The Impact of Pore Aspect Ratio on Elastic Parameters: A Physical Modeling Study
Bibliographic record
Abstract
Summary Studying the effects of reservoir properties on elastic parameters can be extremely useful to extract reservoir properties from indirect/ direct measurements, such as full-waveform inversion and well logs. In this study, we investigate the impact of pore aspect ratio on P- and S-wave velocities by building physical models using 3D printing technology. We printed one solid cubic model as a reference model to extract the shear modulus, bulk modulus and density of the sintered powder used in the 3D printing process. In addition, we printed 3 models with embedded ellipsoidal inclusions that have pore aspect ratios of 1 (Model I), 0.33 (Model II), 0.16 (Model III). We used an ultrasonic transmission geometry to measure the P- and S-wave velocities of all printed models and then we compared the measurements with Kuster and Toksöz’s (KT) theoretical model. Both measurements and the KT model show that the pore aspect ratio can significantly impact the elastic parameters, particularly the P-wave velocity. The P-wave velocity is reduced by 10 percent by decreasing pore aspect ratio. The results imply that flatter pores (smaller aspect ratio) reduce the velocity of the medium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".