Investigation of the Driving Force of Replacing Adsorbed Hydrocarbons by CO<sub>2</sub> in Organic Matter from an Energy Perspective
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) has been widely used to enhance the recovery of adsorbed hydrocarbons from the organic matter (OM) in shale formations. To reveal the driving force of replacing adsorbed hydrocarbons from OM by CO 2, we performed molecular dynamics (MD) simulations of the replacement process and calculated the interaction forces between CO 2 and hydrocarbons. In addition, based on the umbrella sampling method, steered MD simulations were performed, and the free energy profiles of hydrocarbons were obtained using the weighted histogram analysis method. Results show that the condition of the hydrocarbon replacement by CO 2 requires the hydrocarbon to have sufficient kinetic energy or to have a sufficiently large attractive force exerted to ensure that the hydrocarbon escapes the potential well of the OM. The attractive forces exerted on hydrocarbon molecules by CO 2 can significantly decrease the energy barrier associated with hydrocarbon movement away from the OM surface. Furthermore, both CO 2 and supercritical CO 2 can effectively displace adsorbed hydrocarbon gas (methane) on the OM, while supercritical CO 2 is required to enhance the recovery of adsorbed hydrocarbon oil ( n -dodecane). The results obtained in this study provide guidance for enhancing the recovery of adsorbed hydrocarbons by CO 2 in shale formations.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".