Prediction of Differential Pipe Sticking by Using Probabilistic Artificial Neural Network in Offshore Persian Gulf Oil Fields

Document Type : Research Paper

Authors

1 Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran

2 Group of Mechatronics, K. N. Toosi University of Technology, Tehran.

3 Department of Research and Development, Iranian Offshore Oil Company, Tehran

Abstract

Differential pipe sticking is one of the usual and hazardous problems during drilling operation that leads to increasing the total cost. Nowadays minimizing the risk of stuck occurrence is one of the priorities and main goals in petroleum industry. In the past, statistical methods were applied to investigate differential pipe sticking, but these methods cannot remarkably predict the non-linear behaviors. Artificial neural network is a novel method for solving engineering problems. This method is capable of considering the effective parameters at the same time and has the ability of direct generalization and learning from the field data (due to the errors and uncertainties). In this paper, the data from 63 wells of the offshore Persian Gulf oil fields were applied and by using a probabilistic neural network, a predictive model has been developed. High accuracy of this model in predicting differential pipe sticking allows it to be applied in well planning as well as real time drilling operations. Analyzing the result of neural network, associated with engineering viewpoint leads to preventing differential pipe sticking by optimizing the effective parameters.

Keywords

References

مراجع
[1] M-I L.L.C. Drilling Fluid Engineering Manual, Version 2.0-4/01, M-I Drilling Fluids Company, 1998.
[2] Siruvuri C., Nagarakanti S. & Samuel R., “Stuck pipe prediction and avoidance: a convolution neural network approach”. IADC/SPE Drilling Conference, Miami, Florida, USA, 21-23 February. SPE. 98378, pp. 1-6, 2006.
[3] Santos H., “Differentially stuck pipe: early diagnostic and solution”, IADC/SPE Drilling Conference, New Orleans, Louisiana, USA, 23–25 February. SPE 59127, pp. 1-5, 2000.
[4] Wisnie A.P. & Zhu Zh., “Quantifying stuck pipe risk in Gulf of Mexico oil and gas drilling”, SPE 69th Annual Technical Conference and Exhibition, New Orleans, LA, U.S.A., 25-28 September. SPE 28298, pp. 69-80, 1994.
[5] Miri R., Sampaio J., Afshar M. & Lourenco A., “Development of artificial neural networks to predict differential pipe sticking in Iranian offshore oil Fields”, International oil conference and exhibition, Veracruz, Mexico, 27-30 June. Paper SPE 108500, pp. 1-15, 2007.
[6] Murillo A., Neuman J. & Samuel R., “Pipe sticking prediction and avoidance using adaptive fuzzy logic and neural network modeling”, SPE Production and Operations Symposium, Oklahoma City, Oklahoma, U.S.A., 4-8 April, SPE 120128, pp. 1-15, 2009.
[7] Malallah A. & Nashawi I.S., “Estimating the fracture gradient coefficient using neural networks for a field in the Middle East”, Journal of Petroleum Science and Engineering, Vol. 49, pp. 193–211, 2005.
[8] Salehi S., Hareland G., Khademi Dehkordi K., Ganji M. & Abdollahi M., “Casing collapse risk assessment and depth prediction with a neural network system approach”, J. Petroleum Science and Engineering, Vol. 69, pp. 156–162, 2009.
[9] Ali J.K., “Neural networks: a new tool for the petroleum industry?”, European Petroleum Computer Conference, Aberdeen, U.K., 15-17 March. SPE. 27561, pp. 217-231, 1994.
[10] Rabia H., Oilwell drilling engineering: principles and practice, First published, Graham & Trotman, 1985.
[11] Reid P.I., Meeten, G.H., Way, P.W., Clark, P., Chambers, B.D., Gilmour, A. & Sanders, M.W., “Differential-sticking mechanisms and a simple wellsite test for monitoring and optimizing drilling mud properties”, SPE Drilling & Completion, Vol. 15, No.2, pp. 97-104, 2000.
[12] جی.شالکف ر.، ترجمه(جورابیان، م.، زارع، ط.، استوار، ا.)، شبکه‌های عصبی مصنوعی، ویرایش اول، انتشارات دانشگاه شهید چمران اهواز، 1382.
[13] منتظر غ.ع.، قدسیان، م.، نصیری، ف.، جوان، م.، اقبالزاده، ا.، «پیش‌بینی هوشمند فراآبی ناشی از پایه پل با استفاده از شبکه عصبی مصنوعی مبتنی بر تابع پایه شعاعی»؛ مجله فنی و مهندسی مدرس، شماره 14، صفحات 62-49، زمستان 82.
[14] Demuth H. & Beale M., Neural network toolbox for use with MATLAB, Mathworks, Inc, USA. User’s Guide, Fifth Printing, Version 3, 1998.
[15] پیکتن ف.، ترجمه(غضنفری، م.، ارکات، ج.)، شبکه‌های عصبی(اصول و کارکردها)، ویرایش اول، مرکز انتشارات دانشگاه علم و صنعت ایران، 1383.
[16] Fawcett T., “An introduction to ROC analysis”, Pattern Recognition Letters, Vol. 27, pp. 861-874, 2006.
[17] Taylor J.R., An introduction to error analysis: the study of uncertainties in physical measurements, University Science Books. pp. 128–129, 1999.
Journal of Petroleum Research
Volume 21, Issue 65 - Serial Number 65
May and June 2011
Pages 47-57

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