Comprehensive Prediction of Regional Natural Gas Hydrate Resources Based on Volume Method Evaluation

Dongxun Jiang () and Zhaocheng Li
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Dongxun Jiang: Guohao College, Tongji University, Shanghai 200092, China
Zhaocheng Li: School of Mathmatical Science, Tongji University, Shanghai 200092, China

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: As an efficient clean backup energy source, natural gas hydrates have received high attention from countries around the world, and it is very important to establish models to predict the total amount of regional resources. In response to the complexity and existing shortcomings of current methods in resource exploration and prediction, this article used the volume method evaluation as the basis for predictions. The resource and location information of obtained from 14 wells in the research area were used as data, and k-Nearest Neighbor interpolation (KNN interpolation) was used to estimate the effective area. Through the Kolmogorov–Smirnov test (KS test), we found that the parameters for natural gas hydrate resources roughly follow a Poisson distribution with coordinates. After using a three-dimensional configuration, we were able to characterize the overall distribution pattern and predict the resource quantity of natural gas hydrates in each well and the total regional resource quantity. Finally, we used the Monte Carlo algorithm and genetic algorithm based on the k-Nearest Neighbor interpolation to predict the location of the maximum possible resource quantity within the entire region. In the discussion, we discussed the possible reasons for the occurrence of negative saturation and verified the accuracy of the algorithms and analyzed the applicability of the current algorithm model in different environments.

Keywords: natural gas hydrates; volume method; KNN interpolation; Monte Carlo algorithm; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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