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

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

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