Chemical-Reaction Mechanisms That Govern Oxidation Rates During In-Situ Combustion and High-Pressure Air Injection
Bibliographic record
Abstract
Summary The lack of an accurate reaction model for petroleum-oxidation rates is a serious hindrance to the simulation of oil-recovery processes that involve air injection. However, the chemical literature on hydrocarbon oxidation contains many examples of possible reaction mechanisms that could serve as guides. These mechanisms were screened to identify generally accepted reaction paths that could help reveal how oxidation occurs in petroleum reservoirs. It was found that there are at least eight groups of fundamental reactions that can seriously affect oxidation rates of crude oils or their pyrolysis products. These eight reactions are as follows: two that lead to hydroperoxide formation; “branching” by hydroperoxides; two reactions governing the negative temperature coefficient (NTC) region; oxidation inhibition; at least one rate-controlling reaction at very high temperatures; and the combustion of coke that is produced by pyrolysis. Each of these groups exerts an influence within a separate, identifiable range of conditions. These reactions, and the conditions under which they become important, are outlined in this paper. Various oxidation behaviors that were reported for both light and heavy crude oils were then compared and aligned with the eight identified reactions. The result was a framework for selecting pseudoreactions that can facilitate the prediction of the oxidation kinetics under a wide range of oilfield conditions. Some of these pseudoreactions involve the direct representation of free radicals or other chemical intermediates, which is a departure from conventional practice for in-situ-combustion simulation. The new reaction framework is expected to serve as a reliable guide to the construction of predictive reaction models and, consequently, improved simulation of both in-situ-combustion and high-pressure-air-injection (HPAI) processes.
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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) | 0.000 | 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 | 0.000 |
| 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".