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Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO 2 Performance

Author

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  • 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

The injection of CO 2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance. This study proposed the comprehensive evaluations of a water-alternating-CO 2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO 2 production, and net CO 2 storage were accurately reflected by integrating the generated network models. More importantly, since the networks could simulate a series of injection processes, the sequent variations of those technical issues were well presented, indicating the distinct application of ANN in this study compared to previous works. The economic terms were also briefly introduced for a given fiscal condition which included sufficient concerns for a general CO 2 flooding project, in a range of possible oil prices. Using the ANN models, the net present value (NPV) optimization results for several specific cases apparently expressed the profitability of the present enhanced oil recovery (EOR) project according to the unstable oil prices, and most importantly provided the most relevant injection schedules corresponding with each different scenario. Obviously, the methodology of applying traditional ANN as shown in this study can be adaptively adjusted for any other EOR project, and in particular, since the models have demonstrated their flexible capacity for economic analyses, the method can be promisingly developed to engage with other economic tools on comprehensively assessing the project.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:842-:d:102476
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    References listed on IDEAS

    as
    1. Ren, Bo & Ren, Shaoran & Zhang, Liang & Chen, Guoli & Zhang, Hua, 2016. "Monitoring on CO2 migration in a tight oil reservoir during CCS-EOR in Jilin Oilfield China," Energy, Elsevier, vol. 98(C), pages 108-121.
    2. Mohammad Ali Ahmadi & Alireza Ahmadi, 2016. "Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 11(3), pages 325-332.
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    Cited by:

    1. 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.
    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. 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.
    4. 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).

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