Permeability prediction from specific area, porosity and water saturation using extreme learning machine and decision tree techniques: A case study from carbonate reservoir

M. Sitouah, M. Salmeen, S. Oyemakinde, F. Anifowose, Osman Abdullatif

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper presents a comparative study of the capabilities of Extreme Learning Machines (ELM), Decision Trees (DT) and Artificial Neural Networks (ANN), in the prediction of permeability from specific surface area, porosity and water saturation. ANN has been applied in the prediction of various oil and gas properties but with limitations such as computational instability due to its lack of global optima. ELM and DT are recent advances in Artificial Intelligence with improved architectures and better performance. The techniques were optimized and applied to the same carbonate reservoir field dataset. Following the popular convention and to ensure fairness, a stratified sampling approach was used to randomly extract 70% of the dataset for training while the remaining 30% was used for testing. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling. The results showed that ELM performed best with the highest correlation coefficient, lowest root mean square error and shortest execution time. This agrees perfectly with the literature that ELM has a more compact architecture optimized for faster execution than the original ANN. DT was also found to be a promising technique for reservoir modeling.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - 18th Middle East Oil and Gas Show and Conference 2013, MEOS 2013
Subtitle of host publicationTransforming the Energy Future
PublisherSociety of Petroleum Engineers (SPE)
Pages212-221
Number of pages10
ISBN (Print)9781627482851
DOIs
StatePublished - 2013
Event18th Middle East Oil and Gas Show and Conference 2013: Transforming the Energy Future, MEOS 2013 - Manama, Bahrain
Duration: 10 Mar 201313 Mar 2013

Publication series

NameSPE Middle East Oil and Gas Show and Conference, MEOS, Proceedings
Volume1

Conference

Conference18th Middle East Oil and Gas Show and Conference 2013: Transforming the Energy Future, MEOS 2013
Country/TerritoryBahrain
CityManama
Period10/03/1313/03/13

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