Fractal Characteristics of Lacustrine Tight Carbonate Nanoscale Reservoirs
Bibliographic record
Abstract
The complexity and heterogeneity of pore structure greatly affect gas-liquid accumulation and transport, and the fractal theory has been proven to be an effective approach for studying nanoscale reservoirs in shale, coal, and tight sandstones. However, researches on fractal characteristics and control mechanisms for the lacustrine tight carbonate have received little attention. Lacustrine tight carbonate samples from the Jurassic Da’anzhai Member in the Sichuan Basin in China were systematically investigated focusing on the fractal characteristics and control mechanisms of storage spaces, minerals, diagenesis, and paleoenvironments. The fractal dimensions can be separated into two different and valid parts including D 1 (2.515–2.785, average 2.652) and D 2 (2.424–2.562, average 2.485), and the correlation between them is negative rather than positive. The average pore diameters exhibit a positive correlation with D 1 and a negative correlation with D 2, and the storage space is positively correlated with D 2 and negatively correlated with D 1 . Terrigenous minerals (e.g., quartz and clay) exhibit a positive correlation with D 2 and a negative correlation with D 1, whereas the effects of authigenic CaCO 3 minerals (e.g., calcite and aragonite) are exactly opposite to those of terrigenous minerals, which is due to the diagenesis and the own characteristics of minerals. CaCO 3 minerals can effectively change pore structures and fill the storage spaces (>5 nm) by cementation, compaction, pressure-solution, recrystallization, and replacement, whereas terrigenous minerals have developed irregular intraparticle pores, interparticle pores, and microcracks. The low salinity and the humid (rainy) paleoclimate are favorable for the formation of terrigenous minerals (elements), whereas they are harmful to the formation of authigenic minerals (elements), which increases D 2 and reduces D 1 . Additionally, paleoredox has a weak influence on the fractal dimensions.
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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 | 0.001 | 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".