Robust Optimal Crude Oil Supply Chain Planning and Oilfield Development under Uncertainty: Case Study of the National Iranian South Oil Company

Petroleum industry activities can be divided into upstream and downstream sectors. Major upstream activities include exploration of oil reservoirs, drilling and extraction of crude oil, as well as production oil for supply to domestic refineries and export terminals. Refining crude oil and producing oil derivatives are considered in the downstream sector. In this paper, we study optimization of upstream activities of the oil industry and model the crude oil supply chain (COSC) management problem is modeled. The aim of this research is to provide a mathematical optimization model to assist in decision making regarding optimal exploitation of oilfields and COSC management that maximizes the net present palue (NPV) of crude oil production. For this purpose we use a mixed integer linear programming (MILP) model to project strategic and operational decisions for a given time horizon. We use the robust possibilistic programming (RPP) approach to deal with the inherent stochastic or epistemic uncertainty of oil price and demand. Finally, we use the GAMS (CPLEX Solver) software to apply this model and assess its applicability to the National Iranian South Oil Company. The numerical results confirm the applicability of the MILP model, which can be used to maximize the NPV of crude oil for the selected time horizons.