IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i12p1081-d85504.html
   My bibliography  Save this article

Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

Author

Listed:
  • Si Le Van

    (Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea)

  • Bo Hyun Chon

    (Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea)

Abstract

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.

Suggested Citation

  • Si Le Van & Bo Hyun Chon, 2016. "Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application," Energies, MDPI, vol. 9(12), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1081-:d:85504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/12/1081/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/12/1081/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olajire, Abass A., 2014. "Review of ASP EOR (alkaline surfactant polymer enhanced oil recovery) technology in the petroleum industry: Prospects and challenges," Energy, Elsevier, vol. 77(C), pages 963-982.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emad A. Al-Khdheeawi & Doaa Saleh Mahdi, 2019. "Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network," Energies, MDPI, vol. 12(16), pages 1-10, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huoxin Luan & Zhaohui Zhou & Chongjun Xu & Lei Bai & Xiaoguang Wang & Lu Han & Qun Zhang & Gen Li, 2022. "Study on the Synergistic Effects between Petroleum Sulfonate and a Nonionic–Anionic Surfactant for Enhanced Oil Recovery," Energies, MDPI, vol. 15(3), pages 1-12, February.
    2. Hong He & Jingyu Fu & Baofeng Hou & Fuqing Yuan & Lanlei Guo & Zongyang Li & Qing You, 2018. "Investigation of Injection Strategy of Branched-Preformed Particle Gel/Polymer/Surfactant for Enhanced Oil Recovery after Polymer Flooding in Heterogeneous Reservoirs," Energies, MDPI, vol. 11(8), pages 1-17, July.
    3. Huiying Zhong & Weidong Zhang & Jing Fu & Jun Lu & Hongjun Yin, 2017. "The Performance of Polymer Flooding in Heterogeneous Type II Reservoirs—An Experimental and Field Investigation," Energies, MDPI, vol. 10(4), pages 1-19, April.
    4. Xiuyu Wang & Fuqiong Wang & Mohanad A. M. Taleb & Zhiyuan Wen & Xiulin Chen, 2022. "A Review of the Seepage Mechanisms of Heavy Oil Emulsions during Chemical Flooding," Energies, MDPI, vol. 15(22), pages 1-19, November.
    5. Park, Hyemin & Han, Jinju & Sung, Wonmo, 2015. "Effect of polymer concentration on the polymer adsorption-induced permeability reduction in low permeability reservoirs," Energy, Elsevier, vol. 84(C), pages 666-671.
    6. Yang, Renfeng & Jiang, Ruizhong & Guo, Sheng & Chen, Han & Tang, Shasha & Duan, Rui, 2021. "Analytical study on the Critical Water Cut for Water Plugging: Water cut increasing control and production enhancement," Energy, Elsevier, vol. 214(C).
    7. Chang, Yuanhao & Xiao, Senbo & Ma, Rui & Zhang, Zhiliang & He, Jianying, 2022. "Atomistic insight into oil displacement on rough surface by Janus nanoparticles," Energy, Elsevier, vol. 245(C).
    8. Liu, Yu-Long & Li, Yang & Si, Yin-Fang & Fu, Jian & Dong, Hao & Sun, Shan-Shan & Zhang, Fan & She, Yue-Hui & Zhang, Zhi-Quan, 2023. "Synthesis of nanosilver particles mediated by microbial surfactants and its enhancement of crude oil recovery," Energy, Elsevier, vol. 272(C).
    9. Aghil Moslemizadeh & Hossein Khayati & Mohammad Madani & Mehdi Ghasemi & Khalil Shahbazi & Sohrab Zendehboudi & Azza Hashim Abbas, 2021. "A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems," Energies, MDPI, vol. 14(24), pages 1-21, December.
    10. Bin Huang & Wei Zhang & Rui Xu & Zhenzhong Shi & Cheng Fu & Ying Wang & Kaoping Song, 2017. "A Study on the Matching Relationship of Polymer Molecular Weight and Reservoir Permeability in ASP Flooding for Duanxi Reservoirs in Daqing Oil Field," Energies, MDPI, vol. 10(7), pages 1-10, July.
    11. Yi Zhang & Jiexiang Wang & Peng Jia & Xiao Liu & Xuxu Zhang & Chang Liu & Xiangwei Bai, 2020. "Viscosity Loss and Hydraulic Pressure Drop on Multilayer Separate Polymer Injection in Concentric Dual-Tubing," Energies, MDPI, vol. 13(7), pages 1-20, April.
    12. Baghernezhad, Danial & Siavashi, Majid & Nakhaee, Ali, 2019. "Optimal scenario design of steam-assisted gravity drainage to enhance oil recovery with temperature and rate control," Energy, Elsevier, vol. 166(C), pages 610-623.
    13. Abdelaziz L. Khlaifat & Duaa Dakhlallah & Faraz Sufyan, 2022. "A Critical Review of Alkaline Flooding: Mechanism, Hybrid Flooding Methods, Laboratory Work, Pilot Projects, and Field Applications," Energies, MDPI, vol. 15(10), pages 1-14, May.
    14. Siavashi, Javad & Mahdaviara, Mehdi & Shojaei, Mohammad Javad & Sharifi, Mohammad & Blunt, Martin J., 2024. "Segmentation of two-phase flow X-ray tomography images to determine contact angle using deep autoencoders," Energy, Elsevier, vol. 288(C).
    15. Wang, Zhenjun & Xu, Yuanming, 2015. "Review on application of the recent new high-power ultrasonic transducers in enhanced oil recovery field in China," Energy, Elsevier, vol. 89(C), pages 259-267.
    16. Druetta, P. & Raffa, P. & Picchioni, F., 2019. "Chemical enhanced oil recovery and the role of chemical product design," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Omid Mosalman Haghighi & Ghasem Zargar & Abbas Khaksar Manshad & Muhammad Ali & Mohammad Ali Takassi & Jagar A. Ali & Alireza Keshavarz, 2020. "Effect of Environment-Friendly Non-Ionic Surfactant on Interfacial Tension Reduction and Wettability Alteration; Implications for Enhanced Oil Recovery," Energies, MDPI, vol. 13(15), pages 1-18, August.
    18. Azza Hashim Abbas & Obinna Markraphael Ajunwa & Birzhan Mazhit & Dmitriy A. Martyushev & Kamel Fahmi Bou-Hamdan & Ramzi A. Abd Alsaheb, 2022. "Evaluation of OKRA ( Abelmoschus esculentus ) Macromolecular Solution for Enhanced Oil Recovery in Kazakhstan Carbonate Reservoir," Energies, MDPI, vol. 15(18), pages 1-13, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1081-:d:85504. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.