Simulation Study of Underground Coal Gasification in Alberta Reservoirs: Geological Structure and Process Modeling
Bibliographic record
Abstract
Underground coal gasification (UCG) as an efficient method for the conversion of the world’s coal resources into energy, liquid fuels, and chemicals has attracted lots of attention in recent years. This paper is concerned with a feasibility study of the UCG process for Alberta reservoirs using the three-dimensional simulation of this process based on a unique porous media approach. The proposed approach combines the effects of heat, mass transport, and chemical reactions to achieve this goal. The Computer Modeling Group (CMG) software STARS is used for simulation. The geological structure including coal and layers interspersed between coal seams (claystone layers), the porosity/permeability variation, and the chemical processes with corresponding parameters are considered in the model. Chemical stoichiometry coefficients of the pyrolysis process are calculated from proximate and extended experimental data. Genetic algorithm and pattern search are used for parameter estimation. This model is developed to study UCG in deep coal seams and can be used for production prediction and optimization of the process. The simulation results, such as cavity formation, temperature profile, and gas composition at the producer, are presented. Finally, the results are analyzed on the basis of field pilot tests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".