Publication:
Quantitative Interpretation, Seismic Attributes, And Machine Learning Integration For Advanced Reservoir Characterization And Geomechanics In The Browse Basin, Australia

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dc.contributor.authorMuhammad, Khan
dc.date.accessioned2026-07-13T04:13:10Z
dc.date.available2026-07-13T04:13:10Z
dc.date.issued2025-08
dc.description.abstract3D seismic data provide detailed subsurface insights essential for hydrocarbon exploration, production, geothermal development, and CO₂ storage assessment. Conventional seismic interpretation methods often underperform in structurally and depositional complex environments. This study presents a comprehensive, data-driven workflow integrating advanced seismic interpretation, machine learning, and geomechanical modeling to enhance reservoir characterization in the Poseidon 3D area, Browse Basin, Northwestern Australia. This study addresses key challenges such as mapping complex fault systems resulting from multiple tectonic events, identifying high-quality gas reservoirs within the fluvial–deltaic Jurassic Plover Formation, and delineating overpressure zones in the Jamieson Formation.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24676
dc.language.isoen
dc.subjectQuantitative Interpretation
dc.subjectSeismic Attributes
dc.subjectAnd Machine Learning Integration
dc.subjectAdvanced Reservoir Characterization And Geomechanics
dc.subjectBrowse Basin
dc.subjectAustralia
dc.titleQuantitative Interpretation, Seismic Attributes, And Machine Learning Integration For Advanced Reservoir Characterization And Geomechanics In The Browse Basin, Australia
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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