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Abstract

Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and CO2 storage. In this context seismic methods provide spatially distributed data used to determine gas and fluid migration. A method is developed that allows to determine saturation and reservoir pressure from seismic data, more precisely from rock physical attributes that are velocity, attenuation and density. Two rock physical models based on Hertz-Mindlin-Gassmann and Biot-Gassmann are developed. Both generate poroelastic attributes from pore pressure, gas saturation and other rock-physical parameters. The rock physical models are inverted with deep neural networks to derive e.g. saturation, pore pressure and porosity from rock physical attributes. The method is demonstrated with a 65 m deep unconsolidated high porosity reservoir at the Svelvik ridge, Norway. Tests for the most suitable structure of the neural network are carried out. Saturation and pressure can be meaningfully determined under condition of a gas-free baseline with known pressure and data from an accurate seismic campaign, preferably cross-well seismic. Including seismic attenuation increases the accuracy. The training requires hours, predictions just a few seconds, allowing for rapid interpretation of seismic results.