Zheng Zhou

CO2 and Brine Leakage Detection Using Multi-Physics-Informed Convolutional Neural Networks

Zheng Zhou, Youzuo Lin, Zhongping Zhang, Zan Wang, Robert Dilmore and George Guthrie

Electrical Engineering Department at State university of New York at Buffalo, Los Alamos National Laboratory, and National Energy Technology Laboratory, United States Department of Energy, Pittsburgh, PA 15236.

In carbon capture and sequestration, it is crucial to build effective monitoring techniques to detect both brine and CO2 leakage from legacy wells into underground sources of drinking water. The CO2 and brine leakage detection methods rely on geophysical observations from different physical domains. Most of the current detection methods are built on physical models, and the leakage mass of CO2 and brine are detected separately. However, those physics-driven methods can be computationally demanding and yields low detection accuracy. In this paper, we developed a novel end-to-end data-driven detection method, called multi-physics-informed convolutional neural network (Multi-physics CNN), which directly learns a mapping relationship between physical measurements and leakage mass. Our Multi-physical CNN takes simulated reflection seismic and pressure data as inputs, and captures different patterns in leakage process. In particular, we capture two types of multi-physical features from seismic and pressure data, respectively. With those features, we can further detect the CO2 and brine leakage mass, simultaneously. We evaluate our novel method for CO2 and brine leakage mass detection task on simulated multi-physical datasets generated using Kimberlina 1.2 model. Our results show that our Multi-physics CNN yields promising results in detecting both leakage mass of CO2 and brine.