Abstract

Permeability prediction of submarine shallow sediments is significantly important for finding hydrate-bearing sands with commercial potential. Permeability could be more sensitive to hydrate occurrence than traditional velocity parameters, because the rapid decrease of permeability is probably caused by hydrates. The elastic characteristics of the hydrate-bearing sediments differ from the consolidated oil-gas reservoir. In this paper, two elastic limitations that the shallow sediments would be somewhere in between are assumed: 1) sediments are extremely soft; 2) sediments are consolidated. Derivations show that the difference between the bulk modulus in these two assumptions depends on the pore compressibility, and the difference was defined as the pore compressibility indicator (PCI). It has been theoretically proven that PCI is closely related to permeability. Logging data analysis indicates that sediments with high permeability own small PCI and overall large porosity. At the same time, the occurrence of hydrate significantly increases PCI, and even low-saturation hydrate can cause a significant decrease in permeability, so a successful permeability prediction enhances detection and identification of hydrates. Then, the relationship between the seismic reflections and PCI is studied, and a two-state seismic inversion is proposed to obtain PCI. Finally, to predict permeability, a neural network is constructed and well trained using PCI and porosity as inputs and permeability as the output. Error analysis and sensitivity analysis are then carried out. Field applications show that the predicted permeability is consistent with the logging data, and channels for fluid migration such as coarse-grained sands own high permeability, while providing favorable conditions for hydrate formation. In this study, we propose a new seismic approach for permeability prediction in unconsolidated sediments, which is a scientific and effective attempt to predict permeability with seismic data. It can improve the accuracy of hydrate identification and provide basic data for the evaluation of production potential.

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