A Mixed-Integer Nonlinear Programming Model for Solving Integrated Oil and Gas Supply Chain Problem by Considering Enhanced Oil Recovery Methods

Abstract

This paper presents a model to solve a multi-objective optimization problem for optimal oil fielddevelopment and supply chain management (SCM) of oil and gas, considering Enhanced Oil Recovery (EOR)methods in both upstream and midstream sectors. Unlike previous studies that primarily investigated EOR inthe upstream sector, this study focuses on integrating EOR methods within a comprehensive supply chainmodel. The problem is formulated as a mixed integer nonlinear program (MINLP) to accurately capture thecomplexities and interdependencies of oil field development and SCM. To facilitate solution, the multiobjective problem is converted into a single-objective problem using the LP-metric method. The transformedproblem is then solved using the BARON solver within the GAMS software environment. To evaluate theefficiency and robustness of the proposed solution method, a set of 15 test problems with varying dimensionswas solved. The results demonstrate that the solution method is highly efficient for small-size problems,achieving a relative gap of 0.01 in less than 100 seconds. However, the computational time increasessignificantly as the problem size grows, highlighting the challenges of scaling the model for larger and morecomplex scenarios. This study provides a novel approach to incorporating EOR methods into an integratedsupply chain model, offering valuable insights for optimizing oil and gas field development and SCMstrategies.