Insight into the oil–water seepage mechanism based on shale stress deformation: A numerical simulation approach
Bibliographic record
Abstract
In shale reservoirs, organic matter (OM) and inorganic matter exhibit different deformation behaviors under stress. Their heterogeneous distribution leads to complex oil–water flow during the fracturing production process. In this study, a conceptual shale model with varying total organic matter content (TOC) is developed. A novel aspect is the consideration of shale stress deformation coupled with fluid flow, based on which the oil–water seepage mechanism is investigated. Simulation results demonstrate that the high TOC matrix swept by water during the soaking stage can be effectively mobilized, and the flow rate of oil briefly increases. During the soaking stage, pore pressure in the shale matrix increases. The fluid-induced stress on OM leads to deformation, which expands additional oil–water flow channels and creates favorable conditions for fluid exchange between isolated or small pores within the matrix. The elastic energy stored in deformed OM is released during production, which provides energy supplement for the flow of shale oil and helps to maintain a higher oil production in the later stage. Additionally, the influences of Young's modulus, shale permeability, fracture spacing, and matrix distribution on oil recovery efficiency are analyzed. The results are expected to provide a deeper understanding of the oil–water seepage mechanism and clarify the favorable conditions for hydraulic fracturing exploitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".