Large scale simulation of UCG process applying porous medium approach
Bibliographic record
Abstract
Underground coal gasification (UCG) has significant advantages and can be categorized as a clean coal technology for producing syngas in situ. However, a comprehensive understanding of the process is lacking, because it takes place deep underground and consists of multiple phenomena. Hence UCG modelling can be employed to investigate different aspects of this process. While small‐scale processes can be mechanistically informative, large‐scale processes may behave quite differently. In this work, detailed 3D simulation modelling of three widely‐applied UCG technologies was conducted for the Ardley coal formation (Alberta, Canada) in order to compare the performance of different technologies at field scale. The results of these comparisons can be helpful for selecting the right technology for a desired UCG pilot test. The results show that in spite of a higher heating value of produced syngas from the P‐CRIP (parallel controlled retracting injection point) method over the L‐CRIP (linear controlled retracting injection point) method, the volumetric rate and sweep efficiency of these methods are comparable. Moreover, we conducted 2D cross‐sectional modelling of the Thulin test as the earliest UCG process at great depth and in tight coal seams to address modelling issues. Several possible approaches, such as geomechanical modelling, are presented to resolve the issues of UCG modelling in tight coal seams. The modelling results are analyzed and compared with the field results. Comparisons show an engineering match for the composition of the produced syngas. Computer Modelling Group's STARS software was used in this study as the porous medium modelling approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".