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.

在海上环境中进行钻井涉及高风险，这主要是由储层条件的不确定性引起的。计划外事件（例如，储层流体涌入（即涌动））可能导致灾难性事故。因此，减震对提高钻井的安全性和效率至关重要。在当前的研究中，基于无味卡尔曼滤波器（UKF）的估计器用于同时估计位流速率和受控压力钻井（MPD）系统中的突跳。拟议的估计器使用sigma-point变换来确定高斯随机变量的真实均值和协方差，并针对任何非线性准确地捕获直至三阶（泰勒级数展开）的后均值和协方差。在拟议的UKF公式中，隐藏状态和未知输入被连接到增强状态向量。仅使用可用的顶部测量值来估计踢的幅度。通过估计实验室规模设置中的气体踢幅度和现场操作的数据集来验证所应用的方法。发现所提出的估计方法对于在不同噪声情况下的MPD系统是鲁棒的。