Digitalization of the drilling process has led to an increased focus on utilizing sensor measurements topside and downhole in optimization and risk mitigation associated with well control incidents. The objective of this study is to demonstrate how hydraulic models with advanced gas loading features can aid in the automatic interpretation of ASM (Along-String Measurements) data, enhancing the well control potential in automated drilling processes.
A synthetic well control case with ASM pressure data points distributed sparsely along the string is set up. An ensemble of hydraulic model simulations with different influx parameters are run and compared with the ASM data. By considering the simulation results that best explains the measured data, a more fine-grained picture of the downhole situation is obtained, allowing for a more complete understanding of the risk of unwanted consequences. The results may be presented graphically to the drilling team, or may be accessed by control systems for automatic handling.
Based on this work, a methodology for improving automatic risk analysis in drilling operations with ASM is developed. While there has been considerable attention given to the potential of using sensor data recently, it has been less clear exactly how to get the most out of these data. The results of this study show how modeling enhances the data by providing relevant, physics-based information about gas distribution and severity along the entire wellbore. This allows for a detailed picture of the gas loading in the well, demonstrating how the combination of measurements and modelling provides an early kick warning opportunity not possible with measurements alone.
The study contributes to improved automation in the context of gas influx scenarios with ASM. This is especially relevant with regards to digitalization, which involves shifting the interpretation of sensor measurements from humans to computers. The results obtained may be an important step in achieving higher degrees of safety and efficiency in drilling automation.