Pore structure and fractal characteristics of ultralow-permeability sandstone reservoirs in the Upper Triassic Yanchang Formation, Ordos Basin
Bibliographic record
Abstract
The complex pore structure and high heterogeneity of ultralow-permeability sandstone reservoirs have a significant effect on reservoir quality evaluation and hydrocarbon resource assessment. We collected 10 reservoir samples from the Upper Triassic Yanchang Formation Chang 8 in the Zhenbei area of the Ordos Basin. We measured the pore size distribution (PSD) and fluid occurrence characteristics of the reservoir by pressure-controlled porosimetry, rate-controlled porosimetry, and nuclear magnetic resonance, and then we analyzed the results via the fractal theory to determine the pore space fractal characteristics. Our analysis indicates that the three major pore types of these reservoirs are residual intergranular pores, dissolution pores, and intercrystalline pores. The pore structure of the ultralow-permeability sandstone reservoirs is highly heterogeneous with pore throats of various scales, and the corresponding fractal characteristics are notably different, exhibiting multifractal features. Compared with macropores and mesopores, micropores are more uniform and regular in terms of their PSD and thus only slightly influence the reservoir quality. The complexity of the throat distribution and whole pore space is attributed to the development of dissolution pores and the content of feldspar minerals. Fractal features depend on the movable fluid pore space and effective pores, whose fractal dimensions reflect the complexity of interconnected pores and correlate well with the porosity and permeability. The development of different types and sizes of pore throats in these ultralow-permeability sandstone reservoirs resulted in the observed pore structure heterogeneity. The difference in mineral composition and content of these reservoirs aggravates the pore structure complexity and affects the reservoir quality evaluation and further oilfield development.
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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 | 0.002 | 0.001 |
| 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 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".