Enhancing carbon sequestration: Innovative models for wettability dynamics in CO2-brine-mineral systems

This study investigates the application of machine learning techniques-specifically convolutional neural networks, multilayer perceptrons and cascaded forward neural networks -to understand the wettability of the CO2/ brine/rock system, a critical factor in carbon dioxide (CO2) capture, utilization, and storage in deep saline aquifers. Understanding wettability is essential for improving the efficacy of CO2 storage. The study incorporates variables such as salinity, mineral types, measurement methods, pressure, and temperature into the machine learning models. Using a dataset of 876 samples from existing literature, the proposed models were trained and optimized using the Adam optimizer, Levenberg-Marquardt algorithm, and particle swarm optimization respectively. The performance of these models was evaluated through plot analysis, statistical indicators, and the Taylor diagram, demonstrating a high level of accuracy compared to experimental data. The specifically convolutional neural networks model showed exceptional accuracy in predicting CO2 wettability in brine, with a root mean square error of 0.9612 and coefficient of determination value of 0.9982. The minimal presence of outliers in the specifically convolutional neural networks model further confirms its robustness. This research highlights the effectiveness of deep learning in modeling complex wettability behaviors in CO2brine-mineral systems, offering substantial insights for enhancing carbon dioxide (CO2) capture, utilization, and storage strategies. The novelty of this work lies in its comprehensive integration of multiple variables and the use of advanced machine learning optimization techniques, going beyond previous efforts by achieving higher predictive accuracy and providing a more detailed understanding of wettability dynamics.