Abstract
The objective of the paper is to develop a mixed integer nonlinear programming (MINLP) model for optimum design and scheduling of offshore oil and gas field development in respect to simultaneous consideration of economic and environmental impact. The model is utilized as a tool for decision making management in conceptual stage. Nonlinear reservoir behavior and floating demand constraint are incorporated to improve accuracy of the solution.
This paper utilizes mathematical programming techniques to address the design and scheduling problem of offshore oil and gas field development. Field development problem is first formulated into a multi-objective MINLP model incorporating many realistic features such as nonlinear reservoir behavior and floating demands. The objectives are to maximize net present value (NPV) and minimize total environmental impact (TEI) simultaneously. Environmental impact is assessed using the ReCiPe2016 method. Augmented ε-constraint method (AUGMECON) is then employed to solve the proposed multi-objective MINLP model to generate the Pareto-optimal front that is able to assist decision maker selecting the most preferred solution.
The performance of the proposed modelling framework is investigated on a set of problem which consists of 2 reservoirs, 2 FPSOs, 2 customers and 5-years planning horizon. First model with single objective function to maximize NPV can be solved effectively within short computational time. The solution gives optimum decision of design, investment, production schedule, and transportation regardless the environmental impact. Then, simultaneous optimization of multi-objective MINLP with different value of ε-constraint generates multiple development schemes and objective function values. The results indicate trade-off between maximizing NPV and minimizing TEI. It is possible to obtain maximum NPV of USD 2.4 trillion at the expense of TEI which is 307.518 or to generate minimum TEI of 16.65 at the expense of NPV which is USD 74.368 billion. All possible solutions within extreme values range are presented in form of a Pareto-optimal front where TEI and NPV are plotted in x and y-axis respectively. It will assist the company to select the most preferred solution based on NPV. Consequently, the selected option brings corresponding value of TEI. Additionally, the Pareto optimal front also allows decision maker to have more flexibility to compromise between economic and environmental issues.
This is the first study to consider environmental impact in the offshore oil and gas field development. Many realistic operational features such as nonlinear reservoir behavior and floating demands are also incorporated. In addition to that, the proposed framework yields a powerful tool to assist decision maker selecting the most preferred solution that satisfies their criteria in both economic and environmental aspects.
