Economics of carbon capture and storage projects based on automated injection control and monitoring and their contribution to sustainable use of oil and gas territories

Abstract

The study analyzes the economics of carbon capture and storage (CCS) projects enhanced by automated injection control and continuous monitoring, and assesses their contribution to the sustainable use of oil and gas territories. The goal is to quantitatively demonstrate how the integration of fiber-optic DTS/DAS systems, seabed seismic stations, and AI analytics changes the structure of CAPEX/OPEX, financial metrics, and risk profiles compared to traditional periodic control schemes. Materials and methods: a systematic review of more than 150 sources, analysis of open data from global projects (Sleipner, Quest, Gorgon), a parametric DCF model of two scenarios, probabilistic Monte Carlo calculations, and comparison of LCOA, NPV, IRR, PBP, and risk matrices. Results: with increased investments in monitoring, total CAPEX decreases by 8–9% due to optimization of drilling and infrastructure; annual OPEX decreases by 35–40%, including MRV (−68%), maintenance and repair (−43%), and energy costs (−11%). Integral indicators improve: NPV more than doubles, IRR increases by 5–6 percentage points, payback period shortens by about 3–4 years, and LCOA decreases by about 25%. The probability of critical events drops significantly: caprock integrity breaches, wellbore leakages, and induced seismicity are reduced by 58–92% annually, which lowers insurance premiums and regulatory risks. Discussion: automation serves as a source of synergistic savings, redirecting costs from labor intensive field operations to intelligent assets that enhance geomechanical awareness, stability of injection regimes, and storability management. Conclusions: the transition to automated CCS forms a more predictable and investment-attractive model with a lower break-even point and high resilience to carbon price volatility, while ensuring environmental safety, social acceptability, and a tangible contribution to decarbonization goals; the proposed methodology is applicable for feasibility studies of new projects and setting up government support measures. The model assumes a capacity of 1 million tonnes of CO2 per year at a carbon credit price of USD 80 per tonne and a 25-year horizon; for the automated scenario, total CAPEX amounts to USD 277.5 million versus USD 305.0 million in the traditional case, and OPEX — 12.21 versus 19.49 USD/t. Financial results: NPV USD 365.81 million versus 158.27 million; IRR 16.54% versus 11.28%; PBP 9.1 versus 12.4 years; LCOA 51.72 versus 68.34 USD/t.