Prediction of oil well production: A multiple-neural-network approach

Article type: Research Article

Authors: Nguyen, H.H.^a | Chan, C.W.^{a; *} | Wilson, M.^b

Affiliations: [a] Department of Computer Science/Energy Informatics Laboratory, University of Regina, Regina Sask S4S 0A2, Canada | [b] Office of Energy and Environment, University of Regina, Regina Sask S4S 0A2, Canada

Correspondence: [*] Corresponding author: E-mail: chan@cs.uregina.ca

Abstract: This study presents an application using both single and multiple interval prediction models implemented with artificial neural networks to estimate the future production performance of oil wells. The single interval prediction model was developed using NOL (Gensym Corp., USA). The multiple neural network (MNN) model is a novel approach that combines a group of neural networks, with each component neural network being responsible for predicting a different time period. The approach is designed to improve the accuracy of long-term predictions. In addition to conducting both short and long term prediction of oil production, the study also investigates different approaches for modeling the application domain parameters. The MNN model for prediction of future well performance is applied to the time series data obtained from four pools of wells in the southwestern region of Saskatchewan, Canada. The results showed that a MNN model performed better than a single neural network model for long-term predictions.

Keywords: neural networks, petroleum production prediction

DOI: 10.3233/IDA-2004-8206

Journal: Intelligent Data Analysis, vol. 8, no. 2, pp. 183-196, 2004