Muhammad, Khan (2025) Quantitative Interpretation, Seismic Attributes, And Machine Learning Integration For Advanced Reservoir Characterization And Geomechanics In The Browse Basin, Australia. PhD thesis, Universiti Sains Malaysia.
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Abstract
3D 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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QC Physics > QC1-999 Physics |
| Divisions: | Pusat Pengajian Sains Fizik (School of Physics) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 13 Jul 2026 04:18 |
| Last Modified: | 13 Jul 2026 04:18 |
| URI: | http://eprints.usm.my/id/eprint/64619 |
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