Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

作者：NEGASH Berihun Mamo,YAW Atta Dennis

摘要：As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

发文机构：University Teknologi PETRONAS

关键词：neuralnetworksmachinelearningattributeextractionBayesianregularizationalgorithmproductionforecastingwaterflooding

分类号： TE328[石油与天然气工程—油气田开发工程]TP183[自动化与计算机技术—控制理论与控制工程]