Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study

TL;DR

This study develops a neural network framework to estimate water volume in porous reservoirs from seismic data, utilizing 3D simulations and advanced wave modeling techniques.

Contribution

It introduces a coupled poroviscoelastic-viscoelastic model and a 3D discontinuous Galerkin solver to generate training data for neural network-based water volume estimation.

Findings

Neural network accurately estimates water volume from seismic data.

Deconvolution improves robustness against source wavelet effects.

SHAP analysis reveals key input features influencing estimates.

Abstract

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.