Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented-Kalman-Filter Technique

Abstract

Summary To manage the annular pressure profile during managed-pressure drilling (MPD) operations, simulations performed with advanced computer models are needed. To obtain a high degree of accuracy in these simulations, it is crucial that all parameters describing the system are as correct as possible. A new methodology for real-time updating of key parameters in a well-flow model by taking into account real-time measurements, including measuring uncertainty, is presented. Key model parameters are tuned using a recently developed estimation technique based on the traditional Kalman filter. The presented methodology leads to a more-accurate prediction of well-flow scenarios. Although the present study is motivated by applications in MPD, the idea of tuning model parameters should be of great importance in a wide area of applications. The performance of the filter is studied, using both synthetic data and real measurements from a North Sea high-pressure/high-temperature (HP/HT) drilling operation. Benefits by this approach are seen in more-accurate downhole-pressure predictions, which are of major importance for safety and economic reasons during MPD operations.