A Neuro-Fuzzy Approach to Screening Reservoir Candidates for EOR

Lateef Akanji, Rafael Sandrea

Research output: Contribution to journalArticle

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Abstract

The challenge of discovering new reserves coupled with the current dwindling oil price has necessitated the need to generate and sustain long-term production from existing fields through improved or enhanced oil recovery (IOR/EOR) processes. There is however, no established mechanism to match the thousands of candidate reservoirs worldwide to the subtle and critical variations in reservoir properties that control the success of the many different EOR options. We present a neuro-fuzzy approach to screening potential hydrocarbon reservoirs for enhanced oil recovery (EOR) applications.First, reservoir field data from multiple successful thermal, miscible, chemical and biological EOR projects across different petroleum systems worldwide were trained to establish knowledge pattern and represent it using fuzzy rules. This is achieved by combining fuzzy technique with neural network learning capability to deduce knowledge from the EOR data in a form akin to linguistic rules. Then, the extracted knowledge pattern was validated and used to determine the combination of reservoir properties which could best characterise the key heterogeneities that control EOR success. The model output can be used in screening potential reservoir for EOR application.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalAdvances in Petroleum Exploration and Development
Volume12
Issue number1
Early online date28 Sep 2016
DOIs
Publication statusPublished - 30 Sep 2016

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Screening
Recovery
Oils
Fuzzy rules
Linguistics
Crude oil
Hydrocarbons
Neural networks

Keywords

  • Neuro-fuzzy
  • EOR
  • reservoir screening
  • artificial intelligence
  • training and learning

Cite this

A Neuro-Fuzzy Approach to Screening Reservoir Candidates for EOR. / Akanji, Lateef; Sandrea, Rafael.

In: Advances in Petroleum Exploration and Development, Vol. 12, No. 1, 30.09.2016, p. 1-14.

Research output: Contribution to journalArticle

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