Rig State Classification Using Class-Specific Attribute-Weighted Pseudo-Dynamic Bayes for Invisible Lost Time Evaluation

Oil and gas drilling is a capital-intensive industry where minimizing costs is paramount. Invisible Lost Time (ILT) during drilling often causes delays and escalates expenses. Accurate rig state classification is critical for quantifying ILT and improving drilling efficiency. In this study, we introduce a novel rig state classification model based on the Class-Specific Attribute Weighted Pseudo-Dynamic Bayes (CAWPDB) algorithm. Unlike existing machine learning methods, which often underutilize domain knowledge, our approach seamlessly integrates drilling expertise by constructing a pseudo-dynamic Bayesian model informed by an expert-derived state transition matrix. To overcome the limitations of the attribute conditional-independence assumption and boost classification performance, we incorporate a class-specific attribute-weighting mechanism. Experimental results from two wells demonstrate that the model achieves a 99.985% accuracy in identifying rig states from surface sensor data, with outstanding performance even for low-frequency rig states. This research enhances ILT visualization and provides valuable insights for optimizing drilling operations and reducing costs.