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

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 p...

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Vydané v:Petroleum exploration and development Ročník 47; číslo 2; s. 383 - 392
Hlavní autori: NEGASH, Berihun Mamo, YAW, Atta Dennis
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.04.2020
KeAi Communications Co., Ltd
Predmet:
attribute extraction
Bayesian regularization algorithm
machine learning
neural networks
production forecasting
water flooding
production forecasting
water flooding
machine learning
attribute extraction
Bayesian regularization algorithm
neural networks
ISSN:1876-3804, 1876-3804
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Popis
Shrnutí: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.
ISSN:1876-3804
1876-3804
DOI:10.1016/S1876-3804(20)60055-6