Mechanism of shale oil displacement by CO2 in nanopores: A molecular dynamics simulation study
Bibliographic record
Abstract
Utilizing CO2 to enhance shale oil recovery has a huge potential and thus has gained widespread popularity in recent years. However, the microscopic mechanisms of CO2 enhancing shale oil recovery remain poorly understood. In this paper, the molecular dynamics simulation method is adopted to investigate the replacement behavior of CO2 in shale oil reservoirs from a micro perspective. Three kinds of n-alkanes are selected as the simulative crude oil in silica nanopores. Molecular dynamics models are established to study the occurrence patterns of different alkanes on the rock surface and the alkane[1]stripping characteristics of CO2. The fluid density, mean square displacement and centroid variation are evaluated to reveal the effect of CO2 on alkanes. The results indicate that different alkanes exhibit varying occurrence characteristics of oil film on the rock surface of the shale reservoir. Specifically, a higher carbon number leads to a thicker oil film. Through the alkane molecular gaps, CO2 penetrates the alkane molecular system and reaches the rock surface to effectively strip the oil film of different alkane molecules. CO2 will more readily mix with the stripped oil molecules and displace them from the rock surface when the carbon number is small. The process for CO2 replacing crude oil on the rock surface can be divided into four typical stages, namely, CO2 diffusion, competitive adsorption, emulsification and dissolution, and CO2-alkanes miscible phase (for light alkanes). This study contributes to the improvement of micro-scale enhanced oil recovery mechanisms for shale oil via CO2 injection and provides a guidance for enhancing shale oil recovery by using CO2. Document Type: Original article Cited as: Wu, Z., Sun, Z., Shu, K., Jiang, S., Gou, Q., Chen, Z. Mechanism of shale oil displacement by CO2 in nanopores: A molecular dynamics simulation study. Advances in Geo-Energy Research, 2024, 11(2): 141-151. https://doi.org/10.46690/ager.2024.02.06
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".