Bayesian Regularization BP Neural Network Model for Predicting Oil-gas Drilling Cost
Oilgas drilling cost is an important indicator which reflects the economic benefit of oilfield enterprise. Following taking the characteristics of oilgas drilling cost which belongs to subsidiary of CNPC (China National Petroleum Corporation) into account, determinants concerning oilgas drilling cost are identified. Bayesian Regularization Back Propagation Neural Network (BRBPNN) is proposed to predict oilgas drilling cost. Through comparing with LevenbergMarquardt Back Propagation, Momentum Back Propagation, Variable Learning Rate Back Propagation models in terms of prediction precision, convergence rate and generalization ability, the results exhibit that BRBPNN has better comprehensive performances. Meanwhile, results also exhibit that BRBP model has the automated regularization parameter selection capability and may ensure the excellent adaptability and robustness. Thus, this study lays the foundation for the application of BRBPNN in the analysis of oilgas drilling cost prediction.
Bayesianregularization BPneuralnetwork Oil-gasdrillingcostprediction
Zhao Yue Zhao Songzheng Liu Tianshi
Management SchoolNorthwestern PolytechnicaluniversityXi’an, China School of Computer Science Xi’an Shiyou University Xi’an, China
国际会议
广州
英文
1-5
2011-05-13（万方平台首次上网日期，不代表论文的发表时间）