Abstract
The air drilling technology , which is a new kind of drilling technology , has got a wide range of applications at home and abroad. Compared with conventional drilling technology, it has a large number of advantages, such as lower cost, faster drilling speed and less pollution. However, lots of questions like drilling tools out of operation frequently were exposed during the gas drilling which restricted the development of air drilling technology. Thus, it has great significance to hammer at studying the air drilling accident diagnostic techniques. The paper has a comprehensive introduction about air drilling principle and the cause of the accident in the process of air drilling. Meanwhile, the paper has proposed an improved particle swarm algorithm for optimization of fuzzy neural network based on new development in automation and intelligent technology and has carried out a positive analysis by using it. The new model in the convergence speed, adaptive value and diagnostic error, etc. is the optimal by comparing with BP neural network and PSO neural network. It can improve the subjective shortcoming of traditional methods that the staff in the field operation analyses data through the real-time monitoring system and the air drilling experts identify some characteristics of air drilling accident causes to reduce errors, improve the accuracy of diagnostics and become more intelligent.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Artymiuk J, Sokalski M (2004) The new drilling control and monitoring system. Acta Montanistica Slovaca 3:145–151
Bie FF, Pei JF, Lv FX (2014) Research on fault diagnosis method for crankshaft of drilling pump based on operational deflection analysis. Adv Mater Res, Trans Tech Publ 989:2899–2903
Chen G, Chen X et al (2006) The application of air and air/foam drilling technology in tabnak gas field, Southern Iran. In: IADC/SPE Asia Pacific drilling technology conference and exhibition, vol 2006, no 11. Society of Petroleum Engineers, Dallas, pp 13–15
Cooper LW, Hook RA, Payne BR (1997) Investigation of explosion occurrence in underbalanced drilling. In: deep drilling and production symposium of petroleum engineer of AIME. Society of Petroleum Engineers, Dallas, pp 17–19
Dashevskiy D, Dubinsky V et al (1999) Application of neural networks for predictive control in drilling dynamics. In: SPE annual technical conference and exhibition. Society of Petroleum Engineers, Dallas
Guo B, Ghalambor A (2002) Gas volume requirements for underbalanced drilling: deviated holes. PennWell Books, Tulsa
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol IV. IEEE Service Center, Piscataway, pp 1942–1948
Kukolj D, Kulic F, Levi E (2000) Design of the speed controller for sensorless electric drives based on AI techniques: a comparative study. Artif Intell Eng 14(2):165–174
Leng G, McGinnity TM, Prasad G (2005) An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst 150(2):211–243
Li XF, Xu JP et al (2004) The establishment of self-adapting algorithm of BP neural network and its application. Syst Eng-Theory Pract 5:001
Lyons WC, Guo B et al (2009) Air and gas drilling manual: applications for oil and gas recovery wells and geothermal fluids recovery wells. Gulf Professional Publishing, Houston
McGuire LL, McGuire W, Brych F (2000) Mud separator monitoring system. US Patent 6,105,689
Ponce-Cruz P, Ramírez-Figueroa DF (2010) Neuro-fuzzy controller theory and application. Intelligent Control Systems with LabVIEW, pp 89–122
Rajakarunakaran S, Venkumar P et al (2008) Artificial neural network approach for fault detection in rotary system. Appl Soft Comput 8(1):740–748
Rumelhart DE, Mcclelland JL (1987) The PDP research group. The MIT Press, Massachusetts
Shahbazi K, Mehta SA et al (2007) Investigation of explosion occurrence in underbalanced drilling. In: Production and operations symposium. Society of Petroleum Engineers
Su YN, Huang HC et al (2009) Oil gas drilling technology in China: past and present. China Oil Gas 16(4):36–40
Tang X, Qiu G et al (2007) A clustering algorithm based on particle swarm optimization and self-organizing map. J-Huazhong Univ Sci Technol Nat Sci Ed 35(5):31
Venkatasubramanian V, Chan K (1989) A neural network methodology for process fault diagnosis. AIChE J 35(12):1993–2002
Wei X, Pan H (2010) particle swarm optimization and intelligent fault diagnosis. National Defence Industry Press, Beijing (In Chinese)
Willersrud A, Blanke M et al (2015) Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection. J Process Control 30:90–103
Wu S, Er MJ (2000) Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans Syst, Man, Cybern, Part B: Cybern 30(2):358–364
Guo XR, Xu XZ (2011) Research on intelligent decision analysis system of drilling complex circs and accident based on network. In: 3rd World Congress in Applied Computing, Computer Science, and Computer Engineering, pp 815–831
Yang G, Tang X (1998) Evaluation method of the quality of forecast model. China Soft Sci 11:86
Xu XZ, Guo XR (2014) Research on intelligent diagnosis and processing system for drilling accident. In: Fifth international conference on intelligent systems design and engineering applications (ISDEA), 2014. IEEE, New York, pp 817–820
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhang, D., Zhu, T., Lv, Y., Hou, J., He, Y. (2017). Research on Fault Diagnosis for Air Drilling Based on an Improved PSO for Optimization of Fuzzy Neural Network. In: Xu, J., Hajiyev, A., Nickel, S., Gen, M. (eds) Proceedings of the Tenth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 502. Springer, Singapore. https://doi.org/10.1007/978-981-10-1837-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-10-1837-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1836-7
Online ISBN: 978-981-10-1837-4
eBook Packages: EngineeringEngineering (R0)