Digital twin system for deepwater well construction: Enhancing operational efficiency and safety

Abstract

Deepwater oil and gas resources are vital to global energy supply, yet their development often faces challenges such as prolonged construction cycles, low efficiency, high emissions, and operational failures. To tackle these challenges, this study analyzes how operational and geotechnical factors affect conductor installation time and bearing capacity, during jetting and waiting stages. The intelligent optimization algorithm integrating MT-FCNN, LSTM, and PSO was proposed. A digital twin system for deepwater well construction is established for the first time, employing a modularly coupled architecture to integrate installation and post-installation processes. Validation results show that the DWC system exhibits strong performance in predicting jetting flow rates and controlling ROP during conductor installation. The model achieved a coefficient of determination R² above 0.95 and MAPE below 6 %. In beach-scale experiments, all measured values of time-varying bearing capacity fell within the 95 % confidence interval of the DWC system predictions, demonstrating its high accuracy. Finally, the case study was conducted to analyze the impact of flow rate and waiting time on construction parameters and operational decisions. The findings provide a solid theoretical and technical foundation for intelligent decision-making and enhanced reliability in deepwater well construction.