Bappa Mukherjee, Kalachand Sain, Rahul Ghosh, Suman Konar
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引用次数: 0
Abstract
Empirical methods often fail to accurately depict in-situ gas hydrate saturation distributions, despite their relationships with petrophysical and elastic properties remaining partially unclear. We proposed a data-driven approach to estimate gas hydrate saturation employing several machine learning techniques, including radial basis function neural network (RBFNN), random forest (RF), extreme gradient boosting (XGBoost), Adaptive Boosting (AdaBoost), support vector machines (SVM), and k-nearest neighbors (kNN). This study involved pre-processing data from laterolog deep resistivity and p-wave velocity logs, defining their increments as differences from the lowest values in gas hydrate zones. We identified data-driven patterns between pairs of laterolog deep resistivity and p-wave velocity increments, as well as core information corroborated with the traditionally predicted gas hydrate saturations, by adopting machine learning (ML) approaches. The approach tested on four wells in the Krishna-Godavari (KG) offshore basin (India) is extremely feasible. During the training and test phases, the minimum correlation coefficient between the true and predicted responses exceeds 0.94 and 0.88, respectively. The model accuracy hierarchy was RBFNN > AdaBoost > RF > XGBoost > KNN > SVM during training, and AdaBoost > XGBoost > RF > RBFNN > KNN > SVM during testing. This approach allows interpreters to select the most accurate ML model based on training phase performance. The proposed ML-based method is efficient, synergising p-wave and resistivity data increment, significantly improving gas hydrate saturation predictions, and avoiding the complexities of traditional calculations. The study indicates that gas hydrate saturation in the Krishna-Godavari region ranges from 0.17 to 86.84%.
期刊介绍:
Well-established international journal presenting marine geophysical experiments on the geology of continental margins, deep ocean basins and the global mid-ocean ridge system. The journal publishes the state-of-the-art in marine geophysical research including innovative geophysical data analysis, new deep sea floor imaging techniques and tools for measuring rock and sediment properties.
Marine Geophysical Research reaches a large and growing community of readers worldwide. Rooted on early international interests in researching the global mid-ocean ridge system, its focus has expanded to include studies of continental margin tectonics, sediment deposition processes and resulting geohazards as well as their structure and stratigraphic record. The editors of MGR predict a rising rate of advances and development in this sphere in coming years, reflecting the diversity and complexity of marine geological processes.