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Abstract Large-scale geo-sequestration of anthropogenic carbon dioxide (CO2) is one of the most promising methods to mitigate the effects of climate change without significant stress on the current energy infrastructure. However, the successful implementation of CO2 sequestration projects in suitable geological formations, such as deep saline aquifers and depleted hydrocarbon reservoirs, is contingent upon the optimal selection of decision parameters constrained by several key uncertainty parameters. This study performs an in-depth parametric analysis of different CO2 injection scenarios (water-alternating gas, continuous, intermittent) for aquifers with varying petrophysical properties. The petrophysical properties evaluated in this study include aquifer permeability, porosity, relative permeability, critical gas saturation, and others. Based on the extensive data collected from the literature, we generated a large set of simulated data for different operating conditions and geological settings, which is used to formulate a proxy model using different machine learning methods. The injection is run for 25 years with 275 years of post-injection monitoring. The results demonstrated the effectiveness of the machine learning models in predicting the CO2 trapping mechanism with a negligible prediction error while ensuring a low computational time. Each model demonstrated acceptable accuracy (R2 >0.93), with the XGBoost model showing the best accuracy with an R2 value of 0.999, 0.995, and 0.985 for predicting the dissolved, trapped, and mobile phase CO2. Finally, a feature importance analysis is conducted to understand the effect of different petrophysical properties on CO2 trapping mechanisms. The WAG process exhibited a higher CO2 dissolution than the continuous or intermittent CO2 injection process. The porosity and permeability are the most influential features for predicting the fate of the injected CO2. The results from this study show that the data-driven proxy models can be used as a computationally efficient alternative to optimize CO2 sequestration operations in deep saline aquifers effectively. 

We present the first threefold differential measurement for neutral-pion multiplicity ratios produced in semi-inclusive deep-inelastic electron scattering on carbon, iron, and lead nuclei normalized to deuterium from CLAS at Jefferson Lab. We found that the neutral-pion multiplicity ratio is maximally suppressed for the leading hadrons (energy fraction $z\to$1), suppression varying from 25% in carbon up to 75% in lead. An enhancement of the multiplicity ratio at low $z$and high $p_T^2$is observed, suggesting an interconnection between these two variables. This behavior is qualitatively similar to the previous twofold differential measurement of charged pions by the HERMES Collaboration and, recently, by CLAS Collaboration. The largest enhancement was observed at high $p_T^2$for heavier nuclei, namely, iron and lead, while the smallest enhancement was observed for the lightest nucleus, carbon. This behavior suggests a competition between partonic multiple scattering, which causes enhancement, and hadronic inelastic scattering, which causes suppression.