Kinetic Modeling of the In-Situ Combustion Process for Athabasca Oil Sands
Bibliographic record
Abstract
Abstract In-situ combustion (ISC) is an effective thermal recovery method that provides an important alternative to steam injection, but it has yet to be widely applied due to the complexity of the process. The modeling of ISC requires an understanding of the behavior of different physical phenomena including phase change, heat and mass transfer, and chemical reactions. Properly conducted ramped temperature oxidation (RTO) tests on crude oil provide critical parameters for modeling. In this study, the focus is to model appropriate kinetics and improve reaction models for ISC. Different chemical reactions occur during ISC in different temperature ranges. For heavy oils and oil sands low temperature oxidation (LTO) dominates below 260°C, yielding partially oxygenated compounds and increasing the viscosity of oil, and as a result, limiting the success of the ISC process. The so-called middle temperature oxidation (MTO) region is a combination of the negative temperature gradient region (NTGR) as well as the onset of thermal decomposition and pyrolysis/cracking of the hydrocarbon phase, some or all of which may have been previously oxidized. Above 350°C, high temperature oxidation (HTO) dominates, representing the more commonly known traditional combustion region. Most of the current reaction kinetics models for ISC only focus on specific conditions such as HTO and are not able to represent the wide range of reactions that occur over a larger temperature range. The objective of this study was to develop reaction kinetic models that can be used to describe the reactions of hydrocarbon fractions at various temperature conditions during the ISC of Athabasca bitumen. In this work, a set of improved kinetic models including LTO, MTO, and HTO reactions based on Saturates, Aromatics, Resins, and Asphaltenes (SARA) fractions in the crude oil are established. These kinetic models are used to reproduce the RTO experimental results through numerical simulation. This research will contribute to the development of more reliable numerical models that can predict ISC performance in different temperature scenarios with greater accuracy and reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".