Optimal Strategies for Carbon Dioxide Enhanced Oil Recovery under Uncertainty

MIGUEL ZAMARRIPA; DEMIAN J. PRESSER; DIEGO C. CAFARO; VANINA G. CAFARO

San Diego, California

Congreso; PSE 2018; 2018

This work presents a two-stage stochastic programming model to optimize the expected net present value (ENPV) of CO2-EOR projects under uncertainty. The mathematical formulation relies on a multi-period planning approach aimed to find the optimal exploitation strategy for a mature oil reservoir. Given uncertain prices and productivity scenarios, the model sets the most convenient time to launch the CO2-EOR project, and establishes efficient operating conditions over the planning horizon. It determines the number of production and injection wells to operate at every period, the CO2 injection rate in every well, and the timing for maintenance and conversion tasks. The problem complexity grows rapidly with the number of wells and scenarios considered, resulting in a large-scale decision-making problem. Well productivity forecast functions are nonlinear (typically hyperbolic), yielding a mixed integer nonlinear (MINLP), nonconvex formulation. A moving horizon framework is adopted to take recourse actions when uncertain production parameters are revealed. The proposed approach helps operators to increase CO2-EOR profitability by minimizing losses in low-price and productivity scenarios and maximizing the gain under more promising conditions