Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir

Emad A. El-Sebakhy*, Ognian Asparouhov, Abdul Azeez Abdulraheem, Abdul Aziz Al-Majed, Donghui Wu, Kris Latinski, Iputu Raharja

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Permeability prediction has been a challenge to reservoir engineers due to the lack of tools that measure it directly. The most reliable data of permeability obtained from laboratory measurements on cores do not provide a continuous profile along the depth of the formation. Recently, researchers utilized statistical regression, neural networks, and fuzzy logic to estimate both permeability and porosity from well logs. Unfortunately, due to both uncertainty and imprecision, the developed predictive modelings are less accurate compared to laboratory experimental core data. This paper presents functional networks as a novel approach to forecast permeability using well logs in a carbonate reservoir. The new intelligence paradigm helps to overcome the most common limitations of the existing modeling techniques in statistics, data mining, machine learning, and artificial intelligence communities. To demonstrate the usefulness of the functional networks modeling strategy, we briefly describe its learning algorithm through simple distinct examples. Comparative studies were carried out using real-life industry wireline logs to compare the performance of the new framework with the most popular modeling schemes, such as linear/nonlinear regression, neural networks, and fuzzy logic inference systems. The results show that the performance of functional networks (separable and generalized associativity) architecture with polynomial basis is accurate, reliable, and outperforms most of the existing predictive data mining modeling approaches. Future work can be achieved using different structure of functional networks with different basis, interaction terms, ensemble and hybrid strategies, different clustering, and outlier identification techniques within different oil and gas challenge problems, namely, 3D passive seismic, identification of lithofacies types, history matching, rock mechanics, viscosity, risk assessment, and reservoir characterization.

Original languageEnglish
Pages (from-to)10359-10375
Number of pages17
JournalExpert Systems with Applications
Volume39
Issue number12
DOIs
StatePublished - 15 Sep 2012

Keywords

  • Carbonate reservoir
  • Data mining
  • Feedforward neural networks
  • Functional networks
  • Fuzzy logic
  • Minimum description length
  • Permeability
  • Porosity
  • Statistical regression

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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