Relationships Among Formation Resistivity Factor, Compressional Wave Velocity, and Porosity for Reservoirs Saturated with Multiphase Fluids
Bibliographic record
Abstract
The electric formation resistivity factor (F) and the seismic compressional wave velocity (v p) are powerful parameters in understanding the electric and elastic behavior of porous media and in identifying the type of fluid saturating the pore spaces. The formation resistivity factor is a function of various influences, including pore and grain properties; saturation, salinity, and viscosity of pore water; formation and pore-water resistivities; cation-exchange capacity; and clay content. The compressional wave velocity is a function of bulk (grain and fluid) density; type of saturant and degree of saturation; and various elastic moduli, including bulk (pore, fluid, and grain) compressibility. Both parameters (F and v p) are significantly affected by variations of porosity ( φ ), pressure, and temperature. The three parameters (F, v p, φ) were obtained from well log measurements for complex, heterogeneous, and consolidated shaly sandstone reservoirs, saturated with multiphase fluids, offshore of the eastern coast of Canada. Relationships among the three parameters, having coefficients of correlation ranging from 0.75 to 0.92, were obtained. Both F and v p are correlated inversely to φ and directly to each other.
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 | 0.004 |
| 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 | 0.001 |
| 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".