Prediction of oil production rate using vapor-extraction technique in heavy oil recovery operations

Heavy oil and bitumen are major parts of the petroleum reserves in north of America. Owning to this fact and produce this type of oils various methods could be considered. Vapor extraction (VAPEX) method is one of the promising methods that have been executed successfully through North America, specifically in Canada, and is a solvent-based approach. The authors present the implication of the new type of network approach with low parameters called least square support vector machine (LSSVM) in prediction of the oil production rate via VAPEX method. To evaluate and examine the accuracy and effectiveness of both developed models in estimation oil production rate via VAPEX method, extensive experimental VAPEX data were faced to the two addressed models. Moreover, statistical analysis of the output results of the LSSVM was conducted. Based on the determined statistical parameters, the outcomes of the LSSVM model has lower deviation from relevant actual value. Knowledge about oil production via enhanced oil recovery (EOR) methods could help to select and design more proper EOR approach for production purposes. Outcomes of this research communication could improve precision of the commercial reservoir simulators for heavy oil recovery specifically in thermal techniques.