Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties

• Stochastic optimization model is proposed for oilfield planning incorporating endogenous uncertainty and fiscal rules.
• Model yields optimum investment/operating decisions maximizing expected net present value after taxes.
• Correlations among uncertain parameters are used to reduce number of scenarios.
• Lagrangean decomposition with parallel solution of the scenario subproblems allows solving large instances.
• Results show that Lagrangean decomposition is much more efficient compared to full-space method.

The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water–oil ratio and gas–oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach.