Vapor Phase Combustion in Accelerating Rate Calorimetry for Air-Injection EOR Processes
Bibliographic record
Abstract
Abstract The Accelerating Rate Calorimeter (ARC) is unique for its versatility of operation and application - reliability, validity, and accuracy of results - due to very high adiabaticity. Accelerating Rate Calorimetry is one of the screening tests employed to determine the suitability of a reservoir for air- injection Enhanced Oil Recovery. The ARC is well suited for investigating the reaction mechanisms in the Low-Temperature Range, Negative Temperature Gradient Region, and High-Temperature Range. The ARC provides full time-temperature, time-pressure, and self-heat rate-inverse absolute temperature profiles. An experimental and simulation study was carried out to expand knowledge and interpretation of the data derived from high pressure closed ARC tests. Athabasca bitumen was used for the experimental study in a closed ARC at an initial pressure of 13.8 MPag (2,000 psig) to identify the nature of the oxidation reactions occurring over the different temperature ranges. The simulation component of the study focused on the development of a numerical model that captured the elements of the ARC test. The model incorporated solubility of oxygen and diffusion to control the transfer of oxygen in the liquid oil phase. Mass transfer was found to play an important role at low temperatures up to the temperature where chemical interaction starts to control the distribution of oxygen within the liquid bitumen. Likewise, vaporization of oil and generation of vapor by cracking reactions are also believed to play an important role in air injection processes. Therefore, a vapor phase combustion reaction was integrated into the traditional Belgrave s kinetic model. This modified model predicted the combustion of vaporized oil in the gas phase by flammable limits and rate of diffusion of the vaporized component in the gas phase to become flammable. The results of this study indicated that with the addition of mass transfer to the traditional kinetic model, it was possible to predict the negative temperature gradient region. The result showed solubility and diffusion of oxygen played an important role up to a temperature of 125°C where chemical reactions started to control the distribution of oxygen within the liquid bitumen. The results also showed that vapor phase combustion created a temperature gradient between the gas and bitumen phases when vaporized components became flammable. This showed that the ARC could be an effective tool for understanding liquid and vapor phase reaction and their relative importance in different temperature regimes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".