Gas analysis by in situ combustion in heavy-oil recovery process: Experimental and modeling studies

Abstract

Enormous efforts have been made to facilitate produced-gas analyses by in situ combustion implication in heavy-oil recovery processes. Robust intelligence-based approaches such as artificial neural network (ANN) and hybrid methods were accomplished to monitor CO2/O2/CO. Implemented optimization approaches like particle swarm optimization (PSO) and hybrid approach focused on pinpointing accurate interconnection weights through the proposed ANN model. Solutions acquired from the developed approaches were compared with the pertinent experimental in situ combustion data samples. Implication of hybrid genetic algorithm and PSO in gas analysis estimation can lead to more reliable in situ combustion quality predictions, simulation design, and further plans of heavy-oil recovery methods. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.