The geological sequestration of CO₂ in deep saline aquifers is one of the most effective strategies to reduce greenhouse emissions from the stationary point sources of CO₂. However, it is a complex task to quantify the storage capacity of an aquifer as it is a function of various geological characteristics and operational decisions. This study applies physics-based proxy modeling by using multiple machine learning (ML) models to predict the CO₂ trapping scenarios in a deep saline aquifer. A compositional reservoir simulator was used to develop a base case proxy model to simulate the CO₂ trapping mechanisms (i.e., residual, solubility, and mineral trapping) for 275 years following a 25-year CO₂ injection period in a deep saline aquifer. An expansive dataset comprising 19,800 data points was generated by varying several key geological and decision parameters to simulate multiple iterations of the base case model. The dataset was used to develop, train, and validate four robust ML models—multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). We analyzed the sequestered CO₂ using the ML models by residual, solubility, and mineral trapping mechanisms. Based on the statistical accuracy results, with a coefficient of determination (R²) value of over 0.999, both RF and XGB had an excellent predictive ability for the cross-validated dataset. The proposed XGB model has the best CO₂ trapping performance prediction with R² values of 0.99988, 0.99968, and 0.99985 for residual trapping, mineralized trapping, and dissolution trapping mechanisms, respectively. Furthermore, a feature importance analysis for the RF algorithm identified reservoir monitoring time as the most critical feature dictating changes in CO₂ trapping performance, while relative permeability hysteresis, permeability, and porosity of the reservoir were some of the key geological parameters. For XGB, however, the importance of uncertain geologic parameters varied based on different trapping mechanisms. The findings from this study show that the physics-based smart proxy models can be used as a robust predictive tool to estimate the sequestration of CO₂ in deep saline aquifers with similar reservoir characteristics.

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.