Using Chemical and Isotopic Data to Quantify Ionic Trapping of Injected Carbon Dioxide in Oil Field Brines
Bibliographic record
Abstract
Injection of carbon dioxide into depleted oil fields or deep saline aquifers represents one of the most promising means of long-term storage of this greenhouse gas. While the ultimate goal of CO2 injection in the subsurface is mineral storage of CO2 as carbonates, short-term (<50 year) storage of injected CO2 is most likely to be accomplished by ionic trapping of CO2 as bicarbonate ions (HCO3-) and hydrogeological trapping of molecular CO2. Here, we demonstrate a technique for quantifying ionic trapping of injected CO2 as HCO3- using geochemical data collected prior to and during 40 months of CO2 injection into a hydrocarbon reservoir at the International Energy Agency (IEA) Weyburn CO2 Monitoring and Storage Project, Saskatchewan, Canada. As a result of injection of CO2 with a low carbon isotope ratio (delta13C value), fluid and gas samples from four selected production wells showed an increase in HCO3- concentration and a decrease in delta13C values of HCO3- and CO2 over the observation period. Isotope and mass balance calculations indicate that, after 40 months of injection, approximately 80% of the HCO3- in the reservoir brines sampled from the four wells formed via dissolution and dissociation of injected CO2. This chemical and isotopic technique should be applicable to CO2 injection and storage in oil fields and in deep saline aquifers, provided there is sufficient carbon isotopic distinction between injected CO2 and baseline aquifer HCO3- and CO2.
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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".