A Novel Data Assimilation-Based Real-Time State Estimation Method for Gas Influx Profiling During Riser Gas Events

Abstract

Riser gas events during offshore drilling operations are hazardous and challenging to control. Therefore, knowledge of the gas influx sizes and distributions in a marine drilling riser is critical for the selection of riser gas handling methods and the estimation of risks of riser gas unloading. An extended Kalman filter-based data assimilation method is developed and evaluated for the real-time estimation of gas influx rates and void fraction distributions in a riser during riser gas handling. Full-scale experiments were conducted in this study for the evaluation of the proposed data assimilation method. An offshore well, which consists of a marine drilling riser and a wellbore below the subsea blowout preventer, was simulated by a 1572-m-deep experimental well. Real-time measurement data, including surface and downhole pressures, pump rates, and liquid outflow rates, were used to estimate the downhole gas influx rates using the Kalman filter. An online calibrated drift-flux model based on data assimilation is used to estimate the distributions of void fractions in the riser over time. The measurement data from a gas flowmeter and the distributed fiber-optic sensing were used to validate the estimation results, and satisfying performance was seen from the presented method. This study proposed a novel data assimilation-based state estimation method by maximizing the use of measurement data of different types from the available managed pressure drilling systems. This method enables the more accurate estimation and prediction of gas behaviors in a riser and helps to facilitate real-time decision-making during riser gas handling.