Archie's Saturation Exponent for Natural Gas Hydrate in Coarse‐Grained Reservoirs
Bibliographic record
Abstract
Abstract Accurately quantifying the amount of naturally occurring gas hydrate in marine and permafrost environments is important for assessing its resource potential and understanding the role of gas hydrate in the global carbon cycle. Electrical resistivity well logs are often used to calculate gas hydrate saturations, S h , using Archie's equation. Archie's equation, in turn, relies on an empirical saturation parameter, n . Though n = 1.9 has been measured for ice‐bearing sands and is widely used within the hydrate community, it is highly questionable if this n value is appropriate for hydrate‐bearing sands. In this work, we calibrate n for hydrate‐bearing sands from the Canadian permafrost gas hydrate research well, Mallik 5L‐38, by establishing an independent downhole S h profile based on compressional‐wave velocity log data. Using the independently determined S h profile and colocated electrical resistivity and bulk density logs, Archie's saturation equation is solved for n, and uncertainty is tracked throughout the iterative process. In addition to the Mallik 5L‐38 well, we also apply this method to two marine, coarse‐grained reservoirs from the northern Gulf of Mexico Gas Hydrate Joint Industry Project: Walker Ridge 313‐H and Green Canyon 955‐H. All locations yield similar results, each suggesting n ≈ 2.5 ± 0.5. Thus, for the coarse‐grained hydrate bearing ( S h > 0.4) of greatest interest as potential energy resources, we suggest that n = 2.5 ± 0.5 should be applied in Archie's equation for either marine or permafrost gas hydrate settings if independent estimates of n are not available.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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.
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