@inproceedings { , title = {Oil well placement optimization using niche particle swarm optimization.}, abstract = {A challenging problem in oil field development project is the optimization of multi-wells placement because of the increasing in optimization variable quantities and searching space size. To overcome the limitation of traditional optimization with strict definitive objective function, a new method based on Niche Particle Swarm Optimization (NPSO) is proposed in this paper for oil field development project. The new algorithm has no restrictions on the continuity and differentiable requirement for the objective function. It also can handle the combinatorial optimization problems with large numbers of multi-dimensional variable. This algorithm is applied to optimize the multi-well placement in oil field development with the optimized object to maximize the cumulative production. This can provide the reservoir engineers a new idea and methodology for oil well placement optimization. The experimental results show that NPSO algorithm is an effective and out performance method in resolving optimization problem by using multi-dimensional variables specification to optimize the multi-well placement in oilfields}, doi = {10.1109/CIS.2012.22}, isbn = {978-1-4673-4725-9}, note = {Note: “© © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” School: iidi}, pages = {61-64}, publicationstatus = {Published}, url = {http://researchrepository.napier.ac.uk/id/eprint/6452}, keyword = {519 Probabilities & applied mathematics, QA75 Electronic computers. Computer science, Software systems, AI and Technologies, Combinatorial optimization, Niche particle swarm, optimization, Oil well placement}, year = {2024}, author = {Cheng, Guojian and An, Yao and Wang, Zhe and Zhu, Kai} }