Supplementary Files
Keywords
How to Cite
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.
References
Ramos, Geraldo A. and Akanji, L. Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields. Energies. Vol. 10 (7). (2017).
Al-Mutairi, S. M. and Kokal, S. L.; EOR potential in the Middle East: current and future trends. In: SPE EUROPEC/EAGE Annual Conference and Exhibition. Society of Petroleum Engineers. Viena, Austria. (2011).
Kamari, A.; Nikookar, M.; Sahranavard, L. and Mohammadi, A. H. Efficient screening of enhanced oil recovery methods and predictive economic analysis. Neural Comput & Applic. 25(3-4):815-24. (2014)
Mohan, H; Biglarbigi, K.; Carolus, M. J. and Van Wagener, D. Assessing the EOR Potential of the United States. In: SPE Enhanced Oil Recovery Conference. Society of Petroleum Engineers. (2011).
Al Adasani, A. and Bai, B. Analysis of EOR projects and updated screening criteria. J Pet Sci Eng. 79 (1):10-24. (2011)
Saleh, L. D.; Wei, M and Bai, B. Data analysis and updated screening criteria for polymer flooding based on oilfield data. SPE Reservoir Evaluation & Engineering. 17(01):15-25. (2014)
Taber J. J., Martin F. D. and Seright, R. S. EOR screening criteria revisited-Part 1: Introduction to screening criteria and enhanced recovery field projects. SPE Reservoir Engineering. 12(03):189-98. (1997).
Taber J. J. and Martin F. D. Seright RS. EOR screening criteria revisited—part 2: applications and impact of oil prices. SPE Reservoir Engineering. 12(03): 199-206. (1997).
Goodlett, G. O.; Honarpour, M. M. and Chung F. T. Sarathi PS. The role of screening and laboratory flow studies in EOR process evaluation. In: SPE Rocky Mountain Regional Meeting. Society of Petroleum Engineers. (1986).
Akanji, L. and Sandrea, R. A Neuro-Fuzzy Approach to Screening Reservoir Candidates for EOR. Advances in Petroleum Exploration and Development. 12(1): 1-14. (2016)
Chung, T. H; Carroll H. B. and Lindsey R. Application of fuzzy expert systems for EOR project risk analysis. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. (1995).
Dickson, J. L; Leahy-Dios, A. and Wylie P. L. Development of improved hydrocarbon recovery screening methodologies. In: SPE Improved Oil Recovery Symposium. Society of Petroleum Engineers. (2010).
Gharbi, R. B. An expert system for selecting and designing EOR processes. J Pet Sci Eng. 27(1):33-47. (2000).
Guerillot, D. R. EOR screening with an expert system. In: Petroleum [1] Computer Conference. Society of Petroleum Engineers. (1988).
Zhou, C. D.; Wu, X. L. and Cheng J. A. Determining reservoir properties in reservoir studies using a Fuzzy Neural Network. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. (1993).
Taber, J. J. Technical screening guides for the enhanced recovery of oil. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. (1983).
Nageh, M.; El Ela, M. A.; El Tayeb, E. S. and Sayyouh, H. Application of Using Fuzzy Logic as an Artificial Intelligence Technique in the Screening Criteria of the EOR Technologies. In: SPE North Africa Technical Conference and Exhibition. Society of Petroleum Engineers. (2015).
Choubineh, A.; Mousavi, S. R.; Vafaee, Ayouri M.; Ahmadinia, M.; Choubineh, D.; and Baghban, A. Estimation of the CO2-oil minimum miscibility pressure for enhanced oil recovery. Pet Sci Technol. 34(22), 184-54. (2016).
Kamari, A.; Moeini, F.; Shamsoddini-Moghadam, M. J.; Hosseini, S. A.; Mohammadi, A. H. and Hemmati-Sarapardeh, A. Modeling the permeability of heterogeneous oil reservoirs using a robust method. Geosciences Journal. 20(2):259-71. (2016).
Ramos, Geraldo and Akanji, L. Technical Screening of Enhanced Oil Recovery Methods – A Case Study of Block C in Offshore Angolan Oilfields. In: First EAGE/ASGA Petroleum Exploration Workshop. (2017).
Kottungal, L. Worldwide EOR Survey. Oil & Gas. (2014)
Arash, Kamari; Mohammad, Nikookar; Leili, Sahranavard and Amir, H. Mohammadi. Efficient screening of enhanced oil recovery methods and predictive economic analysis. Springer, pages 815–824, February DOI 10.1007/s00521-014-1553-9. (2014).
Arash, Kamari; Farzaneh, Moeini; Mohammad-Javad, Shamsoddini-Moghadam; Seyed-Ali, Hosseini; Amir, H. Mohammadi, and Abdolhossein, Hemmati-Sarapardeh. Modeling the permeability of heterogeneous oil reservoirs using a robust method. Geosciences Journal, 20(2), 259–271 (2016).