Site Characterisation for Carbon Sequestration in the Nearshore Gippsland Basin
Bibliographic record
Abstract
During its assessment of Carbon Storage sites in the nearshore Gippsland Basin, within 25 km of the coastline, the CarbonNet project screened >20 potential sites, comparing storage Capacity (total CO2 volume), Injectivity (CO2 injection rate), and Containment (security). Several play fairways, and a wide range of trap types were compared. Progressively more-detailed geological models were built to enable site- and scenario-specific injection modelling, and for studies of the evolution of the injected CO2 plume using state-of-the-art petroleum industry software. After screening, three key sites were prioritised, each with a secure storage capacity of >25 Mt CO2, and containment over 1000 years of dynamic modelling. The geological context, storage concept, and specific reservoir and seal elements of these three sites will be described and compared, in the context of Australian legislation which requires a demonstration of the “fundamental suitability determinants for CO2 storage” leading to a Declaration of Storage Formation. Different trap types impact the effort required, and the key issue is the (statistical) area covered by any supercritical or dissolved CO2 plume - which depends on injection pressure, reservoir property distribution, and trap geometry. The main interactions with nearby resource owners are coupled by reservoir pressure. Depending on the location of each site and the trap concept, different approaches are required, but these all require a detailed and functional geological model of not just the site, but the context within which it sits. Given this model, a detailed dynamic reservoir model can be built, checked for quality, and used for a wide range of purposes including plume extent, pressure influence, storage security, and development of a site monitoring plan including the best locations and technologies for surface and subsurface monitoring. A number of preconceptions for the basin were addressed that directly affect seal integrity (both the regional petroleum caprock, and additional intraformational seals). In order to understand the capacity and effectiveness of these seals, updated petroleum migration models were required that explain the distribution of primary hydrocarbons, and those modified by water washing, biodegradation, etc. Significant advance has been made in understanding the basin paleogeography and palaeobathymetry during seal deposition, and mapping seal facies and seal perturbations in extensive detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".