论文标题
使用地震数据监测多孔储层中的水量：3D模拟研究
Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study
论文作者
论文摘要
神经网络是一个潜在的框架来估计从地震数据中存储在多孔存储库中的水量。在这项研究中，将人造的地下水储层建模为耦合的porovisCoalastic-VisCo弹性培养基，并使用三维不连续的Galerkin方法与Adams-Bashforth Time Steppping Scheps Scheme相连，并使用三维不连续的Galerkin方法来解决基础波传播问题。波浪问题求解器用于生成基于神经网络的机器学习模型的数据库，以估算水量。在数值示例中，我们研究了一种基于反卷积的方法，除了网络对噪声水平的耐受性外，还将源小波的效果归一化。我们还采用Shapley添加说明方法来获得更深入了解输入数据的哪一部分对水量估计最大的影响。数值结果证明了完全连接的神经网络估计多孔存储库中存储的水量的能力。
A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic-viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water volume. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water volume estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir.
