Abstract
| Original language | English |
|---|---|
| Title of host publication | First EAGE/ASGA Workshop on Petroleum Exploration: Challenges and Solutions for Deep Water Exploration in Angola |
| Publisher | EAGE |
| Pages | 1 - 14 |
| Number of pages | 14 |
| DOIs | |
| Publication status | Published - 2 Oct 2017 |
| Event | First EAGE/ASGA Workshop on Petroleum Exploration: Challenges and Solutions for Deep Water Exploration in Angola - Total Guest House, Luanda, Angola Duration: 2 Oct 2017 → 4 Oct 2017 https://events.eage.org/en/2017/first-eageasga-petroleum-exploration-workshop/technical-programme/proceedings |
Workshop
| Workshop | First EAGE/ASGA Workshop on Petroleum Exploration |
|---|---|
| Country | Angola |
| City | Luanda |
| Period | 2/10/17 → 4/10/17 |
| Internet address |
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Technical Screening of Enhanced Oil Recovery Methods - A Case Study of Block C in Offshore Angolan Oilfields. / Ramos, Geraldo Andre Raposo; Akanji, Lateef.
First EAGE/ASGA Workshop on Petroleum Exploration: Challenges and Solutions for Deep Water Exploration in Angola. EAGE, 2017. p. 1 - 14.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Technical Screening of Enhanced Oil Recovery Methods - A Case Study of Block C in Offshore Angolan Oilfields
AU - Ramos, Geraldo Andre Raposo
AU - Akanji, Lateef
PY - 2017/10/2
Y1 - 2017/10/2
N2 - This paper presents a technical screening of enhanced oil recovery (EOR) methods by using an Artificial Intelligence (AI) model based on neuro-fuzzy (NF) algorithm. The presented NF approach will enable the user to select a suitable EOR method based on available worldwide successful EOR data and field under investigation. The NF approach presented in this study is a five layered feedforward-backpropagation neural networks where the knowledge pattern is extracted by combining both the searching potential of fuzzy-logic and the learning capability of neural network to make a priori decision. The extracted knowledge from the NF system can be expressed in the form of fuzzy rules by computing weights, number of rules and fuzzy set parameters and validated against reservoir properties data trained from worldwide successful EOR projects. The successfully trained and validated model is then tested on the Angolan oilfield data (Block C) where EOR application is yet to be fully established. The test results show that the NF presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block C) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques.
AB - This paper presents a technical screening of enhanced oil recovery (EOR) methods by using an Artificial Intelligence (AI) model based on neuro-fuzzy (NF) algorithm. The presented NF approach will enable the user to select a suitable EOR method based on available worldwide successful EOR data and field under investigation. The NF approach presented in this study is a five layered feedforward-backpropagation neural networks where the knowledge pattern is extracted by combining both the searching potential of fuzzy-logic and the learning capability of neural network to make a priori decision. The extracted knowledge from the NF system can be expressed in the form of fuzzy rules by computing weights, number of rules and fuzzy set parameters and validated against reservoir properties data trained from worldwide successful EOR projects. The successfully trained and validated model is then tested on the Angolan oilfield data (Block C) where EOR application is yet to be fully established. The test results show that the NF presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block C) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques.
U2 - 10.3997/2214-4609.201702357
DO - 10.3997/2214-4609.201702357
M3 - Conference contribution
SP - 1
EP - 14
BT - First EAGE/ASGA Workshop on Petroleum Exploration: Challenges and Solutions for Deep Water Exploration in Angola
PB - EAGE
ER -