Screening <i>In Situ</i> Combustion Applicability for a Heavy Oil Candidate Reservoir with an Accelerating Rate Calorimeter
Bibliographic record
Abstract
Abstract Air injection has immense potential for hydrocarbon recovery from various reservoirs. One of the screening techniques which can be applied to evaluate a candidate oil for the air injection process is the accelerating rate calorimeter (ARC). The unique feature of this instrument is that it can provide adiabatic conditions and handle experiments at high pressures. This paper reviews four tests performed in closed and flowing ARCs to fingerprint and observe the thermal behavior of a crude oil. The crude oil used for this study is characterized as a 19.3°API and viscosity of 710 mPa.s at 21°C. The oxidation experiments were performed under two scenarios of oil-only and oil in the presence of native carbonate core. Initial starting conditions of each test were at a temperature of 23°C and a reservoir pressure of 13.8 MPa. Flowing ARC experiments showed that Low-Temperature Oxidation occurs at a temperature of about 150°C, whereas ignition occurs at about 350°C when High-Temperature Oxidation region was dominant. However, when using the closed ARC, the thermal behavior of the studied oil appeared to have different temperature characteristics, and the onset of the maximum self-heat rate occurred at temperature of 288°C. The effect of the vapor phase combustion as well as the calculation of kinetic parameters are also discussed in this work.
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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) | 0.000 | 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 | 0.001 | 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".