Physical simulation of pore filling pressure gradient at different scales in shale oil reservoirs
Bibliographic record
Abstract
Abstract The influence mechanism of filling pressure gradient on pores of different scales is complex. In this paper, firstly, the nuclear magnetic resonance method is used to measure the spontaneous imbibition water and spontaneous imbibition oil of different scale pores in shale, revealing the differences in the multiscale pore‐fluid interaction mechanism in shale oil reservoirs. On this basis, the oil injection volume of different scale pores in shale is quantified using displacement and two‐dimensional nuclear magnetic resonance measurement technology under different injection pressure conditions, and the injection pressure gradient (start‐up pressure gradient) of different scale pores is clarified. Results show that (a) in non‐aqueous calcite and illite systems, when the oil/pore ratio is 27%, shale oil is filled in the pore throats; when the oil/pore ratio is ≤20%, water channels are formed inside the shale oil. (b) The small, medium, and large pores of block shale are basically saturated during 1.2 MPa pressure differential displacement, with a saturation degree of 95% and an average pressure gradient of 39.2 MPa/m; (c) The small, medium, and large pores of rich‐bedding shale are basically saturated with a pressure difference of 0.5 MPa during displacement, with a saturation degree of 95% and a pressure gradient of 14.2 MPa/m.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".