Kinetic Models for Low Temperature Oxidation Subranges based on Reaction Products
Bibliographic record
Abstract
Abstract In situ combustion (ISC) based enhanced heavy oil recovery is complex because there are numerous chemical reactions taking place simultaneously, in addition to mass transport and flow mechanisms, within the context where oil mobility is controlled largely by its temperature which in turn is controlled by heat transfer all occurring in a reservoir typically several hundred meters deep where geological heterogeneity is uncertain. From a reaction point of view, the complexity arises due to the immense number of components reacting through many different reaction paths in an underground system where the geology and heavy oil saturation vary spatially within the reservoir. It is known that there are four major classes of reactions taking place within an ISC process: low temperature oxidation (LTO), high temperature oxidation (HTO), thermal cracking (TC), and aquathermolysis. Within the reservoir, during ISC, LTO and TC reactions play a major role by providing fuel for HTO. In many documented reaction schemes in the literature, the LTO interval is considered as a single reactive zone spanning a single temperature range. In this work, a new reaction scheme is proposed based on analysis of thermogravimetric data where the LTO reaction temperature range has been separated into three temperature subranges each with their own dominant set of reaction products. The results demonstrate that models of LTO with a single range are inadequate for LTO modeling whereas multiple subranges were capable of representing the behavior of LTO effectively.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".