Monitoring the fate of injected CO2 using geodetic techniques
Bibliographic record
Abstract
Abstract Geodetic methods comprise one class of geophysical data that are sensitive to changes in effective pressure within operating reservoirs, albeit indirectly through induced deformation. Geodetic observations, which have observation intervals that vary from seconds to days, weeks, or months, generally provide more frequent sampling compared to existing geophysical methodologies (such as seismic time-lapse monitoring), which typically invoke repeat times of months to years. These differences in sampling intervals are primarily due to the extensive effort, and hence cost, of conducting geophysical field operations, which often precludes executing a large number of surveys. Satellite-based interferometric synthetic aperture radar (InSAR) is cost effective and used in many applications, including monitoring the injection of carbon dioxide (CO2) for both long-term storage and enhanced oil production. An application to the geologic sequestration of CO2 in Algeria revealed northwest migration along a fault/fracture zone intersected by the injection well. A study in a Texas field demonstrated that enhanced oil recovery utilizing CO2 leads to observable surface deformation that may be used to characterize the sequestered CO2 and to estimate the pressure changes within the reservoir induced by injection and production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".