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Application of an improved harmony search algorithm in well placement optimization using streamline simulation

Afshari, S ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1016/j.petrol.2011.08.009
  3. Publisher: 2011
  4. Abstract:
  5. Optimal well placement is a crucial step in efficient reservoir development process which significantly affects the productivity and economical benefits of an oil reservoir. However, it is a complex and challenging problem due to the different engineering, geological and economical variables involved. This leads to a very large number of potential scenarios that must be evaluated using numerical reservoir simulations. The key points in such an optimization process are using a fast function evaluation tool and development of an efficient and robust optimization algorithm that can find good solutions with a minimum required number of function evaluations. This study presents an approach that employs an Improved Harmony Search (IHS) algorithm in combination with streamline simulation to determine the optimum well locations in a reservoir. Several case studies including both synthetic and real ones have been considered, and the performance of IHS has been compared to that of some stochastic optimization algorithms including Particle Swarm Optimization (PSO), Simulated Annealing (SA), Genetic Algorithm (GA), and the classical Harmony Search (HS) algorithm. Results of the case studies demonstrate the superiority of IHS algorithm over the other mentioned algorithms in providing fast and accurate solutions. These findings are very promising as the IHS is a very simple and easy-to-implement algorithm which can be widely used in other areas of reservoir development process
  6. Keywords:
  7. Improved harmony search ; Particle swarm optimization ; Streamline simulation ; Development process ; Economical benefits ; Harmony search ; Harmony search algorithms ; Keypoints ; Oil reservoirs ; Optimal well placement ; Optimization process ; Reservoir simulation ; Robust optimization algorithm ; Stochastic optimization algorithm ; To a very large ; Well location ; Well placement optimization ; Function evaluation ; Genetic algorithms ; Oil field development ; Petroleum reservoir evaluation ; Petroleum reservoirs ; Particle swarm optimization (PSO) ; Computer simulation ; Genetic algorithm ; Hydrocarbon reservoir ; Oil field ; Optimization ; Simulated annealing
  8. Source: Journal of Petroleum Science and Engineering ; Volume 78, Issue 3-4 , 2011 , Pages 664-678 ; 09204105 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0920410511001999