Volume 4, Issue 2, October 2013, Pages 248–253
Penghui Zhang1, Jinliang Zhang2, Ming Li3, Mingming Tang4, and Jingzhe Li5
1 College of Resources Science & Technology, Beijing Normal University, Beijing, China
2 College of Resources Science & Technology, Beijing Normal University, Beijing, China
3 Henan Oilfield Research Institute, Zhengzhou, Henan, China
4 College of Resources Science & Technology, Beijing Normal University, Beijing, China
5 College of Resources Science & Technology, Beijing Normal University, Beijing, China
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article describes an approach based on artificial neural network to identify waterflooded zone of continental clastic reservoirs. For the logging sequence of waterflooded zone matching the characteristics of the continental oilfield, the application of artificial neural network algorithm is able to distinguish water layers, oil reservoirs and dry layers among reservoirs of waterflooded zones. The output vectors of the network represent the fluid types. Thus, better results are supposed to be obtained than traditional methods in the crossplot plate after network training. Distribution becoming non-uniform and contact between grains being loose were found after microscopic observation in the waterflooded zones. It has revealed that the waterflooded characteristics are of great significance, and it has also proved the accuracy of identification from another perspective.
Author Keywords: Waterflooding, Continental reservoirs, Neural network, Identification, Fluid.
Penghui Zhang1, Jinliang Zhang2, Ming Li3, Mingming Tang4, and Jingzhe Li5
1 College of Resources Science & Technology, Beijing Normal University, Beijing, China
2 College of Resources Science & Technology, Beijing Normal University, Beijing, China
3 Henan Oilfield Research Institute, Zhengzhou, Henan, China
4 College of Resources Science & Technology, Beijing Normal University, Beijing, China
5 College of Resources Science & Technology, Beijing Normal University, Beijing, China
Original language: English
Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This article describes an approach based on artificial neural network to identify waterflooded zone of continental clastic reservoirs. For the logging sequence of waterflooded zone matching the characteristics of the continental oilfield, the application of artificial neural network algorithm is able to distinguish water layers, oil reservoirs and dry layers among reservoirs of waterflooded zones. The output vectors of the network represent the fluid types. Thus, better results are supposed to be obtained than traditional methods in the crossplot plate after network training. Distribution becoming non-uniform and contact between grains being loose were found after microscopic observation in the waterflooded zones. It has revealed that the waterflooded characteristics are of great significance, and it has also proved the accuracy of identification from another perspective.
Author Keywords: Waterflooding, Continental reservoirs, Neural network, Identification, Fluid.
How to Cite this Article
Penghui Zhang, Jinliang Zhang, Ming Li, Mingming Tang, and Jingzhe Li, “Waterflooding identification of continental clastic reservoirs based on neural network,” International Journal of Innovation and Applied Studies, vol. 4, no. 2, pp. 248–253, October 2013.
