Broadband Electrical Spectra of Hydrate-Bearing Sediments and Saturation Evaluation of Hydrate and Gas: A Cross-Scale Numerical Study
Bibliographic record
Abstract
Hydrate and gas may coexist in natural gas hydrate (NGH) reservoirs. Electrical conductivity has been used for evaluating hydrate saturation, but it is challenging to distinguish between hydrate and gas. To achieve a reliable assessment of the gas resources of NGH reservoirs, it is desirable to develop saturation evaluation models for both gas and hydrate. In this work, the broadband electrical spectra ranging from 10 –2 to 10 6 Hz covering both the polarizations of electrical double layer (EDL) and hydrate were utilized for predicting the saturations. First, cross-scale numerical models were built based on the molecular dynamics (MD) and finite element (FE) methods. The ion diffusion coefficient and mobility from the MD model were transferred to the FE model to obtain the broadband electrical response of the hydrate-bearing sediment (HBS). Second, the saturation evaluation models of hydrate and gas were established based on the Maxwell-Garnett (MG) theory and high-frequency electrical spectra for different hydrate distribution modes (i.e., pore-filling PF, grain-coating GC, and PF-GC mixed modes). It has been demonstrated that the ion diffusion coefficient and mobility at the nanoscale and the broadband electrical spectra of HBS at the microscale can be obtained from the cross-scale numerical models. The ion diffusion coefficient and mobility are influenced by the pore-water salinity, temperature, and clay mineral. In the frequency range of 10 4 –10 6 Hz, the quadrature conductivity is sensitive to the variations of hydrate and gas saturations, providing a physical basis for establishing saturation models. In the range lower than 1 kHz, the low-frequency polarization does not include the contribution of EDL for the GC hydrate cases, while the EDL polarization plays a dominant role for the PF hydrate cases. With the numerical solution as a reference, the relative and root-mean-square errors of model-predicted saturations are located within ±10.00% and lower than 7.00%, respectively.
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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 | 0.000 | 0.000 |
| 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".