A Method for Quantifying Low-Temperature Oxidation Coke Formation during In Situ Combustion of Heavy Oil
Bibliographic record
Abstract
In situ combustion (ISC) is a widely used thermal recovery technique for heavy oil, in which coke plays a critical role in sustaining the combustion front. Notably, coke in ISC can be formed via two distinct pathways─low-temperature oxidation (LTO) and high-temperature thermal cracking (pyrolysis)─depending on the ignition mode (spontaneous vs artificial). The coke yield directly influences the stability and efficiency of the ISC process. This work introduces an innovative TGA/DTG-based method for rapidly estimating coke generation during the LTO of heavy oil. By comparing TGA/DTG curves obtained in oxidative (air) versus inert atmospheres, we can identify the portion of coke produced from LTO reactions (within the LTO–FD temperature range) separately from pyrolytic coke. Results indicate that TGA-based total coke yield estimates consistently underestimate those obtained from isothermal oxidation experiments by approximately 18–19%. To address this deviation, a “coke retention factor” ( R f ) is introduced to quantify the effective coke retention efficiency under dynamic TGA conditions. In this study, R f values of approximately 0.461–0.496 were observed, effectively compensating for the underestimation and demonstrating consistency across heavy oils with similar SARA compositions. Additionally, a comparative analysis based on three compositionally similar heavy oil samples preliminarily supports the broader applicability of the R f parameter. Furthermore, the conventional three-stage division and kinetic modeling strategies in TGA-based crude oil oxidation research were reevaluated. A comparative kinetic analysis using Coats–Redfern, KAS, and OFW methods revealed that the commonly adopted first-order reaction model may significantly underestimate activation energies in such multistage oxidation systems. Overall, the proposed approach significantly improves the experimental efficiency and overcomes the limitations of time-intensive autoclave tests by leveraging TGA data for rapid coke yield estimation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".