Effect of Surface Wettability on the Miscible Behaviors Of Co2-Hydrocarbon in Shale Nanopores
Bibliographic record
Abstract
Abstract The minimum miscible pressure (Pm) of CO2-hydrocarbon mixtures in nanopores is a key parameter for CO2-enhanced shale oil recovery. Although the miscible behaviors of CO2-hydrocarbon mixtures in nanopores have been widely investigated through the simulations and calculations, the heterogeneity of shale components with different affinity to hydrocarbons results in the deviation of traditional predictions and motivates us to investigate how the surface properties influence the CO2-hydrocarbon miscible behaviors in nanopores. In this work, we established a model and framework to determine the wettability-dependent physical phenomena and its impact on the Pm of CO2-hydrocarbon in shale nanopores. First, a generalized scaling rule is established to clarify the potential correlation between critical properties shift and wettability based on the analysis of microscopic interactions (fluid-surface interactions and fluid-fluid interactions). Second, a wettability-dependent SKR EOS is structured and a generalized and practical framework for confined phase behavior with different surface wettability is constructed. Subsequently, the Pm of CO2-hydrocarbon mixtures in confined space with various wettability is evaluated with our model. The calculated results demonstrate that the nanoconfined effects on Pm not only relate to the pore dimension but also depend on the contact angle. In an intermediate-wet nanopore, the minimum miscible pressure approaches the bulk value. In an oil-wet nanopore with a width smaller than 100nm, the minimum miscible pressure is suppressed by the confined effects, and the reduction is further strengthened with a reduction in pore dimension and increase of wall-hydrocarbon affinity. Our work uses a macroscopically measurable parameter (contact angle) to characterize the shift of critical properties derived from the microscopic interactions, and further construct a generalized and practical framework for phase behavior and minimum miscible pressure determination in nanopores with different surface properties. The method and framework can make a significant contribution in the area of upscaling a molecular or nanoscale understanding to a reservoir scale simulation in shale gas/oil research.
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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".