TY - JOUR ID - 6644 TI - Neural Network Meta-Modeling of Steam Assisted Gravity Drainage Oil Recovery Processes JO - Iranian Journal of Chemistry and Chemical Engineering JA - IJCCE LA - en SN - 1021-9986 AU - Alali, Najeh AU - Pishvaie, Mahmoud Reza AU - Taghikhani, Vahid AD - Faculty of Chemical & Petroleum Engineering, Sharif University of Technology, Tehran, I.R. IRAN Y1 - 2010 PY - 2010 VL - 29 IS - 3 SP - 109 EP - 122 KW - artificial neural network (ANN) KW - Meta-modeling KW - Surrogate modeling KW - Enhanced oil recovery KW - Steam Assisted Gravity Drainage (SAGD) DO - 10.30492/ijcce.2010.6644 N2 - Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propagation (BEP) learning algorithm to provide a versatile SAGD forecasting and analysis framework. The constructed neural network architectures are capable of estimating the recovery factors of the SAGD production as an enhanced oil recovery method satisfactorily. Rigorous studies regarding the hybrid static-dynamic structure of the proposed network are conducted to avoid the over-fitting phenomena. The feed forward artificial neural network-based simulations are able to capture the underlying relationship between several parameters/operational conditions and rate of bitumen production fairly well, which proves that ANNs are suitable tools for SAGD simulation. UR - https://ijcce.ac.ir/article_6644.html L1 - https://ijcce.ac.ir/article_6644_13b6a8eabea0b3db59a0c78bce67a970.pdf ER -