Marine CSEM synthetic study to assess the detection of CO2 escape and saturation changes within a submarine chimney connected to a CO2 storage site
Bibliographic record
Abstract
SUMMARY Carbon capture and storage (CCS) within sealed geological formations is an essential strategy to reduce global greenhouse gas emissions, the primary goal of the 2015 United Nations Paris Agreement. Large-scale commercial development of geological CO2 storage requires high-resolution remote sensing methods to monitor CO2 migration during/after injection. A geological formation containing a CO2 phase in its pore space commonly exhibits higher electrical resistivity than brine-saturated (background) sediments. Here, we explore the added value of the marine controlled-source electromagnetic (CSEM) method as an additional and relevant geophysical tool to monitor moderate to significant changes in CO2 saturation within a fluid conduit breaking through the seal of a CCS injection reservoir, using a suite of synthetic studies. Our 2-D CSEM synthetic models simulate various geological scenarios incorporating the main structural features and stratigraphy of two North Sea sites, the Scanner Pockmark and the Sleipner CCS site. Our results show significant differentiation of leakage through the seal with CO2 saturation ($S_{{\rm CO}_2}$) ranging between 20 and 50 per cent, while our rock physics model predicts that detection below 20 per cent would be challenging for CSEM alone. However, we are able to detect with our 2-D inversion models the effects of saturation with 10 and 20 per cent CO2 within a chimney with 10 per cent porosity. We demonstrate that simultaneous inversion of Ey and Ez synthetic electric field data facilitates a sharper delineation of a CO2 saturated chimney structure within the seal, whereas Ez synthetic data present higher sensitivity than Ey to $S_{{\rm CO}_2}$ variation, demonstrating the importance of acquiring the full 3-D electric field. This study illustrates the value of incorporating CSEM into measurement, monitoring and verification strategies for optimal operation of marine CCS sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".