Molecular Dynamics Study on CO<sub>2</sub> Storage in Water-Filled Kerogen Nanopores in Shale Reservoirs: Effects of Kerogen Maturity and Pore Size
Bibliographic record
Abstract
CO 2 sequestration in shale reservoirs is an economically viable option to alleviate carbon emission. Kerogen, a major component in the organic matter in shale, is associated with a large number of nanopores, which might be filled with water. However, the CO 2 storage mechanism and capacity in water-filled kerogen nanopores are poorly understood. Therefore, in this work, we use molecular dynamics simulation to study the effects of kerogen maturity and pore size on CO 2 storage mechanism and capacity in water-filled kerogen nanopores. Type II kerogen with different degrees of maturity (II-A, II-B, II-C, and II-D) is chosen, and three pore sizes (1, 2, and 4 nm) are designed. The results show that CO 2 storage mechanisms are different in the 1 nm pore and the larger ones. In 1 nm kerogen pores, water is completely displaced by CO 2 due to the strong interactions between kerogen and CO 2 as well as among CO 2 . CO 2 storage capacity in 1 nm pores can be up to 1.5 times its bulk phase in a given volume. On the other hand, in 2 and 4 nm pores, while CO 2 is dissolved in the middle of the pore (away from the kerogen surface), in the vicinity of the kerogen surface, CO 2 can form nano-sized clusters. These CO 2 clusters would enhance the overall CO 2 storage capacity in the nanopores, while the enhancement becomes less significant as pore size increases. Kerogen maturity has minor influences on CO 2 storage capacity. Type II-A (immature) kerogen has the lowest storage capacity because of its high heteroatom surface density, which can form hydrogen bonds with water and reduce the available CO 2 storage space. The other three kerogens are comparable in terms of CO 2 storage capacity. This work should shed some light on CO 2 storage evaluation in 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".