A hybrid partial least squares regression-based real time pore pressure estimation method for complex geological drilling process

Accurate real-time estimation of pore pressure is essential for the geomechanical analysis of wellbore stability. Conventional empirical methods may find it difficult to capture pore pressure trends, especially in the complex geological environments. In this study, a data-driven pore pressure estimation method is developed on the basis of hybrid partial least squares regression. This method, which combines empirical methods, comprised three stages: data preprocessing, depth series segmentation, and model establishment and switching. First, concerning the existence of outliers and noises, an outlier detection and wavelet filtering algorithm are introduced to obtain reliable model parameters. Additionally, Pearson correlation-analysis is employed to determine strongly correlated attributes with pore pressure in the data preprocessing stage. Afterward, an online principal component analysis similarity method is proposed for depth series segmentation, considering the varying drilling depth. Finally, a real-time data-driven pore pressure estimation model that integrates conventional empirical methods is established on the basis of partial least squares regression, and a model switching strategy is further developed and will be activated when performance deteriorates. The proposed method can be applied to a wide range of formations, and a real case study is conducted using actual data from a drilling site in Utah. The mean absolute error and root mean square error of the proposed method achieve 0.5128 and 0.8056 in the online condition, and achieved 1.4592 and 2.0100 in the offline condition, which are at least 45% less than those of other nine well-known methods. The results indicate the superior performance of our method on this well.