Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs

Tarek Helmy*, Anifowose Fatai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

The hybridization of two or more Computational Intelligence (CI) techniques to build a single model has increased in popularity over the recent years. Such models that combine the best properties of different Artificial Intelligence (AI) techniques in a single package are very much required in the process of reservoir characterization in petroleum engineering, where a high degree of prediction accuracy is essential for efficient exploration, and management of oil and gas resources. In this paper, we have successfully predicted, with higher accuracy, the porosity and permeability of oil and gas reservoirs through the hybridization of Type-2 Fuzzy Logic (FL), Support Vector Machines (SVM) and Functional Networks (FN), using several real-life well log data. While utilizing the capabilities of data mining and CI, two hybrid models (FFS and FSF) were built. In both models, FN, using its functional approximation capability with least-square fitting algorithm, was used to select the best of the predictor variables from the input data. In the FFS model, the selected predictor variables were passed to Type-2 FL to remove uncertainties in the data (if any), and then to SVM for training and making final predictions. In the FSF model, the best predictor variables from FN were passed to SVM to transform them to a feature space, and then passed to Type-2 FL to remove uncertainties (if any), extract inference rules and make final predictions. The results showed that the hybrid models, with their higher correlation coefficients, performed better than the individual techniques when used separately with the same datasets. An extended study, used as a benchmark, showed that the hybrid models also performed better than a hybridization of only two of the techniques viz. Type-2 FL and SVM, both in terms of higher correlation coefficients and lower execution times. This was attributed to the role of FN in selecting the best variables and reducing the dimensionality of input data in the FFS and FSF models.

Original languageEnglish
Pages (from-to)313-337
Number of pages25
JournalInternational Journal of Computational Intelligence and Applications
Volume9
Issue number4
DOIs
StatePublished - Dec 2010

Bibliographical note

Funding Information:
The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work under Project No. 08-OIL82-4 as part of the National Science, Technology and Innovation Plan. We would like also to thank King Fahd University of Petroleum & Minerals for providing the computing facilities. Special thanks go to Mr. David Birkett for his help in proof-reading of the paper. Mr. Fatai is also grateful to Dr. El-Sebakhy for the initial knowledge he gained from him about the computational techniques during the AI course. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the paper.

Keywords

  • FL
  • Hybrid computational intelligence
  • functional networks
  • reservoir characterization
  • support vector machine

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications

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