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Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

Author

Listed:
  • Sungil Kim

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Kyungbook Lee

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
    Department of Geoenvironmental Sciences, Kongju National University, Gongju, Chungnam 32588, Korea)

  • Minhui Lee

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
    GEOLAB Corporation, 351, Galmae-ro, Sejong 30121, Korea)

  • Taewoong Ahn

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Jaehyoung Lee

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Hwasoo Suk

    (CGO Corporation, 9, World Cup buk-ro 56-gil, Mapo-gu, Seoul 06159, Korea)

  • Fulong Ning

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
    National Center for International Research on Deep Earth Drilling and Resource Development, Wuhan 430074, China)

Abstract

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

Suggested Citation

  • Sungil Kim & Kyungbook Lee & Minhui Lee & Taewoong Ahn & Jaehyoung Lee & Hwasoo Suk & Fulong Ning, 2020. "Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images," Energies, MDPI, vol. 13(19), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5032-:d:418769
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    References listed on IDEAS

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    1. Geraldo A. R. Ramos & Lateef Akanji, 2017. "Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields," Energies, MDPI, vol. 10(7), pages 1-33, June.
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    Cited by:

    1. Kim, Sungil & Kim, Tea-Woo & Hong, Yongjun & Kim, Juhyun & Jeong, Hoonyoung, 2024. "Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning," Energy, Elsevier, vol. 290(C).
    2. Sungil Kim & Byungjoon Yoon & Jung-Tek Lim & Myungsun Kim, 2021. "Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning," Energies, MDPI, vol. 14(5), pages 1-20, March.
    3. Sungil Kim & Kyungbook Lee & Minhui Lee & Taewoong Ahn, 2020. "Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation," Energies, MDPI, vol. 13(21), pages 1-19, November.

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