CO2 utilization and sequestration potential in deep coal seams: A case study on Carboniferous coals from the Karaganda Basin, Kazakhstan
Bibliographic record
Abstract
Kazakhstan is a major coal producer and emitter of carbon dioxide (CO 2 ), presenting both a challenge and an opportunity for CO₂ utilization and storage. The main goal of this work is to study the feasibility of CO 2 as a feedstock for enhanced coalbed methane recovery (CO 2 -ECBM), as well as the associated geological storage potential of the D6 coal seam in the Karaganda Basin. For this purpose, coal samples were investigated using elemental analysis, Rock-Eval pyrolysis (RE), organic petrography as well as low-pressure (LP: N 2 , CO 2 ), and high-pressure (HP: CO 2 , CH 4 ) sorption tests. Vitrinite reflectance values show that seam D6 reached the medium-volatile bituminous rank. Higher organic matter content significantly increases the LP CO 2 sorption capacity. The adsorption-desorption isotherms of CO 2 recorded under both LP and HP conditions show a hysteresis loop. This is probably due to interactions between CO 2 and functional groups leading to enhanced physisorption at LP and chemisorption and matrix swelling at HP conditions. This effect is favorable for storage purposes as it implies safe CO 2 trapping even at reduced reservoir pressure. The CBM potential of seam D6 is estimated at 9 billion m 3 initial gas and 360 million m 3 producible gas in place. Estimates of the adsorptive and total CO 2 storage capacity yielded 1.1 and 3.6 gigatons (Gt), respectively. With this considerable total storage capacity, Kazakhstan's current annual CO 2 emissions could be stored for 14 years. This study highlights how CO 2 can be effectively utilized as a feedstock to enhance methane recovery while achieving long-term CO 2 sequestration.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".