Well Group Connectivity Relations Discriminate Based on CART Algorithm

Abstract:

Up to now, the common method of reservoir well group that is dynamic connectivity, it mainly includes tracer testing, stress testing, well testing, and numerical simulation. The implementation of these methods is more complex, expensive, high cost, and will affect the normal production of the oilfield. Because of the convenient injection and dynamic data it can get convenient. This paper presents a method that using dynamic reservoir development data inverse well group connectivity. CART algorithm analysis and extraction of potential knowledge from the oilfield development. It establish direct mapping of logging data and well group connectivity relationship. Experiments show that using dynamic data to study well group connectivity relationship can greatly reduce the cost and as a result has a higher accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 1252-1255
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.1252
• Online since: February 2014
• Authors: Ji Sheng Liang*, Wei Zheng, Zi Bo Yuan
• Keywords: CART Algorithm, Component, Connectivity, Injection and production wells system, Oilfield

References

[1] TANG Liang, YIN Yan-ling, ZHANG Gui-cai, Study on Connectivity of An Injection-production System[J], Journal of Oil and Gas Technology, 2008, 30(4): 134-136.

[2] ZHAO Hui, LI Yang, GAO Da, Research on reservoir interwell dynamic connectivity using systematic analysis method, Acta Petrolei Sinica, 2010, 31(4): 633-636.

[3] LI Hongqi, GUO Haifeng, GUO Haimin, An approach of data mining for evaluation of complex formation using well logs, Acta Petrolei Sinica, 2009, 30(4): 542-549.

[4] Breiman L, Friedman J H, Olshen R A . Classification and Regression Tree[M]. Wadsworth: Belmont Press, (1984).

[5] SONG Guang-ling, HAO Zhong-xiao, An Improved Algorithm Based on CART Decision, Journal of Harbin University of Science and Technology, 2009, 14(2): 17-20.

[6] HAN Hui, MAO Feng, WANG Wen-yuan, Review of Recent Development in Decision Tree Algorithm in Data Mining, Application Research of Computers, 2004，(12): 5-8.

[7] TIAN Miao-miao, A summary of decision tree in data mining, Journal of Changchun University, 2004, 14(6) : 48-51.

[8] YohannesY, Hoddinott J. Classification and Regression Tree: An Introduction [M]. Washington, D. C. U. S. A.: International Food Policy Research Institute, (1999).

[9] TANG Hua song, YAO Yao wen, Research on Decision Tree in Data Mining, Application Research of Computers, 2001, 8: 18-22.