Insights into In-situ Combustion by Analytical and Pore-network Modeling
Bibliographic record
Abstract
In-situ combustion is a subject of continuing interest due to its rich interplay of flow, mass/heat transfer and reactions. The process complexity, however, has precluded its thorough understanding. In this paper, we provide some analytical results and pore-network simulations that explore fundamental aspects of this process.The analytical approach relies on high-activation energy asymptotics. It consists of the assumption that the combustion front is a thin layer, within which reactions occur. We review our recent findings and derive exact results for the front temperature, the front propagation velocity and oxygen consumption. The results are then compared to pore-network simulations, where the effects of pore structure are included in a detailed simulation at the pore-network level. The simulations demonstrate the validity of the thin layer assumption and provide approaches for upscaling.Then, the effect of porous medium heterogeneity, a ubiquitous feature of oil reservoirs, on the front is considered, in particular, in the form of a layered reservoir system. The fronts in two layers can be coupled under certain conditions, depending on the permeability-thickness contrast R between the layers, the extent h of external heat losses and the inlet conditions. We derive the parameter space, where this coupling occurs, and compare the results with the porenetwork simulations. Stability of the derived stationary nonequilibrium states is also analyzed. It is shown that the adiabatic states are always stable; whereas the non-adiabatic states being conditionally stable. This suggests that coherence of the fronts in the layered system is possible under certain conditions and that the effect of heterogeneity is not as detrimental to the process and its sweep efficiency, provided that heterogeneity does not exceed a certain limit, Rc.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".