NEURAL NETWORK META-MODELING OF STEAM ASSISTED GRAVITY DRAINAGE OIL RECOVERY PROCESSES

Abstract

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.