Optimal Design of Low-Temperature Air Injection with Propane for Efficient Recovery of Heavy Oil in Deep Naturally Fractured Reservoirs: Experimental and Numerical Approach
Bibliographic record
Abstract
Low-temperature air/solvent injection (LTASI) can be a possibility if injected air/solvent into a fractured reservoir diffuses into the matrix effectively to oxidize oil in it while reducing its viscosity by temperature and solvent dissolution. However, early breakthrough of air with partial consumption of oxygen as a result of the highly conductive nature of the reservoirs is a concern. Once it is controlled by a proper injection scheme and 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 naturally fractured reservoirs (NFRs). Limited number of studies on light oils showed that this process was highly dependent upon the oxygen diffusion coefficient and matrix permeability. In this process, oil production is governed by drainage and stripping of light oil components, which have a greater effect on recovery than the swelling of oil. In the present study, static laboratory tests were performed that complement previously published experimental data, by immersing heavy-oil-saturated porous media into air-filled reactors to determine critical parameters on recovery, such as the diffusion coefficient. A data acquisition system was established for continuous monitoring of the pressure at different temperatures. Also analyzed was the possibility of hydrocarbon gas additive to air, minimizing the oil viscosity increase created by oxidation reactions. On the basis of core-scale experimental results, a numerical simulation model of air diffusion into a single matrix was created to obtain the diffusion coefficient through matching of laboratory results. Then, sensitivity runs were performed for different matrix sizes and composition of injected gas (air and hydrocarbon). Additionally, a scaling-up study was performed to obtain an approximate production time for different matrix block sizes and temperatures. It is imperative that enough timing is required for the 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 | 0.000 | 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 | 0.000 | 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".