Investigation of Thermal Fingerprint in Accelerating-Rate Calorimetry for Air-Injection Enhanced-Oil-Recovery Processes
Bibliographic record
Abstract
Summary The accelerating-rate calorimeter (ARC) is unique for its exceptional adiabaticity, its sensitivity, and its sample universality. Accelerating Rate Calorimetry is one of the screening tests used to determine the suitability for air-injection enhanced oil recovery (EOR). These tests show oil reactivity and exothermicity over a broad range of temperatures: low-temperature range (LTR), negative-temperature-gradient region (NTGR), and high-temperature range (HTR). 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 13.89 MPag (2000 psig) to identify the temperature ranges over which the oil reacts with oxygen in the injected air. Self-heat rate from accelerating-rate calorimetry and mass-loss rates from the differential thermogravimetric analysis show the influence of mass transfer of oxygen within bitumen in the LTR and HTR. A numerical model was developed to integrate the concept of mass transfer with a reaction-kinetic model. The model incorporates solubility of oxygen with partition equilibrium coefficient (K-value) as a medium to introduce oxygen into the bitumen layer, which later transfers throughout oil layer by diffusion. This model considers both low- and high-temperature oxidation (LTO and HTO), and thermal-cracking reactions, as described in traditional reaction-kinetic models of in-situ-combustion (ISC) processes. Results show that formation of an asphaltenes film in the LTR caused by oxidation of maltenes obstructs oxygen (mass-transfer restriction) penetration into the bitumen layer. The simulated result shows that, by integrating mass transfer with the kinetic model, it is possible to predict the NTGR. Viscosity and temperature dependence on 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 increasing 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 implies that the ARC can be a useful tool for understanding the effect of mass transfer on the oxidation characteristic for predicting LTR, NTGR, and HTR.
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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".