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Title

Artificial intelligence: a proper approach for prediction of water saturation in hydrocarbon reservoir

Year: 1390
COI: IPEC03_126
Language: EnglishView: 847
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Authors

A Hosseini - Faculty of mining and petroleum engineering, Shahrood University of Technology
A Kamkar Rouhani - Faculty of mining and petroleum engineering, Shahrood University of Technology
A Roshandel - Faculty of mining and petroleum engineering, Shahrood University of Technology
J Hanachi - Iranian Offshore Oil Company

Abstract:

Water saturation (Sw) is a significant petrophysical parameter usually used for reservoir estimation and production. This parameter is one of the mostdifficult petrophysical properties to determine and predict. The conventional methods for water saturation determination are core analysis and well testdata. These methods are, however, very expensive and time-consuming. One of the comparatively inexpensive and readily available sources ofinferring Sw is from well logs. In recent decades, artificial Intelligent (AI) has many applications in the petroleum engineering as well as other areas ofresearch. The aim of this paper is to use two diverse machine learning technology named back-propagation neural network (BPNN) and generalregression neural network (GRNN) for predicting the water saturation of four wells in Burgan reservoir, south of Iran. Comparing the obtainedresults of these two methodologies has shown that BPNN is a faster and precious method than GRNN in prediction of water saturation.

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Paper COI Code

This Paper COI Code is IPEC03_126. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/260101/

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Hosseini, A and Kamkar Rouhani, A and Roshandel, A and Hanachi, J,1390,Artificial intelligence: a proper approach for prediction of water saturation in hydrocarbon reservoir,3rd Iranian Petroleum Engineering Congress,Tehran,https://civilica.com/doc/260101

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