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Oil Production Optimization in Petroleum Reservoirs by Ant Algorithm
1Institute of Petroleum Engineering - University of Tehran
2Institute of Petroleum Engineering - University of Tehran
Citation Information: Chemical Product and Process Modeling. Volume 3, Issue 1, ISSN (Online) 1934-2659, DOI: 10.2202/1934-2659.1166, March 2008
- Published Online:
The objective of the research presented in this paper is to investigate the effectiveness of a new stochastic algorithm to obtain maximum hydrocarbon production rate in petroleum reservoirs. The hydrocarbon production rate is a function of several variables. Some of these variables are production well location, production well flow pressure, injection well location and fluid injection rate. Changing any of the variables will change the hydrocarbon production rate value. By using stochastic optimization techniques, there is no limit to the number of decision variables that can be optimized simultaneously. The presented method in this paper is a new metaheuristic algorithm which enables us to analyze a system of mathematical equations containing a large number of decision variables and determine the optimum values of them that should give the most economic result. This is referred to as the Ant Colony Optimization Algorithm. Ant Colony Optimization technique leads us to lower computational cost to optimize complicated problems too. Although applied to discrete domains, this algorithm with some modifications has been applied to continuous optimization. Here, a numerical simulation is initially applied for modeling the hydrocarbon reservoir and then the results are analyzed and furthermore optimized to enhance profitability using Ant Colony Optimization algorithm. The developed code in MATLAB environment, based on Ant Colony Optimization, is able to estimate production and injection wells location, fluid injection rate and well flow pressure which would result in improved hydrocarbon production. This algorithm is simple to implement and the results of case studies show its ability to provide fast and accurate solutions. Results prove the reliability of algorithm for solving a large class of optimization problems in petroleum engineering.