Low-Temperature Air/Solvent Injection for Heavy-Oil Recovery in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Summary Limited studies on oil recovery from naturally fractured reservoirs using low-temperature air injection show that the process is strongly dependent on oxygen (O2)-diffusion coefficient and matrix permeability, both of which are typically low. A new approach (i.e., the addition of hydrocarbon solvent gases into air) is expected to improve the diffusivity of the gas mixture and to accelerate the oxidation. To study this new idea, called low temperature air/solvent injection, laboratory tests were performed by soaking heavy-oil-saturated cores in air/solvent filled reactors to determine the critical parameters on recovery. Laboratory tests were complemented by conducting experiments using air at different O2 concentrations: zero (i.e., nitrogen), 21.0 mol% (air), and 37.3 mol% (O2-enriched air). For safety reasons, it is imperative that enough time be given for air diffusion before the injected air breaks through a highly permeable fracture network. This implies that the huff ‘n’ puff type of injection is a plausible option as opposed to the continuous injection of air. A high recovery factor was obtained by soaking a single matrix in an air/solvent chamber at static conditions rather than with air only. The period of pressure stabilization was faster for the air/solvent mixture than in 100% solvent. The asphaltene content was lower in the air/solvent case than in the 100%-air injection case. Instead of pure-hydrocarbon solvent, injection of an air/solvent mixture yields a better recovery with less asphaltene. This is expected to reduce the cost of the process compared with pure-solvent injection. At low temperatures (75C), O2 consumption in the matrix oil was low, while at high temperatures, the O2 was partially (150C) or totally (200C in the presence of propane) diffused and consumed in the matrix.
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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 | 0.000 | 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) | 0.001 | 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".