Can Injection of Low Temperature Air-Solvent LTASI Be a Solution for Heavy Oil Recovery in Deep Naturally Fractured Reservoirs?
Bibliographic record
Abstract
Abstract Heterogeneity and depth rule out steam injection and in-situ combustion processes for heavy-oil recovery in deep naturally fractured reservoirs (NFR). Once it is controlled by a proper injection scheme and the consumption of air injected through efficient diffusion into the matrix, low temperature air injection (LTAI) can be an alternative technique for heavy-oil recovery from deep NFRs. Limited studies on light oils showed that this process was strongly dependent on an oxygen 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 reaction to breakdown asphaltenic molecules effectively. This improves the gravity drainage recovery from the matrix. To study this new idea called low temperature air-solvent injection (LTASI), laboratory tests were performed by immersing heavy-oil saturated cores into air¬ solvent filled reactors to determine the critical parameters on recovery, diffusion coefficient, oxidation kinetics, viscosity reduction, and gravity drainage rate. It is imperative that enough time is given for the diffusion process before injected air filling to fracture network breakthrough. This implies that huff and puff injection is an 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 atmosphere than in 100% solvent. The asphaltene content was lowered more in the air-solvent chamber than in a 100% air case.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".