Integrated Rock Physics, Seismic, and Machine Learning Methodology for Regional 3D Lithofacies Modelling-A Case Study from the Jeju Basin, Offshore Korea

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ID: 331576
2026
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Abstract
Summary While well-log data provide direct measurements of subsurface properties essential for building lithofacies models, their sparse distribution and limited availability in exploration areas introduce significant uncertainty. This study presents an approach that integrates seismic data, rock physics, machine learning, and geostatistics to construct a regional-scale three-dimensional (3D) lithofacies model in the Jeju Basin, located offshore the southern Korean Peninsula. The proposed workflow comprises three integrated steps: performing Simultaneous Pre-Stack inversion on processed seismic data to derive rock properties, training neural networks for lithofacies prediction, and expanding the two-dimensional (2D) predictions into a 3D lithofacies model using Sequential Indicator Simulation. The prediction networks reproduced the interpretation-derived lithofacies labels with reasonable accuracy for the primary sedimentary classes, as demonstrated by quantitative evaluation metrics. The resulting 3D lithofacies model captured key geological features indicated by 2D seismic data, including faults and sedimentary structures, exhibiting improved spatial continuity and detail compared to conventional well-log-only geostatistical simulation. Comparison with a borehole outside the training volume demonstrated reasonable agreement between the predicted and well-log-derived lithofacies. This observation preliminarily implies the workflow’s applicability to field data beyond the 3D seismic coverage, though this assessment is based on a single well location. This integrated approach offers a cost-effective solution for lithofacies characterisation in frontier exploration sites where regional 3D seismic surveys are economically unfeasible and well control is limited.
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Authors Saichao Han, Yongchae Cho, Min Je Lee, Jaeho Jun, Seik Paik, Jun-Woo Lee
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag411
URL
https://doi.org/10.1093/gji/ggag411
Keywords Keywords not found

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