Seismic modeling of multidimensional heterogeneity scales of Mallik gas hydrate reservoirs, Northwest Territories of Canada
Bibliographic record
Abstract
In hydrate‐bearing sediments, the velocity and attenuation of compressional and shear waves depend primarily on the spatial distribution of hydrates in the pore space of the subsurface lithologies. Recent characterizations of gas hydrate accumulations based on seismic velocity and attenuation generally assume homogeneous sedimentary layers and neglect effects from large‐ and small‐scale heterogeneities of hydrate‐bearing sediments. We present an algorithm, based on stochastic medium theory, to construct heterogeneous multivariable models that mimic heterogeneities of hydrate‐bearing sediments at the level of detail provided by borehole logging data. Using this algorithm, we model some key petrophysical properties of gas hydrates within heterogeneous sediments near the Mallik well site, Northwest Territories, Canada. The modeled density, and P and S wave velocities used in combination with a modified Biot‐Gassmann theory provide a first‐order estimate of the in situ volume of gas hydrate near the Mallik 5L‐38 borehole. Our results suggest a range of 528 to 768 × 106 m3/km2 of natural gas trapped within hydrates, nearly an order of magnitude lower than earlier estimates which did not include effects of small‐scale heterogeneities. Further, the petrophysical models are combined with a 3‐D finite difference modeling algorithm to study seismic attenuation due to scattering and leaky mode propagation. Simulations of a near‐offset vertical seismic profile and cross‐borehole numerical surveys demonstrate that attenuation of seismic energy may not be directly related to the intrinsic attenuation of hydrate‐bearing sediments but, instead, may be largely attributed to scattering from small‐scale heterogeneities and highly attenuate leaky mode propagation of seismic waves through larger‐scale heterogeneities in sediments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".