Basalt is prevalent in the Earth’s crust and makes up about 90% of all volcanic rocks. The earth is warming at an alarming rate, and there is a search for a long-term solution to this problem. Geologic carbon storage in basalt offers an effective and durable solution for carbon dioxide sequestration. Basaltic rocks are widely used for road and building construction and insulation, soil amendment, and in carbon storage. There is a need to understand the parameters that affect this process in order to achieve efficient carbon mineralization. This review systematically analyzes peer-reviewed studies and project reports published over the past two decades to assess the mechanisms, effectiveness, and challenges of carbon mineralization in basaltic formations. Key factors such as mineral composition, pH, temperature and pressure are evaluated for their impact on mineral dissolution and carbonate precipitation kinetics. The presence of olivine and basaltic glass also accelerates cation release and carbonation rates. The review includes case studies from major field projects (e.g., CarbFix and Wallula) and laboratory experiments to illustrate how mineralization performs in different geological environments. It is essential to maximize mineralization kinetics while ensuring the formation of stable carbonate phases in order to achieve efficient and permanent carbon dioxide storage in basaltic rock.

Understanding fault reactivation mechanisms during subsurface fluid injection is critical for safe geological carbon sequestration operations. This laboratory study investigates the hydro-mechanical-acoustic behavior of pre-existing faults in Entrada sandstone under triaxial compression conditions representative of the San Juan Basin. We conducted experiments on saw cut on an Entrada sandstone specimens with 60° fault orientations under 10 MPa confining pressure, employing synchronized acoustic emission (AE) monitoring to capture micro seismic activity during loading. Results revealed five distinct behavioral regimes: elastic loading, microcrack initiation, pre failure acceleration, fault slip, and post-failure relaxation. Peak differential stresses ranged from 100.9 to 105.5 MPa, with apparent elastic moduli of 8.38–13.55 GPa. Assuming zero cohesion (due to the saw‑cut interface), the calculated apparent friction angles are 51.13°–51.47°, indicating critically stressed fault conditions. High energy AE bursts (amplitude >100) correlated strongly with the transition from stable to unstable slip, with maximum energy release occurring during fault activation at 225–245 seconds. The zero-cohesion, high friction behavior suggests that pre-existing faults in sedimentary basins may be highly susceptible to reactivation under small pore pressure perturbations induced by CO2 injection. These findings provide essential constraints for developing risk-informed injection protocols and establishing safe operational limits for carbon sequestration projects in fault bearing formations.

Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate-relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics-informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data-driven machine learning, and scientific machine learning within a target-specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one-way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics-informed neural networks, neural operators, and reduced-order models. The synthesis herein identifies the strongest evidence base for pressure and deformation modeling and seismic signal processing, for which repeated field or operational applications and task-specific evaluation are available. By contrast, prospective fault slip and seismicity forecasting remain at a limited evidence level because of uncertain in situ stress, fault connectivity, CO₂-conditioned friction, monitoring detection limits, model discrepancy, and scarce cross-site validation. We propose task-appropriate metrics, explicit method maturity criteria, a validation ladder, and a staged, human-supervised digital twin roadmap. Machine learning and physics-informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty-calibrated prospective evidence rather than algorithm novelty.

Carbon capture, utilization, and storage (CCUS) supply chains depend on injection nodes whose loss of capacity can propagate upstream as curtailment and network disruption. This design science study develops a digital twin ready predictive risk artifact that converts subsurface measurements into governed states rather than treating model probability as automatic authority. Eighteen slip events from two rough and one smooth fault were represented by seven mechanics aligned variables. Source domain leave one event out probabilities were mapped to Watch (60 s), Mitigate (30 s), and Hold (10 s) states using source calibrated negative exposure quantiles and persistence rules. A cross fitted Mahalanobis support gate identified states outside source experience. Rough to smooth Watch and Mitigate policies intercepted all six events with median warnings of 16.5 and 3.5 s and no pre horizon intervention burden. Smooth to rough policies also intercepted all twelve events but incurred 22.9 and 42.2 s/event of burden. At the 99th percentile support cutoff, support eligible coverage within 30 s was 91.7% versus 36.4% in reverse, and within 10 s it was 75.0% versus 11.7%. These specimen limited results support an Industry 5.0 architecture that separates predictive risk, domain validity, human authority, and physics based fallback.

Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data driven machine learning, and scientific machine learning within a target specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics informed neural networks, neural operators, and reduced order models. The synthesis shows that pressure and deformation modeling and seismic signal processing are comparatively mature, whereas prospective fault slip and seismicity forecasting remain limited by uncertain in situ stress, fault connectivity, CO₂ conditioned friction, monitoring detection limits, model discrepancy, and scarce cross site validation. We propose task appropriate metrics, an explicit validation ladder, and a staged, human supervised digital twin roadmap. Machine learning and physics informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty calibrated prospective evidence rather than algorithm novelty.