Analysis of the unsaturated hydraulic properties of rocks using multiple laboratory methods
Bibliographic record
Abstract
Abstract Proper characterization of the unsaturated hydraulic properties in rocks is significant for predicting fluid flow in soil, hydrogeologic, and petroleum science and engineering problems. In this study, we contributed rigorous analysis of the unsaturated hydraulic properties of three reservoir rock samples (Berea Sandstone, Guelph Dolomite, and Indiana Limestone). An improved version of the standard evaporation method (HYPROP) was developed to cater specifically to rock samples. The improved HYPROP setup enables measurements of local water pressures within rock samples without disturbing the upper portion of the samples. The obtained results were compared with those obtained using the conventional pressure plate method and a state-of-the-art nuclear magnetic resonance (NMR) method. Observed data were analyzed in terms of four different unimodal and bimodal hydraulic functions. The HYPROP data were found to be relatively close to the pressure plate data of two carbonate rocks. The NMR-based data were reasonably consistent with the HYPROP data, with differences likely due in part to the fact that they were obtained using two different 5-cm long plugs taken from the same core. Heterogeneity along the rock cores from which the samples were taken could be a major reason for the observed differences, and hence should be considered in reservoir analyses.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".