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Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm

Meisam Adibifard, Gholamreza Bashiri, Emad Roayaei, Mohammad Ali Emad
Copyright: © 2016 |Volume: 7 |Issue: 3 |Pages: 23
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466691223|DOI: 10.4018/IJAMC.2016070101
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MLA

Adibifard, Meisam, et al. "Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm." IJAMC vol.7, no.3 2016: pp.1-23. http://doi.org/10.4018/IJAMC.2016070101

APA

Adibifard, M., Bashiri, G., Roayaei, E., & Emad, M. A. (2016). Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 7(3), 1-23. http://doi.org/10.4018/IJAMC.2016070101

Chicago

Adibifard, Meisam, et al. "Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 7, no.3: 1-23. http://doi.org/10.4018/IJAMC.2016070101

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Abstract

Since two of the most important disadvantages of the classical nonlinear regression methods, such as Levenberg-Marquardt (LM), are to calculate error derivative function and use an initial point to get the results, PSO algorithm, which lies in the category of population based meta-heuristic algorithms, is used in this study to implement nonlinear regression in well test analysis. Root Mean Square Error (RMSE) over pressure and pressure derivative data are used in the cost function formulation and the multi-objective problem is reduced to single objective one by including the weight for each of the cost functions related to pressure and pressured derivative data. The superiority of the procedure developed in this study is verified through a simulated drawdown test and one field case. Error comparison over estimated reservoir parameters and analysis of 95% confidence interval reveal that implemented PSO algorithm can be used accurately to estimate reservoir properties.

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