Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir

Abstract

Low salinity water alternating immiscible gas CO₂ (Immiscible CO₂-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO₂ flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO₂-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO₂-LSWAG injection outperforms conventional immiscible CO₂ and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of Catboost and LightGBM used in this study produced R² scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of CatBoost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications.