Experiment study of pore structure effects on velocities in synthetic carbonate rocks
Bibliographic record
Abstract
ABSTRACT Carbonate rocks have a complex pore structure, show strong heterogeneity, and have a wide range of velocities that lead to more complicated velocity-porosity relationships compared with sandstones. We designed and prepared 72 carbonate synthetic cores with known pore structures according to the control variate principle. We measured the P- and S-wave velocities of these cores by an ultrasonic pulse transmission method, analyzed the effects of the pore aspect ratio (AR) and pore size d on velocities, and compared the experimental results with predictions of effective medium theories (EMTs). The matrix of our synthetic cores was consolidated mixture of carbonate cuttings and epoxy. We randomly imbedded predesigned penny-shaped silicone disks or expandable polystyrene balls into the matrix during the core preparation process to simulate secondary pores. The experimental results indicated that Han’s empirical linear velocity-porosity relation was a good prediction for cores with only interparticle pores. Secondary pores played an important role in the velocity variation of carbonates. Cores with a larger AR had faster velocities. Different ARs could lead to velocity variations as high as 1227 m/s at a given porosity. When the wavelengths λ were larger than the pore size, cores with larger secondary pores found higher velocities under the same pore shape, pore fluid, and porosity condition. Different pore sizes could contribute to nearly 15% velocity variation at a given porosity. The comparison between our measurements and EMT predictions indicated that for carbonate rocks with a complicated pore structure, the self-consistent model gave more reliable predictions when the secondary pore size was relatively small (λ/d>10) and Kuster and Toksoz formulations as well as the differential effective medium model gave more satisfactory results when the secondary pore size was relatively large (λ/d=5−8, or even smaller).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".