Machine-Learning-Assisted Seismic Facies Screening of Reservoir-Seal Architecture for CO₂ Storage in the Nong Phak Chi Field, Thailand
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Abstract
Geological carbon capture and storage (CCS) requires secure reservoir-seal systems and robust assessment of additional containment. This study screens potential secondary CO₂-containment pairs and additional reservoir candidates in the depleted Nong Phak Chi field, Suphan Buri Basin, Thailand. The workflow integrates Relative Geological Time (RGT) modelling, horizon-stack interpretation, multi-attribute seismic facies classification using an Unsupervised Vector Quantizer (UVQ) neural network, and seismic-to-well calibration across four wells. A seven-attribute dataset derived from the conditioned 3D seismic volume was partitioned into three seismic facies classes. After calibration with well-log data, the classes were interpreted as sandstone-prone, silty sandstone-prone, and shale-prone facies. The UVQ model produced a 94% average vector-matching score, which indicates internal classification consistency rather than independent lithological prediction accuracy. Horizon 24/S5 is the preferred primary reservoir candidate for CO₂ injection, and the overlying shale-rich Horizon 53 is the interpreted primary seal. Horizon 58 is a potential CO₂-accumulation interval above the primary reservoir-seal system, and Horizon 62 is a potential upper seal. Together, H58-H62 form the most plausible candidate secondary-containment pair. In contrast, Horizon 9/D6 lies below S5 and is therefore treated as a separate deeper reservoir candidate for dedicated CO₂ injection, not as a secondary trap or secondary-containment interval. The results indicate a multilayer reservoir-seal architecture in the Nong Phak Chi field. Confirmation of long-term storage performance will require petrophysical analysis, fault-seal assessment, static reservoir modelling, dynamic CO₂ flow simulation, and geochemical and geomechanical evaluation.
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Copyright © 2008 Department of Geology, Faculty of Science, Chulalongkorn University. Parts of an article can be photocopied or reproduced without prior written permission from the author(s), but due acknowledgments should be stated or cited accordingly.
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