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Screening reservoir candidates for enhanced oil recovery (EOR) in Angolan offshore projects.

Ramos, Geraldo A.R.; Elias, Bruno; Yates, Kyari

Authors

Geraldo A.R. Ramos

Bruno Elias



Abstract

The neuro-fuzzy (NF) approach presented in this work is based on five (5) layered feedforward backpropagation algorithm applied for technical screening of enhanced oil recovery (EOR) methods. Associated reservoir rock-fluid oilfield data from successful EOR projects were used as input and predicted output in the training and validation processes, respectively. The developed model was then tested by using data set from Block B of an Angolan oilfield. The results of the sensitivity analysis between the Mamdani and the Takagi-Sugeno-Kang (TSK) approach incorporated in the algorithm has shown the robustness of the TSK ANFIS (Adaptive Neuro-Fuzzy Inference System) approach in comparison to the other approach for the prediction of a suitable EOR technique. The simulation test results showed that the model presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block B, Angola) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques for EOR.

Citation

RAMOS, G.A.R., ELIAS, B. and YATES, K. 2020. Screening reservoir candidates for enhanced oil recovery (EOR) in Angolan offshore projects. Angolan minerals, oil and gas journal [online], 1(1), pages 6-10. Available from: https://doi.org/10.47444/amogj.v1i1.3

Journal Article Type Article
Acceptance Date May 6, 2020
Online Publication Date May 6, 2020
Publication Date May 10, 2020
Deposit Date May 26, 2022
Publicly Available Date May 26, 2022
Journal Angolan mineral, oil and gas journal
Electronic ISSN 2708-2989
Publisher AMOGJ
Peer Reviewed Peer Reviewed
Volume 1
Issue 1
Pages 6-10
DOI https://doi.org/10.47444/amogj.v1i1.3
Keywords Artificial intelligence (AI); Enhanced oil recovery (EOR); Neural network (NN); Neuro-fuzzy (NF); Reservoir screening (RS); Adaptive neuro-fuzzy inference system (ANFIS)
Public URL https://rgu-repository.worktribe.com/output/1655251

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