A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem

•Joint CCS-EOR planning in a multi-reservoir EOR system and a long-term horizon is studied.•The deterministic model proposed in the literature is improved by reducing the binary variables.•The deterministic model is extended to stochastic with endogenous uncertainty. Computational experiments over two case studies are provided.•The results confirm that incorporating uncertainty may cause significant cost-saving. Carbon-capture-and-storage (CCS) is one of the leading technologies to reduce CO2 emissions. A commercial way to deploy CCS on a large scale is to sequestrate CO2 in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this manner, not only the CO2 emission is reduced, but also the oil production increases. The collaborative CCS-EOR planning problem determines the proper allocation of available CO2 to depleted reservoirs and the scheduling of the EOR operations. This problem is of great importance, especially when there are multiple oil reservoirs. This paper presents a deterministic mixed-integer linear programming model as an improvement of an existing model in the literature. Then, it is extended to a multistage stochastic model with endogenous uncertainty in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the time of uncertainty realization is decision-dependent. Our deterministic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables to about one-third. Also, providing the possibility of selecting pipeline types among different options as well as incorporating uncertainty may lead to a significant cost-saving. The proposed models are examined over two case-studies taken from the literature. The results indicate that in comparison to the deterministic model, the cost-saving achieved by incorporating uncertainty is about 8.8%, on average.