In the last decades, permafrost gas hydrates have beneficiated from an increasing attention among researchers and industries around the world. However, little work has been done on characterizing this resource at the reservoir scale. In this study, we used cutting edge stochastic inversion software and we developed a cascade stochastic Bayesian algorithm to simulate the gas hydrate grade (product of porosity and gas hydrate saturation) on a 3D seismic cube at the Mallik field, in the Mackenzie Delta, Canada. Firstly, the 3D seismic data are stochastically inverted for acoustic impedance leading to multiple high-resolution AI realizations conditioned to the seismic and the well-log data from wells 2L- and 5L-38. Secondly, a petrophysical inversion is performed in a stochastic Bayesian framework using gas hydrate grade logs as hard data, and randomly selected AI scenarios as secondary data. For the later inversion, an in-situ petrophysical relationship linking gas hydrate grades to acoustic impedance is built using upscaled well data. The results are thus multiple 3D gas hydrate grade realizations conditioned to all available data, and reflecting a great part of the model uncertainty. These models allow calculating the total gas volume with its associated uncertainty for the studied region.  


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