Bicheng Yan

3 weeks ago | jpt.spe.org | Zhenzhen Wang |Bicheng Yan |Quang Nguyen |Xue Guo

This year’s History Matching and Forecasting selections highlight innovations in surrogate modeling, artificial intelligence, and well-test analysis. These three papers leverage machine learning and hybrid methods to tackle challenges in forecasting, optimization, and reservoir characterization. In paper SPE 220002, the authors introduce the embed-to-control observe (E2CO) framework, a deep-learning surrogate model for reservoir performance forecasting and life-cycle optimization.