et al.  EnsembleSVM: a library for ensemble learning using support vector machines , 2014, J. Mach. Learn. Res..

[35]  Guo Hang,et al.  A two-position SINS initial alignment method based on gyro information , 2014 .

[36]  Z.J. Wang,et al.  A variable threshold page procedure for detection of transient signals , 2005, IEEE Transactions on Signal Processing.

[37]  Wei Zhao,et al.  A Method of Buried Pipeline Route Detection Based on the Geomagnetic Field , 2012, 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring.

[38]  Jaroslaw A. Czyz,et al.  Measuring Pipeline Movement in Geotechnically Unstable Areas Using an Inertial Geometry Pipeline Inspection Pig , 1996 .

[39]  Jang Gyu Lee,et al.  An off-line navigation of a geometry PIG using a modified nonlinear fixed-interval smoothing filter , 2005 .

[40]  Graham H. Powell,et al.  Geometry Monitoring of the Trans-Alaska Pipeline , 2002 .

[41]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[42]  Woo-Jin Seo,et al.  A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding , 2011 .

[43]  Hsuan-Tien Lin A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[44]  Michael Cramer GPS / INS Integration , 1997 .

[45]  Zhang Yuan-kai Design and Implementation of Algorithms of Selecting PIG Odometer Wheels Signals , 2006 .

[46]  Michael Himmelsbach,et al.  Autonomous Off-Road Navigation for MuCAR-3 , 2011, KI - Künstliche Intelligenz.

[47]  Xiang Li,et al.  Numerical simulation and experiments of magnetic flux leakage inspection in pipeline steel , 2009 .

[48]  R. K. Stanley,et al.  Dipole Modeling of Magnetic Flux Leakage , 2009, IEEE Transactions on Magnetics.

[49]  Jinyang Zheng,et al.  Finite Element Analysis of Buried Polyethylene Pipe Subjected to Seismic Landslide , 2014 .

[50]  N. I. Krobka,et al.  Estimating quantum limits on SINS accuracy based on accurate error equations , 2014 .

[51]  B. Zadov,et al.  A Three-Axial Search Coil Magnetometer Optimized for Small Size, Low Power, and Low Frequencies , 2011, IEEE Sensors Journal.

[52]  Ludovic Duponchel,et al.  Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. , 2014, Food chemistry.

[53]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[54]  M. Paulasto-Krockel,et al.  Shock Impact Reliability and Failure Analysis of a Three-Axis MEMS Gyroscope , 2014, Journal of Microelectromechanical Systems.

[55]  Eugene Paperno,et al.  Integration of the electronics and batteries inside the hollow core of a search coil , 2010 .

[56]  Nobuaki Arai,et al.  Animal-mounted gyroscope/accelerometer/magnetometer: In situ measurement of the movement performance of fast-start behaviour in fish , 2014 .

[57]  Sanjiv Singh,et al.  Motion Estimation from Image and Inertial Measurements , 2004, Int. J. Robotics Res..

[58]  Xiaohang Wang,et al.  The inertial technology based 3-dimensional information measurement system for underground pipeline , 2012 .

[59]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[60]  Mohinder S. Grewal,et al.  Global Positioning Systems, Inertial Navigation, and Integration , 2000 .

[61]  R. A. Pappas,et al.  Mechanical Damage Characterization in Pipelines , 2001 .

[62]  Won-Ki Kim,et al.  Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor , 2014 .

[63]  Stephen Del Marco,et al.  Improved transient signal detection using a wavepacket-based detector with an extended translation-invariant wavelet transform , 1997, IEEE Trans. Signal Process..

[64]  Junjie Yao,et al.  Breakthroughs in Photonics 2013: Photoacoustic Tomography in Biomedicine , 2014, IEEE Photonics Journal.

[65]  Volkmar Frinken,et al.  Neural network language models for off-line handwriting recognition , 2014, Pattern Recognition.

[66]  Sameh Nassar,et al.  Improving the Inertial Navigation System (INS) error model for INS and INS/DGPS applications , 2003 .

[67]  Eun-Hwan Shin,et al.  Backward Smoothing for Pipeline Surveying Applications , 2005 .

[68]  Pengfei Liu,et al.  Failure analysis of natural gas buried X65 steel pipeline under deflection load using finite element method , 2010 .

[69]  H. Vincent Poor,et al.  Quickest Detection: Probabilistic framework , 2008 .

[70]  Songling Huang,et al.  Equivalent MFL model of pipelines for 3-D defect reconstruction using simulated annealing inversion procedure , 2015 .

[71]  Guo Wei,et al.  Temperature compensation method using readout signals of ring laser gyroscope. , 2015, Optics express.

[72]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[73]  Rosemarie Swanson,et al.  Algorithms for Finding the Axis of a Helix: Fast Rotational and Parametric Least-squares Methods , 1996, Comput. Chem..

[74]  Thomas Beuker,et al.  Inline Inspection of Dents and Corrosion Using “High Quality” Multi-Purpose Smart-Pig Inspection Data , 2006 .

[75]  Dieter Hausamann,et al.  A concept for natural gas transmission pipeline monitoring based on new high-resolution remote sensing technologies , 2001 .

[76]  A. S. Madhukumar,et al.  Design and performance analysis of a signal detector based on suprathreshold stochastic resonance , 2012, Signal Process..

[77]  Gerhard Kopp,et al.  Sizing limits of metal loss anomalies using tri-axial MFL measurements: A model study , 2013 .

[78]  Isaac Skog,et al.  GNSS-aided INS for land vehicle positioning and navigation , 2007 .

[79]  Michael J. O'Rouke,et al.  Response of buried pipelines subject to earthquake effects , 1999 .

[80]  Ram Gopal,et al.  Lumped parameter analytic modeling and behavioral simulation of a 3-DOF MEMS gyro-accelerometer , 2015 .

[81]  Wei Hai-xia Application of Pipe Pig in Hua-Ge Pipeline , 2011 .

[82]  Hans Burkhardt,et al.  RENNSH: A Novel \alpha-Helix Identification Approach for Intermediate Resolution Electron Density Maps , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[83]  Bob Snodgrass,et al.  Smart Utility Pigs Used to Determine and Monitor Pipeline Out-of-Straightness, With Specific Reference to Inspection of BP Alaska’s 10” Northstar Crude Oil Pipeline , 2004 .

[84]  Cheng Yu,et al.  The Development of Micromachined Gyroscope Structure and Circuitry Technology , 2014, Sensors.

[85]  Seunghee Kim,et al.  An SVM-based high-quality article classifier for systematic reviews , 2014, J. Biomed. Informatics.