A proposed model for predicting the drilling path based on hybrid Pso-Bp neural network
作者： A. S. Elons , Dalia Ahmed Magdi , M. Y. Elgendy
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摘要: In the early days Hydrocarbon wells were normally drilled vertically which rarely requires making decisions. As technology progressed, capability to do some deviation of bit was used resulting in deviated or directional follow formation. This type drilling real-time directions this paper, aim is develop a intelligent system that can model and predict optimal path for new well before actual done based on geological layers pre-drilled surrounding wells. presented work, hybrid method stochastic population-based search algorithm (Particle Swarm Optimization) gradient (Back Propagation) train Multiplicative Neural Network. The proposed network topology training algorithms have shown superiority traditional Network determining path. generated plans achieved more than 88% decision accuracy measured according
sci-hub.se 参考文章(16)
R.C. Eberhart, Yuhui Shi, Tracking and optimizing dynamic systems with particle swarms congress on evolutionary computation. ,vol. 1, pp. 94- 100 ,(2001) , 10.1109/CEC.2001.934376
Terrence Henry Quinn, James Peter Dwyer, Christopher Allen Wolfe, Stephen A. Morris, Daniel F. Coope, Formation Evaluation Logging While Drilling (LWD) in Unconventional Reservoirs for Production Optimization SPE Eastern Regional/AAPG Eastern Section Joint Meeting. ,(2008) , 10.2118/119227-MS
J.C. Rasmus, C. Esmersoy, J. Seydoux, A. Hawthorn, LWD For Imaging, Wellbore Placement, And Formation Evaluation Offshore Technology Conference. ,(2005) , 10.4043/17646-MS
R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model soft computing. ,vol. 7, pp. 1157- 1163 ,(2007) , 10.1016/J.ASOC.2006.01.003
Carl Tape, Qinya Liu, Alessia Maggi, Jeroen Tromp, Seismic tomography of the southern California crust based on spectral‐element and adjoint methods Geophysical Journal International. ,vol. 180, pp. 433- 462 ,(2010) , 10.1111/J.1365-246X.2009.04429.X
Ashok Kumar Patel, Snehamoy Chatterjee, Computer vision-based limestone rock-type classification using probabilistic neural network Geoscience frontiers. ,vol. 7, pp. 53- 60 ,(2016) , 10.1016/J.GSF.2014.10.005
S. Rangwala, D. Dornfeld, Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring Journal of Engineering for Industry. ,vol. 112, pp. 219- 228 ,(1990) , 10.1115/1.2899578
Tsili Wang, Jack Signorelli, Finite‐difference modeling of electromagnetic tool response for logging while drilling GEOPHYSICS. ,vol. 69, pp. 152- 160 ,(2004) , 10.1190/1.1649383
P. M. Wong, F. X. Jian, I. J. Taggart, A CRITICAL COMPARISON OF NEURAL NETWORKS AND DISCRIMINANT ANALYSIS IN LITHOFACIES, POROSITY AND PERMEABILITY PREDICTIONS Journal of Petroleum Geology. ,vol. 18, pp. 191- 206 ,(1995) , 10.1111/J.1747-5457.1995.TB00897.X
Danilo Machado L. da Silva, Rafael F. Solano, Antonio Roberto de Medeiros, Marcos V. Rodrigues, Fabio B. de Azevedo, Pipeline Shore Approach Installation by Horizontal Directional Drilling Volume 4B: Pipeline and Riser Technology. ,(2013) , 10.1115/OMAE2013-10851