Estimates of gas hydrate saturation from conventional and triaxial induction-resistivity measurements, Aurora/JOGMEC/NRCan Mallik 2L-38 gas hydrate production research well
Bibliographic record
Abstract
In the winter of 2007, Japan Oil, Gas and Metals National Corporation, Natural Resources Canada, and Aurora College/Aurora Research Institute acquired induction, laterolog, and triaxial induction-resistivity measurements over the gas-hydrate-bearing formations in the Aurora/JOGMEC/NRCan Mallik 2L-38 gas hydrate production research well. The most common well-log analysis techniques for computing saturation in gas-hydrate-saturated intervals are the Archie-based resistivity approach and the porosity deficit between the formation density and magnetic resonance. The two approaches have been considered to give similar results in gas-hydrate-saturated reservoirs when the input Archie parameters, such as Rw, m, and n, are well known. However, conductive shale layers associated with the presence of thin sand-shale laminations have a profound effect on resistivity logs and can cause significantly lower readings than those of the high-resistivity sand layers. This is known as resistivity anisotropy. If resistivity anisotropy is not considered, the Archie-based resistivity approach can lead to pessimistic water-saturation computations.
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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 | 0.001 | 0.001 |
| 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".