Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and ${\mathrm{CO}}_2$ 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.

中文翻译：

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深度学习用于压力和饱和度判别的多孔弹性岩石物理模型

确定饱和度和孔隙压力与碳氢化合物生产以及天然气和天然气相关。 ${\mathrm{一氧化碳}}_2$存储。在这种情况下，地震方法提供了用于确定气体和流体迁移的空间分布数据。开发了一种方法，可以从地震数据中更准确地从速度，衰减和密度的岩石物理属性确定饱和度和储层压力。基于Hertz-Mindlin-Gassmann和Biot-Gassmann的两个岩石物理模型被开发出来。两者都根据孔隙压力，气体饱和度和其他岩石物理参数生成孔隙弹性属性。用深层神经网络将岩石物理模型反演，以从岩石物理属性中得出饱和度，孔隙压力和孔隙度。挪威Svelvik山脊上一个65 m深的非固结高孔隙度储层证明了该方法。对最合适的神经网络结构进行了测试。饱和度和压力可以在无气基线的情况下，利用已知压力和来自精确地震活动（最好是井间地震）的数据来有意义地确定。包括地震衰减可以提高准确性。尽管训练需要几个小时，但预测只能在几秒钟内完成，从而可以快速解释地震结果。