Abstract
Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and [Formula: see text] storage. In this context, seismic methods provide spatially distributed data used to determine gas and fluid migration. A method is developed that allows the determination of saturation and reservoir pressure from seismic data, more accurately from the rock-physics attributes of velocity, attenuation, and density. Two rock-physics models based on Hertz-Mindlin-Gassmann and Biot-Gassmann are developed. Both generate poroelastic attributes from pore pressure, gas saturation, and other rock-physics parameters. The rock-physics models are inverted with deep neural networks to derive saturation, pore pressure, and porosity from rock-physics 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 the 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. Although training requires hours, predictions can be made in only a few seconds, allowing for rapid interpretation of seismic results.
Highlights
The determination of gas saturation is a frequent task in hydrocarbon production (Grude et al, 2013; Calvert et al, 2016) and natural gas storage (Priolo et al, 2015) and is highly important for CO2 storage applications (Chadwick et al, 2010; Ivandic et al, 2012)
The underlying equations are identical for all applications because the change in the elastic attributes is physically induced by the higher compressibility and lower density of gas compared to liquid, resulting in a reduced impedance
The data are acquired based on surface seismic acquisition, inverted to obtain elastic attributes, and soft elastic attributes are correlated to the presence of gas
Summary
The determination of gas saturation is a frequent task in hydrocarbon production (Grude et al, 2013; Calvert et al, 2016) and natural gas storage (Priolo et al, 2015) and is highly important for CO2 storage applications (Chadwick et al, 2010; Ivandic et al, 2012). Deep neural networks are used as an inversion tool to determine rock-physics properties based on elastic attributes. The training data set comprising the rock-physics parameters and resultant poroelastic attributes is fed through a sequence of increasingly deep neural networks. MR55 pressure variation effects on the poroelastic attributes are typically not included in the rock-physics models, they are introduced into the appropriate formulations (Avseth et al, 2010; Lang and Grana, 2019). The current paper aims at methodological progress on two fields: first, the application of appropriate deep neural networks for seismic inversion; and second, the formulation and application of appropriate rock-physics models to distinguish pressure- and saturation-induced changes in seismic attributes. According to Mavko and Mukerji (1998), the effective pressure Peff is the overburden pressure Pover minus the pore pressure Pp: Peff 1⁄4 Pover − Pp:
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