Poisson’s Ration, Deep Resistivity and Water Saturation Relationships for Shaly Sand Reservoir, SE Sirt, Murzuq and Gadames Basins, Libya (Case study)
Bibliographic record
Abstract
It is important to obtain relationships between the physical quantities, overburden and reservoir composition and fluid type. This is significant in the sense that if one property, e.g., electrical resistivity, can be more easily measured than Poisson’s ratio (PR). Therefore, later parameter can be estimated and defined against resistivity log data. In fact, these relations constructed by several wells data have been taken for each studied productive reservoir from different oil fields at different sedimentary basin, in Libya. However, comparison between calculated PR, measured deep resistivity and calculated water saturation content are using to a certain extent justification of reservoir conditions (tight zone). These cross relations throw up the increase of PR range at low values of deep resistivity values and water saturation degrees, which present like a hyperbolic curves formed a two parts. The stable trend with constant PR values in hydrocarbon and clean intervals depths within a first part, while a shaly depth intervals act as a second part of the hyperbolic shape, which shows a scatter or cluster points indicates of water intervals and not a tight zone. Therefore, estimation of PR in shaly intervals in such these reservoirs are ranging above 0.3 up to 0.4.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".