Emissions Reduction Visual Presentation R12: Towards an improved understanding of fault systems behaviour in a CCS project
Bibliographic record
Abstract
Emissions Reduction Visual Presentation R12 A key uncertainty when investigating potential carbon capture and storage (CCS) projects is the role of faults and their interactions with the injected CO2. While it is anticipated that CCS projects will, at least in the short-term, focus on avoiding the interaction between the injected CO2 and fault systems, the likelihood of a storage projects encountering a fault system would increase, as the number of storage projects increases. While faults have been significantly studied for petroleum production, the role of faults for CCS projects is yet to be better understood: friend or foe? In the later context, analytical and desktop studies have been performed, yet limited understanding exists at pilot scale. In this work, we focus on field scale investigation at the CSIRO In-Situ Laboratory research facility (ISL), Western Australia, where an extensive fault system, the F10 fault, has been drilled through. A shallow CO2 controlled release test suggested that the fault did not significantly affect CO2 migration. More recently, two new 2D seismic lines were acquired and existing 3D seismic survey was reprocessed in 2023 and the Harvey 6 well intersecting fully the fault was drilled in March 2024. In March 2025, a vertical injection well will be drilled with the intent to inject fluid into the fault. This new data provides great insights on the fault structure and the planning of a deeper injection experiment which is discussed. To access the Visual Presentation click the link below. To read the full paper click here
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.476 | 0.124 |
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 source (direct Gemma or distilled Codex), 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".