1 ZAMA ACID GAS EOR, CO2 SEQUESTRATION AND MONITORING PROJECT
Bibliographic record
Abstract
A comprehensive monitoring, mitigation, and verification (MMV) plan is being implemented at the Zama Oil Field in northwestern Alberta, Canada, to determine the effect of acid gas injection for the simultaneous purpose of disposal, sequestration of CO2, and enhanced oil recovery (EOR). The injection process and hydrocarbon recovery will be carried out by Apache Canada Ltd. while the Energy & Environmental Research Center (EERC) through the Plains CO2 Reduction (PCOR) Partnership (one of seven U.S. Department of Energy Regional Carbon Sequestration Partnerships) will conduct the MMV activities at the site. Research activities are being conducted at multiple scales of investigation in an effort to validate the ultimate fate of the injected gas. Geological, geomechanical, geochemical, and engineering work is being used to fully describe the injection zone and adjacent strata. Certifying the integrity of the caprock is a critical research area, with additional tests being completed on the reef to determine the nature of potential geochemical and geomechanical changes that may occur because of acid gas exposure. Fluids will be sampled at the producing horizon and directly above the horizon to ensure containment through active and inactive wells in the pinnacle. A perfluorocarbon tracer is being used to track fluid flow throughout the system and to identify leakage should it occur. With over 800 pinnacles in the Zama
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".