@article{scholars13232,
         journal = {Shiyou Kantan Yu Kaifa/Petroleum Exploration and Development},
       publisher = {Science Press},
           pages = {357--365},
            year = {2020},
           title = {Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection {\aa}??{\"a}??{\"a}??{\aa}.Y{\cc}Y?{\cc}??{\cc}1/2?{\cc}??{\cc}??{\ae}3?{\ae}o?{\aa}1/4?{\aa}??{\ae}21{\`e}??{\"a}?{\S}{\'e}??{\'e}c?{\ae}u?},
          number = {2},
          volume = {47},
            note = {cited By 9},
             doi = {10.11698/PED.2020.02.14},
        abstract = {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. {\^A}{\copyright} 2020, The Editorial Board of Petroleum Exploration and Development. All right reserved.},
        keywords = {Forecasting; Mean square error; Neural networks; Petroleum industry, Bayesian regularization algorithms; Determination coefficients; Error distributions; Hydrocarbon reservoir; Minimal information; Predictive modeling; Production forecasting; Water-flooding reservoir, Gas industry},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086082114&doi=10.11698\%2fPED.2020.02.14&partnerID=40&md5=7bb9daf8eae525888d190bddb9da0e0a},
            issn = {10000747},
          author = {Mamo, N. B. and Dennis, Y. A.}
}