Oil and gas production system optimization using particle swarm optimization

Abstract

Field development planning and optimization of production system are main factors while developing oil and gas fields. The main aim of any oil and gas development is to maximize the economic value of projects under study. Reservoir engineers focus on optimizing wells placement, trajectory, and type. Facility and production engineers have as a role to set and optimize production system elements in terms of placement, sizing and their interconnection. Literature review showed an important research work in the field of production system optimization. This latter is typically classified as tree-like, nodes and segments, multilayers problem, yet recent research work only dealt with two layers optimization problems. In addition, recent literature presented the use of genetic algorithm (GA), which is a population-based method, as an efficient optimizer. Another optimization technique, particle swarm optimization (PSO), is introduced in this type of problems. Both methods’ efficiency was compared and PSO outperformed GA in terms of convergence time and value. Literature showed that many improvements were applied to the standard algorithm. Adaptive particle swarm optimization is applied in parallel with two newly introduced improvements in this work: Multiple runs initialization, and Restart improvement. The novel improved method showed better results than the standard PSO as the complexity of the problem is increased starting from two-layers up to four-layers high complexity. This introduced method showed robustness and high efficiency in handling multiple layers problems.