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

Sungil Kim, Kyungbook Lee, Minhui Lee, Taewoong Ahn, Jaehyoung Lee, Hwasoo Suk and Fulong Ning
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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
Minhui Lee: Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, 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

Energies, 2020, vol. 13, issue 19, 1-20

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.

Keywords: X-ray CT image; gas hydrate sand sample; saturation modeling; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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