Gas
hydrates have the potential to significantly disturb global
climate change and alter subsurface stability, particularly in the
context of production due to their extensive presence and widespread
distribution in marine deposits. The electrical conductivity of hydrate-bearing
sediments (HBS) serves as a crucial parameter for hydrate reservoir
prospection. However, the electrical conductivity of HBS is influenced
not only by hydrate saturation but also by the hydrate distribution
within the pore space. This study presents a numerical approach for
quantifying the relationship between the hydrate volume, distribution,
and conductivity of HBS using pore network modeling (PNM). We use
two distinct hydrate distributions in pores, ideal grain-contacting
and pore-filling. Their electrical conductivities, in relation to
hydrate saturation, were simulated on the pore scale using the finite
element method. Regardless of the hydrate distribution, the electrical
conductivity of the pore network models decreases with increasing
hydrate saturation. At the same saturation, the electrical conductivity
of PNM with grain-contacting hydrates is higher than that of pore-filling
hydrates. While the resistivity index of the hydrate-bearing PNM exhibits
a variation pattern consistent with Archie’s formula, the saturation
exponent is not a fixed value. The experimental samples represent
a closed system where significant local fluid salinity changes would
occur due to hydrate formation, strongly influencing the bulk conductivity.
The numerical simulation results considering the salinity effect confirm
the plausibility of grain-contacting hydrate while challenging the
existence of an ideal pore-filling hydrate when compared to the measured
data as the conductivity associated with such a uniformly distributed
pore-filling hydrate contradicts the experimental measurements. Our
research indicates that the variability of the saturation exponent,
highlighting the complex nature of hydrate distributions within sediments,
calls for refined electrical saturation models to enhance the evaluation
of marine hydrate reservoirs.