LAUSR

ABSTRACT Predicting the density of bitumen after solvent injection is highly required in solvent-based recovery techniques like expanding solvent-steam assisted gravity drainage (ES-SAGD) and vapor extraction (VAPEX) in order to estimate the cumulative oil recovery by these processes. Using experimental procedures for this purpose is so expensive and time-consuming; therefore, it is crucial to propose a rapid and accurate model for predicting the effect of various solvents on the dilution of bitumen. In this study, an adaptive neuro-fuzzy interference system is introduced to estimate the effect of methane, ethane, propane, butane, carbon dioxide, and n-hexane on the density of undersaturated Athabasca bitumen in wide ranges of operating conditions. The obtained results were in an excellent agreement with experimental data with coefficients of determination (R2) of 0.99997 and 0.99948 for training and testing datasets, respectively. Statistical analyses illustrate the superiority of the proposed model in predicting the bitumen density at different conditions.