A Comprehensive Kinetic Theory to Model Thermolysis, Aquathermolysis, Gasification, Combustion, and Oxidation of Athabasca Bitumen
Bibliographic record
Abstract
Abstract Extraction and upgrading of bitumen in Alberta, Canada uses large amounts of energy, generates huge volumes of acid gas, consumes massive volumes of water, and is costly. Most bitumen produced in Alberta is converted in surface upgraders to synthetic crude oil (SCO), a 31 to 33°API oil product. Next, SCO is converted to transportation fuels and lubricants in conventional refineries. Bitumen upgrading requires hydrogen. Today, most of the hydrogen is produced by steam reforming of methane which requires huge amounts of methane. Alternatively, hydrogen can be generated by in situ gasification of bitumen. Gasification is potentially more energy efficient with reduced emissions since acid gases are sequestered to some extent in the reservoir. Also water usage is lowered and heavy metals and sulfur compounds in the bitumen tend to remain downhole. The overall objective of this research is to understand and optimize hydrogen generation from bitumen reservoirs. In situ technologies that convert bitumen to hydrogen will have direct application in bitumen upgrading, use as feedstock for ammonia and other chemicals, and may be a key step to start a hydrogen economy. One key step towards the design of in situ hydrogen generations processes is the construction of the reaction scheme together with the associated kinetic parameters. Here, a unified kinetic model that takes pyrolysis (thermolysis, thermal cracking), aquathermolysis, gasification, and combustion (oxidation) of Athabasca bitumen has been assembled. The model has been calibrated against 7 experimental and plant data sets (with 149 data points in total) and implemented in a thermal reservoir simulator. The unified kinetic model was developed by performing a global match of the reaction scheme and kinetic parameters against all available pyrolysis, aquathermolysis, gasification, combustion, and oxidation data.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".