Economic Evaluation for Enhanced Oil Recovery Using Deterministic-Stochastic Particle Swarm Optimization (PSO)

Abstract

The utilization of CO2 gas from the oil and gas fields or industry for the purposes of Enhanced Oil Recovery has a dual purpose which to increase reserves and also to help deal with the greenhouse effect. The objective is to analyze economic uncertainty in decision making based on stochastic method analysis with the objective function of Net Present Value (NPV).

This research was conducted by two methods, deterministic and stochastic method. In this field development, producers were converted to injector and the economic parameter such as Net Present Value (NPV), Internal Rate of Return (IRR), and Pay Out Time (POT) are feasible to be applied in the field. Then the analysis of the optimal cases is applied to Field X both technically and economically with the stochastic method. This study applied stochastic method Particle Swarm Optimization (PSO) with objective function Net Present Value (NPV) and limit parameters for the number of injection wells and CO2 injection volume. There are scenarios using different parameter of stochastic method Particle Swarm Optimization (PSO): Inertia Weight and Learning Factor. The changes in the main parameters of Particle Swarm Optimization (PSO) method (Inertia Weight and Learning Factor) indicate that Inertia Weight is the most influential for the simulation.