Early Detection and Estimation of Kick in Managed Pressure Drilling

Drilling in the offshore environment involves high risk, mainly caused by uncertainties in the reservoir conditions. Unplanned events such as the influx of reservoir fluids (i.e., kick) can lead to catastrophic accidents. Therefore, mitigation of kick is extremely crucial to enhance the safety and efficiency of drilling. In the current study, an unscented-Kalman-filter (UKF)-based estimator is used to simultaneously estimate the bit-flow rate and kick in a managed-pressure-drilling (MPD) system. The proposed estimator uses sigma-point transformations to determine the true mean and covariance of the Gaussian random variable and captures the posterior mean and covariance accurately up to the third order (Taylor-series expansion) for any nonlinearity. In the proposed UKF formulation, hidden states and unknown inputs were concatenated to an augmented state vector. The magnitude of the kick is estimated using only available topside measurements. The applied method was validated by estimating the gas-kick magnitude in a laboratory-scale setup and data set from a field operation. The proposed estimation method was found robust for the MPD system under different noisy scenarios.