CO2 utilization and sequestration potential in deep coal seams: A case study on Carboniferous coals from the Karaganda Basin, Kazakhstan
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it