The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface, with applications ranging from subsurface resource exploration to geothermal, carbon capture and storage, and hydrogen storage. We propose a novel, data-driven approach, named Seis2Rock, that utilizes optimal basis functions learned from well-log information to create a direct link between pre-stack seismic data and so-called band-limited petrophysical reflectivities. Seis2Rock is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic amplitude variation with offset gathers created from measured or rock-physics synthesized elastic well logs. In inference, seismic pre-stack data are first projected into a set of band-limited petrophysical reflectivities using the previously computed basis functions; this is followed by regularized post-stack seismic inversion of the individual properties. In this work, we apply the Seis2Rock methodology to a synthetic data set based on the Smeaheia reservoir model and to the open Volve field data set. Our numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time-lapse, pre-stack seismic data for water saturation changes.

中文翻译：

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Seis2Rock：一种数据驱动的叠前地震数据直接岩石物理反演方法

从地震数据反演岩石物理参数是表征地下特征过程中的一个基本步骤，其应用范围从地下资源勘探到地热、碳捕获和储存以及氢储存。我们提出了一种新颖的数据驱动方法，名为 Seis2Rock，它利用从测井信息中学习到的最佳基函数来创建叠前地震数据和所谓的带限岩石物理反射率之间的直接联系。Seis2Rock由两个阶段组成：训练和推理。在训练期间，通过对一个或多个合成振幅变化执行奇异值分解来识别一组最佳基函数，其中偏移道集是根据测量的或岩石物理合成的弹性测井曲线创建的。在推论中，首先使用先前计算的基函数将地震叠前数据投影到一组带限岩石物理反射率中；接下来是各个属性的规范化叠后地震反演。在这项工作中，我们将 Seis2Rock 方法应用于基于 Smeaheia 油藏模型的合成数据集和开放的 Volve 现场数据集。我们的数值结果揭示了该方法恢复准确孔隙度、页岩含量和含水饱和度模型的能力。最后，所提出的方法应用于水库监测，以反演时移、叠前地震数据以了解水饱和度变化。