Automated Methods for Predicting Wellbore Washouts in Tight Carbonate Rock Based on Drilling Data

Abstract The objective of the paper is to develop automated methods for predicting wellbore washouts in tight carbonate formations using high-resolution caliper logs and drilling data. By linking drilling activity patterns with borehole enlargements, the research aims to enhance drilling efficiency and support sustainable drilling practices. This research utilizes high-resolution caliper logs from Logging-While Drilling (LWD) dual-imaging technology across extended lateral sections (above 10,000 ft) in tight carbonate formation. Data from multiple wells were analyzed, focusing on key drilling parameters like rate of penetration (ROP), and weight-on-bit (WOB). The methodology involves linking high-frequency drilling data to borehole shapes using 180-sector resolution caliper logs. The severity of the impact of different drilling activities on wellbore shapes is estimated, and the risk levels associated with each activity are ranked. Additionally, a predictive model was developed to estimate wellbore size by incorporating activity-based risk levels to closely match the actual wellbore diameter. The advanced analysis capabilities significantly improved the understanding of drilling impacts on wellbore conditions, ensuring better well placement and minimizing hydrocarbon loss due to wellbore instability. The high-resolution, real-time data provided insights into drilling practices, aiding in future Field Development Planning (FDP) and reducing the environmental footprint associated with drilling risks. Furthermore, the comprehensive view of drilling impacts supported enhanced completion strategies and optimized production, contributing to overall operational efficiency. The research introduces an innovative approach to predicting wellbore washouts by integrating advanced drilling data with high-resolution caliper logs. This innovative approach is expected to lead to continuous improvement and broader adoption of environmentally responsible drilling practices. By increasing the visibility of environmental benefits and operational efficiencies, this research supports the adoption of advanced digital automation.