CO2 Injection for Enhanced Gas Recovery and Geological Storage of CO2 in the Long Coulee Glauconite F Pool, Alberta
Bibliographic record
Abstract
Abstract Impure CO2 containing less than 2% H2S has been injected since 2002 into the depleted Long Coulee Glauconite F gas Pool in southeastern Alberta. Breakthrough was observed within one to three years in producing wells, leading to their abandonment. Simulation studies reported in this paper indicate that additional gas was recovered as a result of CO2 injection. An interesting observation at the breakthrough wells was that the CO2 broke through ahead of the H2S. The partitioning of the H2S and CO2 as they flow through the reservoir was studied in detail. One objective of the reported work is examination of interactions of the injected gas with the in-situ fluids and the displacement of the in-situ gas by the injection gas, for better understanding of the mechanisms involved in enhanced-gas recovery. Another objective of the work is to study factors that affect the spread of the injected gas in a depleted oil and gas reservoir and its implications with respected to CO2 geological storage. The results of this study indicate that at low pressures, the injected gas occupies a large reservoir volume and exhibits little density difference with the in-situ fluids, leading to rapid spread of the injected gas and early breakthrough. Also, it was found that in the case of Long Coulee Glauconite F gas Pool the well-spacing used for production did not allow a detailed geological characterization that was required for accurate prediction of breakthrough as a result of gas-gas displacement. Simulation studies, together with displacement experiments in the laboratory reported elsewhere, confirmed that the preferential solubility of H2S in the reservoir water led to stripping of the H2S at the leading gas front and it delayed its breakthrough relative to that of CO2. The implications of such chromatographic partitioning of H2S and CO2 in geological storage of impure CO2 streams are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".