Abstract
In recent years, the demand for heavy oil has increased due to its abundant availability and low cost. However, the extraction of heavy oil poses a significant challenge due to its high viscosity and low mobility. Therefore, various methods have been developed to enhance the recovery of heavy oil, including the use of catalysts. This study has created a unique simulation approach that uses liquid catalysts (LCs) to improve heavy oil recovery. In this work, laboratory testing dataset and numerical simulation studies were used to examine the potential of applying LCs as an alternative chemical agent for enhancing heavy oil recovery. CMG-STARS and CMOST modules were used to historical match the laboratory scale results of two sand-pack flooding experiments (water flooding and liquid catalyst flooding in tertiary recovery mode). Moreover, a sensitivity study was conducted to apply a wide range of assumptions to determine the most effective process controlling parameters. Finally, oil production optimization is performed using a genetic algorithm (particle swarm optimization) by selecting the optimum-operating parameters. In comparison to typical water flooding, the results revealed a discernible rise in the heavy oil recovery factor (RF) when injecting LCs. The simulation results showed that the optimized production strategy could increase the ultimate oil recovery by up to 45.06%. The injection rate, slug size, and injection temperature were found to be significant factors in optimizing the production of heavy oil. This simulation approach can be used to optimize the production of heavy oil using acidic Mo-Ni based liquid catalyst in different reservoirs.
Published Version
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