CO2 Storage Characterization Driven by Images of a Prior Injection: CO2CRC's Otway Project
| dc.contributor.author | Aldakheel, Mohammed | |
| dc.contributor.supervisor | Stanislav Glubokovskikh | en_US |
| dc.contributor.supervisor | Sinem Yavuz | en_US |
| dc.date.accessioned | 2021-08-03T05:41:26Z | |
| dc.date.available | 2021-08-03T05:41:26Z | |
| dc.date.issued | 2020 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/84951 | |
| dc.description.abstract |
To characterise geological features that control the fluid flow in the subsurface from seismic data, I develop a multi-attribute analysis using an artificial neural network. The network is trained on the plume of CO2 injected into a saline aquifer as part of the CO2CRC Otway Project, using the plume’s time-lapse seismic image as ground truth. The results highlight geological features controlling CO2 flow and guide static and dynamic modelling for future injection. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | CO2 Storage Characterization Driven by Images of a Prior Injection: CO2CRC's Otway Project | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | MPhil | en_US |
| curtin.department | WASM: Minerals, Energy and Chemical Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Aldakheel, Mohammed [0000-0002-7182-0044] | en_US |