|dc.description.abstract||An efficient and optimized field development plan is a crucial and primary aspect of maximizing well productivities and improving the recovery factors of oil and gas fields, and thereby most effectively increasing profitability. In this research, we apply a meta-heuristics algorithm known as the imperialist competitive algorithm (ICA) to determine optimal well locations for maximum well productivity.
The ICA, an evolutionary algorithm that mimics socio-political imperialist competition, uses an initial population that consists of colonies and imperialists that are assigned to several empires. The empires then compete with each other, which cause the weak empires to collapse and the powerful empires to dominate and overtake their colonies.
We compared the ICA performance with that of particle swarm optimization (PSO) and the genetic algorithm (GA) in the following four optimization scenarios: 1) a vertical well in a channeled reservoir, 2) a horizontal well in a channeled reservoir, 3) placement of multiple vertical wells, and 4) placement of multiple horizontal wells. In all four scenarios, the ICA achieved a better solution than did the PSO or GA in a fixed number of simulation runs. We also applied the ICA optimization algorithm to optimize well placement, well type (producer/injector), well configuration (vertical/directional), wellbore length, and drilling schedules for a sector of a Middle East reservoir.
In addition, we conducted sensitivity analyzes on three important parameters (revolution ratio, assimilation coefficient, and assimilation angle), and the analyses show that the recommended ICA default parameters generally led to acceptable performances in our examples. However, to obtain optimum performance, we recommend tuning the three main ICA parameters with respect to specific optimization problems.||