An intelligent algorithm for identifying dropped blocks in wellbores

Abstract

Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions. The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid, enabling preventive measures to be taken. In this study, an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks. The raw data obtained were preprocessed using methods such as format conversion, down sampling, coordinate transformation, statistical filtering, and clustering. Feature extraction algorithms, including the principal component analysis bounding box method, triangular meshing method, triaxial projection method, local curvature method, and model segmentation projection method, were employed, which resulted in the extraction of 32 feature parameters from the point cloud data. An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field. The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model. An intelligent, fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability. An average accuracy of 93.9 % was achieved. This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.