An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs)

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• 作者：N. Chithra Chakra^a ; Ki-Young Song^b ; Madan M. Gupta^b ; ^{madan.gupta@usask.ca} ; Deoki N. Saraf^a
• 关键词：oil production forecasting ; black oil reservoir ; time series ; higher-order neural networks ; higher-order synaptic operation ; data preprocessing
• 刊名：Journal of Petroleum Science and Engineering
• 出版年：2013
• 出版时间：June, 2013
• 年：2013
• 卷：106
• 期：Complete
• 页码：18-33
• 全文大小：1137 K

文摘

Precise and consistent production forecasting is indeed an important step for the management and planning of petroleum reservoirs. A new neural approach to forecast cumulative oil production using higher-order neural network (HONN) has been applied in this study. HONN overcomes the limitation of the conventional neural networks by representing linear and nonlinear correlations of neural input variables. Thus, HONN possesses a great potential in forecasting petroleum reservoir productions without sufficient training data. Simulation studies were carried out on a sandstone reservoir located in Cambay basin in Gujarat, India, to prove the efficacy of HONNs in forecasting cumulative oil production of the field with insufficient field data available. A pre-processing procedure was employed in order to reduce measurement noise in the production data from the oil field by using a low pass filter and optimal input variable selection using cross-correlation function (CCF). The results of these simulation studies indicate that the HONN models have good forecasting capability with high accuracy to predict cumulative oil production.