Bibliographic record
Abstract
Abstract Carbon dioxide (CO 2) sequestration in coal seams represents one (of only two) geological sequestration option that has the potential to yield a value-adding byproduct (enhanced coalbed methane [ECBM] recovery) to mitigate sequestration costs (the other being enhanced oil recovery [EOR]). Although the global pervasiveness of coal seams and their substantial adsorption capacity for CO 2would suggest they represent a significant sequestration opportunity, the infancy of the technology as well as concerns over future coal minability has tempered sequestration capacity estimates. These two issues, an incomplete understanding of the interactions between CO 2and coal (and the implications that derive therefrom, such as knowing what are the most appropriate geological environments for sequestration, what are the best well development strategies, how should wells be operated, etc.) and the potential future minability of coal (i.e., what exactly is a deep, unminable coal that will not be developed at some future date), represent the major obstacles to widespread acceptance of coal seams as a viable carbon sequestration option. The largest CO 2-ECBM field test was performed by Burlington Resources (now ConocoPhillips) at the Allison unit in the San Juan Basin from 1995 to 2001. In this pilot test, approximately 336,000 t of CO 2was injected into four wells completed in the Fruitland coal. Improvement in ECBM recovery was predicted, and the ability of the coal to adsorb and retain CO 2was demonstrated. Smaller two-well and single-well tests have also been performed in Poland, Japan, Canada, and China. These are primarily government-funded demonstration projects with the primary objective of testing carbon sequestration technology. Additional field tests with similar objectives are also currently (as of this writing) in the planning stages as part of the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnership program. Economic analysis suggests that the CO 2-ECBM process can be profitable in some cases. Besides the obvious importance of infrastructure costs and energy prices to economic performance, a key technical factor is the ability to maintain high CO 2injection rates into the coal seams, which is a challenge because CO 2tends to swell coal, thus reducing permeability and injectivity. Overcoming this challenge, via the identification of the most favorable reservoir environments, the best project development strategies, appropriate operating practices, etc., represents a critical milestone toward the widespread acceptance of the technology.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".