Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network

• Published in: Energy & fuels 2010-04, Vol.24 (4), p.2520-2526
• Main Authors: Ahmadloo, Farid, Asghari, Koorosh, Renouf, Gay
• Format: Article
• Language: English
• Subjects: Fossil Fuels
• ISSN: 0887-0624; 1520-5029
• DOI: 10.1021/ef9013218

This study presents a successful application of multivariate partial least-squares (PLS), response surface methodology (RSM), and artificial neural network (ANN) to develop a new diagnostic tool for performance prediction of waterflooding in heavy oil reservoirs. The data used in this study consist of 120 operational and reservoir parameters for 177 waterfloods in heavy and medium oil reservoirs in western Canada (i.e., Alberta and Saskatchewan). This study also used 15 numerically devised indices for performance evaluation of water injection, based on collected injection and production histories of studied waterfloods. To reduce the number and complexity of input parameters of ANN models, a comprehensive PLS analysis was conducted. In addition to parameter selection, the PLS model provided a more in-depth understanding of differences between heavy and medium oil waterfloods. Next, RSM was used to improve the quality of the database for a selected combination of 38 reservoir and operational parameters by predicting some of the missing data. Finally, ANN models were developed using the feed-forward backpropagation algorithm with momentum for error minimization. The developed models show the superior ability of the ANN for creation of an efficient reservoir engineering tool for fast performance prediction of waterfloods using easily obtainable operational and reservoir parameters. The developed models in this study can be incorporated into reservoir engineering, risk assessment, and production optimization software programs to improve the quality of predictions based on more than 50 years of waterflooding experience in western Canada.