Optimal Design of Low Temperature Air Injection for Efficient Recovery of Heavy Oil in Deep Naturally Fractured Reservoirs: Experimental and Numerical Approach
Bibliographic record
Abstract
Abstract Low temperature air injection (LTAI) can be a possibility if injected air diffuses into matrix effectively to oxidize oil in it creating enhanced gravity drainage of lower viscosity oil. However, early breakthrough of air with partial consumption of oxygen due to the highly conductive nature of the reservoirs is a concern. Once it is controlled by proper injection scheme and consumption of air injected through efficient diffusion into matrix, LTAI can be an alternative technique for heavy-oil recovery from deep NFR. Limited number of studies on light oils showed that this process was highly dependent on oxygen diffusion coefficient and matrix permeability. In this process, oil production is governed by drainage and stripping of light oil components has a greater effect on recovery than the swelling of oil. In the present study, static laboratory tests were performed by immersing heavy-oil saturated porous media into air filled reactors to determine critical parameters on recovery; diffusion coefficient and gravity drainage rate. A data acquisition system was established for continuous monitoring of pressure at different temperatures. Also analyzed was the possibility of hydrocarbon gas additive to air to enhance diffusion into matrix. A numerical model of air diffusion into a single matrix was created to obtain diffusion coefficient through matching the lab results. Then, sensitivity runs were performed for different matrix properties and composition of injected gas (air and hydrocarbon). It is imperative that enough timing is required for diffusion process before injected air filling to fracture network breakthrough. This implies that huff and puff type injection is an option as opposed to continuous injection of air. The optimal design and duration of the cycles were also tested experimentally and numerically for a single matrix 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".