Quantification of low‐temperature oxidation of light oil and its SAR fractions with TG‐DSC and TG‐FTIR analysis
Bibliographic record
Abstract
Abstract The oxidation reaction is the key to determining the success of air flooding. In this paper, experimental and theoretical techniques have been developed to identify the low‐temperature oxidation (LTO) mechanisms for light oil during air flooding by comprehensively analyzing thermal stability and oxidation process of the crude oil and its SAR (ie, saturates, aromatics, and resins) fractions. Experimentally, both a thermogravimetric analyzer coupled with differential scanning calorimetry (TG‐DSC) and a thermogravimetric analyzer coupled with Fourier transform infrared spectrometer (TG‐FTIR) are employed to quantify the LTO process of crude oil and each SAR fraction as well as the corresponding oxidation properties. Theoretically, reaction models have been developed to reproduce the experimentally identified reactions. The results show that the oxygen addition reaction and the bond scission reaction occur simultaneously. The former can be initiated when temperature is higher than 50°C, and it is gradually shifted to the latter with the continuous increase in reservoir temperature. The LTO products of light oil include H 2 O, CO 2 , carboxylic acids, alcohols, phenols, and ethers. Saturates, aromatics, and resins are all the sources of H 2 O, CO 2 , alcohols, and carboxylic acids, whereas ethers are mainly derived from aromatics and resins. At the beginning of an air flooding process, heat is mainly generated from the oxidation of aromatics and resins. Subsequently, oxidizing saturates gradually dominates the air flooding process with an increase in the reservoir temperature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".