Carbon Dioxide Sequestration in Unconventional Shale Reservoirs Via Physical Adsorption: An Experimental Investigation
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) is the main greenhouse gas contributing to environmental damage and global warming. It is emitted as a result of many processes, part of which is combustion of oil and gas. One of the methods by which CO2 emissions can be controlled or reduced is through CO2 sequestration processes. This research investigates the ability to store CO2 in shale reservoirs through adsorption and some of the factors impacting the adsorption capacity. CO2 adsorption was measured using the volumetric adsorption method using pulverized shale particles of uniform size. Initially, the void space in the shale-bearing cell was measured using helium. The void space is used in the CO2 adsorption calculations in order to account for the extra volume created when the shale core was pulverized. The effect of varying the CO2 pressure, temperature, and shale volume on the CO2 adsorption capacity was studied. Results showed that both pressure and temperature had a strong effect of CO2 adsorption, with an increase in pressure resulting in an increase in adsorption and an increase in temperature resulting in a decrease in adsorption. Altering the volume of the shale resulted in a change in adsorption as well due to an increase in error as the shale volume decreased relative to the vessel volume. This research provides insight on the impact of multiple factors on CO2 adsorption to shale particles thus illustrating the potential for CO2 storage in unconventional shale reservoirs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".