A method for pore-scale simulation of single-phase shale oil flow based on three-dimensional digital cores with hybrid mineral phases
Bibliographic record
Abstract
The mineral properties of the pore walls have a great influence on the single-phase shale oil flow at the pore scale. In this paper, a new method is proposed for pore-scale simulation of single-phase shale oil flow based on digital cores with hybrid mineral phases. This method can identify each mineral pore wall and correspondingly consider the adsorption layer and slippage boundary condition. First, three-dimensional (3D) digital cores with hybrid mineral phases are reconstructed from two-dimensional (2D) scanning electron microscope images of a shale sample, and correspondingly the pore space is divided with computational grids. Second, a mathematical model of shale fluid flow is established based on the Navier–Stokes (N–S) equation, combined with the slip length and viscosity formula. Finally, the equations are discretized on the mesh by the finite volume method and solved by the semi-implicit method for pressure-linked equations for flow simulation of shale oil in the 3D digital cores. By applying the method, we analyze effects of total organic carbon in volume, slippage, and adsorption on the single-phase shale oil flow based on 3D digital cores with hybrid mineral phases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".