Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

Geraldo A R Ramos, Lateef Akanji

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
23 Downloads (Pure)


In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.
Original languageEnglish
Article number837
Issue number7
Publication statusPublished - 22 Jun 2017

Bibliographical note

This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscript


  • enhanced oil recovery (EOR)
  • neuro-fuzzy (NF)
  • artificial intelligence (AI)
  • reservoir screening
  • neural network (NN)


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