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Practical CO 2 —WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

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  • Qian Sun

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
    School of Energy Resources, China University of Geoscience, Beijing 100083, China)

  • William Ampomah

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA)

  • Junyu You

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
    School of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China)

  • Martha Cather

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA)

  • Robert Balch

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA)

Abstract

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO 2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO 2 -WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO 2 enhanced oil recovery (EOR) projects.

Suggested Citation

  • Qian Sun & William Ampomah & Junyu You & Martha Cather & Robert Balch, 2021. "Practical CO 2 —WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches," Energies, MDPI, vol. 14(4), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1055-:d:501004
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    References listed on IDEAS

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    1. Ampomah, W. & Balch, R.S. & Cather, M. & Will, R. & Gunda, D. & Dai, Z. & Soltanian, M.R., 2017. "Optimum design of CO2 storage and oil recovery under geological uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 80-92.
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