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Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade‐off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real‐time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO2 saturation. The network is trained on pairs of CO2 label models and multiphysics data so that it can directly image CO2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO2 saturation images. Using realistic CO2 label models and multiphysics data derived from the Kimberlina CO2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single‐physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO2 saturation images. Our results also suggest that the deep learning multiphysics network for the non‐seismic data (i.e., electromagnetic and gravity) can be used as an effective low‐cost monitoring tool in between regular seismic monitoring. [ABSTRACT FROM AUTHOR]

The concept of green carbon science is to promote the goal of achieving carbon neutrality, the importance and urgency of which have been well‐recognized and received worldwide attention. Carbon neutrality has become the focus of research work in many fields, and correspondingly, a growing number of papers and publications have been published over the last few years. However, since carbon neutralization is a real problem that urgently requires technical solutions, the transition from fundamental research to practical applications must be accelerated. A number of research teams are bold in putting things into practice and blazing new trails. This Review summarizes the recent technological developments within the framework of green carbon science, focusing on conducted pilot tests, industrial demonstrations, and commercial applications beyond the laboratory stage. [ABSTRACT FROM AUTHOR]

The concept of green carbon science is to promote the goal of achieving carbon neutrality, the importance and urgency of which have been well‐recognized and received worldwide attention. Carbon neutrality has become the focus of research work in many fields, and correspondingly, a growing number of papers and publications have been published over the last few years. However, since carbon neutralization is a real problem that urgently requires technical solutions, the transition from fundamental research to practical applications must be accelerated. A number of research teams are bold in putting things into practice and blazing new trails. This Review summarizes the recent technological developments within the framework of green carbon science, focusing on conducted pilot tests, industrial demonstrations, and commercial applications beyond the laboratory stage. [ABSTRACT FROM AUTHOR]