Economic efficiency of the introduction of digital twins in the oil and gas industry

Abstract

The article evaluates the economic efficiency of implementing digital twins in oil and gas production and addresses whether integrated digital models create sustained added value across the entire asset lifecycle amid market volatility and tightening environmental requirements; the aim is to develop and validate a methodological approach for calculating the total effect (CAPEX, OPEX, revenue) and the integral financial metrics of investment projects. Materials and methods include analysis of de-identified operational and financial data from five vertically integrated companies for 2018-2023, benchmarking against public reports and reviews, as well as econometric and DCF modeling with calculation of NPV, IRR, DPP, and PI; regression models were used to identify relationships between digital twin integration depth and changes in key KPIs, alongside sensitivity analysis to oil prices, license costs, and inflation. The results show that applying digital twins at the FEED and detailed design stages reduces CAPEX by 6,7-9,5% (maximally on offshore platforms) through clash detection, layout optimization, and fewer reworks; in operations, cumulative OPEX decreases by up to 16,42% by year five, driven primarily by the shift to predictive maintenance and repair (-23,9% from baseline) and lower energy consumption (-11,3%). Production effects are expressed in a 54,7% reduction in unplanned downtime, a 7.6 pp increase in OEE, and a 5,0% rise in annual production without additional capacity. Integrated financial metrics for a typical field (15-year horizon, 12% discount rate) were: NPV $82,45 million, IRR 24,6%, discounted payback period 4,8 years, PI 1,78; stress testing confirmed NPV > 0 at -20% oil price or +30% implementation costs. The discussion emphasizes the synergistic nature of the effect (CAPEX+OPEX+productivity) and the strategic value of digital twins as a tool for enhancing competitiveness, recommending scaling to comprehensive asset models and integration with AI for autonomous control.