Application of particle swarm optimization for enhanced cyclic steam stimulation in a offshore heavy oil reservoir

TL;DR

This study compares three variations of particle swarm optimization algorithms to enhance cyclic steam stimulation in offshore heavy oil reservoirs, identifying the most effective method for maximizing oil recovery.

Contribution

It introduces and evaluates Canonical, Gaussian Bare-bone, and Lévý Bare-bone PSO algorithms for optimizing oil recovery in offshore heavy oil reservoirs.

Findings

Canonical PSO yields the best optimization results.

All three PSO methods improve the objective function.

Injection steam temperature and gas composition significantly impact recovery.

Abstract

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and L\'evy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able to improve the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The L\'evy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigated in this study, it is found that the temperature of the injection steam, CO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate have less impact on the objective function.