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Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection
2020
Petroleum Exploration and Development
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
doi:10.1016/s1876-3804(20)60055-6
fatcat:uzhe4hb7rjffjmqnejjgpqabaq