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LAPSE:2019.0391
Published Article
LAPSE:2019.0391
Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application
February 27, 2019
Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV) of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter five-spot scale. Basically, the ANN model generated by this work can be flexibly applied in different economic conditions and extended to a larger reservoir scale for similar chemical flooding projects that demand a quick prediction rather than a simulation process.
Keywords
artificial neural network, chemical flooding, enhanced oil recovery, net present value, Optimization
Suggested Citation
Le Van S, Chon BH. Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application. (2019). LAPSE:2019.0391
Author Affiliations
Le Van S: Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea [ORCID]
Chon BH: Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea [ORCID]
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E1081
Year
2016
Publication Date
2016-12-17
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9121081, Publication Type: Journal Article
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LAPSE:2019.0391
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doi:10.3390/en9121081
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Feb 27, 2019
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CC BY 4.0
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Feb 27, 2019
 
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Calvin Tsay
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