A comprehensive techno-eco-assessment of CO2 enhanced oil recovery projects using a machine-learning assisted workflow

Abstract

Carbon dioxide enhanced oil recovery (CO2-EOR) projects not only extract residual oil but also sequestrate CO2 in the depleted reservoirs. This study develops a machine-learning-based workflow to co-optimize the hydrocarbon recovery, CO2 sequestration volume and project net present value (NPV) simultaneously. Considering the trade-off relationships among the objective functions, support vector regression with Gaussian kernel (Gaussian- SVR) proxies are coupled with multi-objective particle swarm optimization (PSO) protocol and generate Pareto optimal solutions. Taking advantage of the high computational efficacy of the proxy model, economic uncertainties introduced by tax credits, capital costs and oil price are investigated by this study. The results indicate that the tax incentive policy (Section 45Q) plays a vital role in enhancing the economic returns of CO2-EOR projects, especially under the depression of crude oil market. The proposed workflow has been successfully implemented to optimize a water alternative CO2 (CO2-WAG) injection project in a depleted oil sand in the US. The optimization results yield an incremental oil production of 15.8 MM STB and 1.37 MM metric tons of CO2 storage in a 20-year development strategy, with the highest project NPV to be 205.6 MM US dollars.