Trusting the Invisible: Cyber-Resilient Geophysical Verification of CO₂ Storage Integrity in Carbon Capture and Storage (CCS) Systems

Abstract

Carbon Capture and Storage (CCS) depends on geophysical monitoring to verify subsurface CO₂ containment, yet increasing reliance on digitized sensors, automated interpretation, and networked data transmission exposes monitoring systems to cyber manipulation that can compromise storage integrity assessments. This study presents a cyber-resilient geophysical verification framework that integrates petrophysical constraints with cybersecurity-inspired anomaly detection to ensure the physical credibility of CCS monitoring data. Time-lapse seismic, electrical resistivity, and pressure responses were simulated for heterogeneous CCS reservoirs characterized by porosity values of 15–35%, permeability ranges of 50–800 mD, reservoir pressures of 8–25 MPa, and supercritical CO₂ saturations between 0 and 60%. Controlled cyberattack scenarios, including data spoofing, signal replay, and systematic sensor bias with perturbation magnitudes of 5–30%, were introduced into the monitoring data streams. Results indicate that conventional interpretation workflows incorrectly classified approximately 27% of compromised datasets as physically plausible CO₂ plume evolution, whereas the proposed framework reduced false storage integrity confirmation to less than 6% by enforcing petrophysical plausibility limits. Seismic velocity anomalies exceeding 4% without corresponding saturation-driven rock physics responses and resistivity deviations greater than 18% from Archie-based predictions were identified as reliable indicators of cyber-induced data corruption. The results demonstrate that embedding petrophysical laws as intrinsic validation constraints significantly enhances the robustness of CCS monitoring, enabling simultaneous detection of subsurface leakage and digital manipulation, and providing a defensible pathway for secure, transparent, and regulator-ready verification of long-term CO₂ storage performance.