IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v349y2023ics0306261923009571.html
   My bibliography  Save this article

Multi-objective optimization of WAG injection using machine learning and data-driven Proxy models

Author

Listed:
  • Bocoum, Alassane Oumar
  • Rasaei, Mohammad Reza

Abstract

In complex optimization processes such as CO2-water alternating gas (CO2-WAG), several iterative steps are needed before finding an optimal or sub-optimal set of solutions. This leads to time-consuming operations, especially when the simulation is run with a compositional simulator. Proxy models have been used to tackle this issue as they can replicate efficiently and accurately reservoir simulators in specific studies. However, the construction of such a proxy model, its basic database in particular, differs according to the designer and the objective function(s).

Suggested Citation

  • Bocoum, Alassane Oumar & Rasaei, Mohammad Reza, 2023. "Multi-objective optimization of WAG injection using machine learning and data-driven Proxy models," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009571
    DOI: 10.1016/j.apenergy.2023.121593
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923009571
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121593?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pål Østebø Andersen & Jan Inge Nygård & Aizhan Kengessova, 2022. "Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM," Energies, MDPI, vol. 15(2), pages 1-35, January.
    2. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    3. Si Le Van & Bo Hyun Chon, 2017. "Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO 2 Performance," Energies, MDPI, vol. 10(7), pages 1-20, June.
    4. Menad Nait Amar & Noureddine Zeraibi & Ashkan Jahanbani Ghahfarokhi, 2020. "Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(3), pages 613-630, June.
    Full references (including those not matched with items on IDEAS)

    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. Abdoli, B. & Hooshmand, F. & MirHassani, S.A., 2023. "A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem," Applied Energy, Elsevier, vol. 338(C).
    2. 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.
    3. Jin, Wencheng & Atkinson, Trevor A. & Doughty, Christine & Neupane, Ghanashyam & Spycher, Nicolas & McLing, Travis L. & Dobson, Patrick F. & Smith, Robert & Podgorney, Robert, 2022. "Machine-learning-assisted high-temperature reservoir thermal energy storage optimization," Renewable Energy, Elsevier, vol. 197(C), pages 384-397.
    4. Jinkai Wang & Hengyi Liu & Jinliang Zhang & Jun Xie, 2018. "Lost Gas Mechanism and Quantitative Characterization during Injection and Production of Water-Flooded Sandstone Underground Gas Storage," Energies, MDPI, vol. 11(2), pages 1-26, January.
    5. Aaditya Khanal & Md Fahim Shahriar, 2022. "Physics-Based Proxy Modeling of CO 2 Sequestration in Deep Saline Aquifers," Energies, MDPI, vol. 15(12), pages 1-23, June.
    6. Naghizadeh, Arefeh & Jafari, Saeed & Norouzi-Apourvari, Saied & Schaffie, Mahin & Hemmati-Sarapardeh, Abdolhossein, 2024. "Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field," Energy, Elsevier, vol. 293(C).
    7. Emad A. Al-Khdheeawi, 2024. "Optimizing CO 2 -Water Injection Ratio in Heterogeneous Reservoirs: Implications for CO 2 Geo-Storage," Energies, MDPI, vol. 17(3), pages 1-14, January.
    8. Wei Jia & Ting Xiao & Zhidi Wu & Zhenxue Dai & Brian McPherson, 2021. "Impact of Mineral Reactive Surface Area on Forecasting Geological Carbon Sequestration in a CO 2 -EOR Field," Energies, MDPI, vol. 14(6), pages 1-22, March.

    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:eee:appene:v:349:y:2023:i:c:s0306261923009571. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.