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A systematic approach for estimation of reservoir rock properties using Ant Colony Optimization | ||
| Geopersia | ||
| مقاله 2، دوره 5، شماره 1، خرداد 2015، صفحه 7-17 اصل مقاله (812.29 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.7508/GEOP.2015.01.002 | ||
| نویسنده | ||
| Ali Kadkhodaie-Ilkhchi* | ||
| Department of Earth Science, Faculty of Natural Science, University of Tabriz, Iran | ||
| چکیده | ||
| Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization (ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper, the Ant Colony Optimization is used as an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity estimation from petrophysical data by the linear and nonlinear ACO models. The results of this research show that the ACO is a fast, robust and cost-effective method for rock properties estimation. It is proposed that ant colony optimization aids in future reservoir characterization studies. | ||
| کلیدواژه‌ها | ||
| Ant colony Optimization؛ Petrophysical Data؛ Rock Properties؛ Shear wave velocity | ||
| مراجع | ||
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Blum, C., 2005. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2, 353-373. Dorigo, M., Maniezzo, V., Colorni, A., 1996. Ant System: Optimization by a colony of cooperating a gents. IEEE Trans. Syst. Man. Cybernet. Part B, 26(1): 29-41. Dorigo, M., Stützle, T., 2004. Ant Colony optimization. Cambridge, MA: MIT Press. 305p. Dorigo, M., Birattari, M., Stutzle,T., 2006. Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine, 11: 28-39. Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H., Rezaee, M.R., 2009. A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: An Example from the Kangan and Dalan Reservoirs in South Pars Gas Field, Iran. Computers & Geosciences, 35: 459-474. Kamali, M.R., Mirshady, A.A., 2004. Total organic carbon content determined from well logs using Δ log R and neurofuzzy techniques. Journal of Petroleum Science and Engineering,45: 141–148. Mohaghegh, S., 2000. Applications of virtual intelligence to petroleum engineering. Computers and Geosciences 26, 867p. Nikravesh, M., Aminzadeh, F., 2003. Soft computing and intelligent data analysis in oil exploration. Part1: Introduction: Fundamentals of Soft Computing. Elsevier, Berkeley, USA. 744 pp. Razavi., SF., Jalali-Farahani, F., 2010. Optimization and parameters estimation in petroleum engineering problems using ant colony algorithm. Journal of Petroleum Science and Engineering, 74: 147-153. Rezaee, M.R, Kadkhodaie-Ilkhchi, A. Barabadi, A., 2007. Prediction of shear wave velocity from petrophysical data using intelligent systems, a sandstone reservoir of Carnarvon Basin. Journal of Petroleum Science and Engineering, 55: 201- 212. Zerafat, M.M., Ayatollahi, S., Roosta, A.A., 2009. Genetic Algorithms and Ant Colony Approach for Gas-lift Allocation Optimization. Journal of the Japan Petroleum Institute 52 (3): 102-107. | ||
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