LAPSE:2019.0391v1
Published Article
LAPSE:2019.0391v1
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.
Record ID
Keywords
artificial neural network, chemical flooding, enhanced oil recovery, net present value, Optimization
Subject
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:2019.0391v1
Author Affiliations
Journal Name
Energies
Volume
9
Issue
12
Article Number
E1081
Year
2016
Publication Date
2016-12-17
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en9121081, Publication Type: Journal Article
Record Map
Published Article
LAPSE:2019.0391v1
This Record
External Link
doi:10.3390/en9121081
Publisher Version
Download
Meta
Record Statistics
Record Views
707
Version History
[v1] (Original Submission)
Feb 27, 2019
Verified by curator on
Feb 27, 2019
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2019.0391v1
Original Submitter
Calvin Tsay
Links to Related Works