NDLI: Application of neural network technique for logging fluid identification in low resistance reservoir

Content Provider	IEEE Xplore Digital Library
Author	Li Ming Jinliang Zhang
Copyright Year	2010
Description	Author affiliation: College of resources science & technology, Beijing Normal University, China (Jinliang Zhang) \|\| College of Marine Geo-science, Ocean University of China, OUC, Qingdao, China (Li Ming)
Abstract	In recent years, artificial-neural-network (ANN) technology has been applied successfully to many petroleum engineering problems, including reservoir logging fluid identification. In this paper, we present the application of ANN technology to judge the type of fluid of reservoir sandstones. We demonstrate this with an ANN model that uses the well logs associated with known fluid type from well test conclusion as input and produces predictions of water/(oil + water) ratio, a key reservoir fluid property used in oilfield to evaluate the type of reservoir fluid. We set the output vector as x and y, so that the train sample with fluid type can be reflected to a two-dimensional crossplot and create four point of intersections represent oil, oil & water, water and dry layer respectively. With this trained crossplot, inputting well logs of the layer to be identified, using Euclidean distance to calculate the distance between the result and the four fluid type crossing points and find the shortest one, we can obtain the fluid type of this layer. The result of this research indicates that this method is quite effective and gets satisfying prediction precision for the low resistance reservoir logging fluid identification.
Starting Page	163
Ending Page	166
File Size	1771870
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583311
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fluids BP neural network Estimation Artificial neural networks Reservoirs Data models logging fluid identification low resistance reservoir Petroleum Biological neural networks
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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