Investigation of Thermal Finger Print in Accelerating Rate Calorimetry for Air-Injection Enhanced Oil Recovery Processes.
Bibliographic record
Abstract
Abstract The Accelerating Rate Calorimeter (ARC) is unique for its exceptional adiabacity, its sensitivity and its sample universality. Accelerating Rate Calorimetry is used as one of the screening tests employed to determine the suitability for Air-Injection enhanced oil recovery. These tests indicate oil reactivity and exothermicity over a broad range of temperatures: low temperature range, negative temperature gradient region and high temperature range. An experimental and simulation study was carried out to expand understanding 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 13.8 MPag (2000 psig) to identify the temperature ranges over which the oil reacts with oxygen in the injected air. Experimental analysis for self-heat rate from accelerating rate calorimetry data and mass loss rates from differential thermo-gravimetric analysis shows the influence of mass transfer of oxygen within bitumen in the low and high temperature range. A numerical model was developed to predict the solubility, diffusivity and reactions of oxygen with bitumen. The model incorporates solubility of oxygen using partition/equilibrium coefficient (K-value) which transfers throughout oil layer by diffusion. This model considers both low and high temperature oxidation and thermal cracking reactions. The results show by integrating mass transfer with the traditional kinetic model it is possible to predict the negative temperature gradient region which will therefore predict the onset in the exotherm of Self- Heat-Rate. The results also show that the swellability of bitumen due to solubility of air and product gases creates a concentration gradient of oxygen inside the bitumen. Viscosity and temperature dependence of the mass transfer of oxygen is linear, as time passes and chemical reaction becomes more important with increasing temperature, the relationship deviates from linearity. With an increase in temperature, the influence of chemical interaction on the oxygen distribution becomes greater, and this results in a shorter initial stage of mass transfer of oxygen within the bitumen film at low temperatures. This implicates that the ARC can be an effective tool for understanding the role of mass transfer on the oxidation characteristic for predicting low, negative temperature gradient and high temperature regions.
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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 | 0.000 |
| 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".