Transforming Gas Plant Performance Through First-Principle Digital Twin Integration: Enhanced Visualization with Intelligent Engineering Deployment at GTPOGT, Turkmenistan

Abstract

As the global energy sector intensifies its focus on efficiency, reliability, and lower carbon operations, industrial digital twins are becoming critical enablers for operational optimization and GHG emissions reduction. Enhanced Visualization with Intelligent Engineering is a new generation, first-principle-based digital twin designed to provide continuous performance insights for complex processing facilities. This paper presents the first deployment of digital twin integration at an international asset, specifically the Gas Treatment Plant and Onshore Gas Terminal (GTPOGT), in Turkmenistan. The deployment demonstrates how process-engineering-led digitalization can translate plant data into operationally relevant insights and measurable value across an integrated gas processing value chain. Digital twin integration was developed using a fully integrated steady-state process simulation software model representing the major gas and condensate processing systems in GTPOGT. Its scope encompasses feed gas and condensate reception, feed gas separation, acid gas removal, gas dehydration, hydrocarbon dewpoint control, propane refrigeration, flash gas compression, and condensate stabilization. The solution integrates process simulation with plant historian data, Plant Information Asset Framework, Plant Information Visualization Platform dashboards, automated data conditioning, model-informed key performance indicators, and virtual measurements. This architecture establishes continuous mass balance visibility and enables equipment-level performance diagnostics using a unified engineering model. Process studies covering October 2024 to June 2025 identified optimization opportunities in the hydrocarbon dewpointing, propane refrigeration, and flash gas compression systems. Simulation results indicated that achieving the intended temperature profile could increase condensate recovery by up to 28%, representing an estimated margin opportunity of USD 10,000 to USD 30,000 per day on the basis of the stated economic assumptions. Post-cleaning observations showed a 2 to 3°C improvement in performance. Flash gas compressor analysis showed that the antisurge valves were frequently open by more than 25% in the first-stage and more than 50% in the second-stage. Simulation estimated that rerouting approximately 2.9 MMscf/D of triethylene glycol (TEG)-treated gas to the compressor suction could reduce dependence on internal gas recycling and improve compressor loading. The value assessment associated compressor trip avoidance and approximately 8 MMscf/D of flaring with a cost avoidance potential of USD 593.75/hour. The digital twin integration deployment demonstrates how a first-principle digital twin can transform gas plant performance management from retrospective data review toward model-informed engineering oversight. By combining process fundamentals, operational data, interactive visualization, and targeted scenario analysis, digital twin integration provides a scalable framework for enhancing visibility, identifying optimization opportunities, improving reliability, and supporting GHG emissions reduction initiatives across gas processing plants.