Assessment of Protentional Underground Carbon Dioxide Storage in Post Heavy Oil Eor Phase Through Steam, Solvent, or Hybrid Injection: A Field Case Study
Bibliographic record
Abstract
Abstract The Paris Agreement aims to limit global warming to well below 2°C and pursue efforts to limit it to 1.5°C above pre-industrial levels. In response to the Paris Agreement, carbon dioxide storage, also known as carbon capture and storage (CCS), is a critical part of efforts to mitigate climate change in response to the Paris Agreement. Canada has abundant heavy oil resources, but to recover this immobile liquid, hot water or solvents are often injected underground as a heat carrier (SAGD) or dilution (VAPEX) or hybrid (ES-SAGD) to mobilize the heavy oil. However, these processes often result in energy losses and CO2 emissions. Thus, how to effectively treat the produced carbon dioxide seems to be an economic and environmental problem especially under the context of the Paris Agreement and the increasing carbon tax in Canada. To address the urgent problem, this study first establishes the SAGD, warm VAPEX, and solvent-based ES-SAGD process through numerical simulation. Then carbon dioxide is injected underground in the post EOR phase. In steam-based technology (SAGD or ES-SAGD), water is used as an excellent heat carrier to inject underground to displace heavy oil, The left water underground forms an excellent dissolution trapping barrier for carbon sequestration. In solvent-based technology (ES-SAGD or VAPEX), carbon dioxide produced by heating can not only be injected for underground storage in reducing the environmental impact, but also can recover the underground solvent from gas, oil and even water phases further reducing the cost of solvent-based technology. The outcome of this research not only provides potential for carbon sequestration from post-EOR activities, but also supports the transformation of Canada's heavy oil industry towards more solvent-based methods, resulting in economic and environmental benefits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".