Marine measurement, monitoring and verification for offshore carbon storage projects – learnings from a coastal Gippsland setting
Bibliographic record
Abstract
Designing cost-effective methods for implementing measurement, monitoring and verification (MM&V) plans for subsea CO2 storage is an active area of research globally. Despite some preliminary research and examples overseas, there remains a lack of established protocols and configurations for offshore carbon capture and storage (CCS) monitoring overlying storage sites and an absence of methods to establish environmental impact in the event of leakage. Over the last 4 years, CSIRO in collaboration with ANLEC R&D and CarbonNet have been undertaking research in the Gippsland region to inform the development of assurance monitoring approaches for subsea CCS operations to address three key technical assurance monitoring challenges: The ‘signal-to-noise’ problem: distinguishing CO2 release signatures from similar naturally occurring variability to reduce false alarm rates in future baseline monitoring design; characterising impact: determining the level of CO2 release that would be associated with environmental impact at a range of scales; and attributing impact: distinguishing changes resulting from other drivers and pressures in multiple-use zones (e.g. climate change) from the activities of CCS operations. The research has included a wide variety of approaches and technologies including the development and testing of fixed and mobile autonomous monitoring systems, chemical and acoustic sensing and the collection of biological datasets. These data have been used in the development of biogeochemical models and to define possible integrated MM&V frameworks. This paper will summarise this research and identify how it could be applied for offshore CO2 storage projects around Australia.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".