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摘要
With the development of big data processing techniques, artificial intelligence algorithm is emerging to automatically dig out the features and hidden information from large datasets, which performs with high accuracy and reliability. Machine learning is one of the core algorithms of artificial intelligence, which has been applied to a variety of fields, including geoscience research. The thickness of gas hydrate stability zone is one of the key parameters to assess the resource potential of gas hydrate, and its accuracy directly affects the process and result of gas hydrate exploration. Previous studies to calculate the thickness of gas hydrate stability zone, usually utilize simple gas hydrate phase equations, which ignore many effects, such as gas component, thermal conductivity, etc., so that the results contain large bias. This paper predicted the phase condition of gas hydrate using machine learning, based on the experimental hydrate formation data of different gas components and seawater salinities. Then combining with heat flow, water depth and thermal conductivity of northern South China Sea, the thickness of gas hydrate stability zone was obtained by machine learning model. The predicted result is highly consistent with the experimental data and the coefficient of determination is up to 0.997. The computed thickness of gas hydrate stability agrees well with drilling data and seismic profiles. This paper provides an applied example for machine learning to calculate the thickness of gas hydrate in the northern South China Sea, which indicates that artificial intelligence has great application prospect in the potential evaluation of gas hydrate resource in the feature.
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