AI-Driven Identification and Mitigation of Drill String Vibrations in Heterogeneous Lithologies

Abstract Drillstring vibrations continue to represent a major technical and economic challenge in drilling operations, leading to mechanical failures, poor borehole quality, and reduced penetration rates. This work introduces two artificial intelligence (AI) -based methodologies designed to identify and mitigate these vibrations in real time. By integrating multiple surface and downhole parameters, the proposed approach enhances prediction accuracy and provides actionable insights to improve drilling performance and operational efficiency in heterogeneous formations. Traditional vibration identification relies on downhole sensors or numerical modeling of drillstring dynamics, both of which are limited in complex lithologies. To overcome these challenges, a hybrid framework combining artificial neural networks (ANNs) with global Galerkin theory was developed to estimate vibration intensity using only surface sensor data. ANNs were trained using both downhole and surface measurements to approximate nonlinear dynamic responses. For mitigation, a clustering-based model was implemented to define confidence zones for rotary speed and weight on bit (WOB), minimizing harmful vibration modes while maintaining drilling efficiency. The proposed methodologies were validated in two challenging lithologies: a highly heterogeneous conglomerate and an interbedded shale-sandstone formation. Conventional numerical models struggle to represent bit-formation-string-wellbore interactions under such conditions. In contrast, the ANN-based model accurately captured nonlinear system dynamics, achieving rapid and reliable vibration predictions. The mitigation model successfully identified optimal operational envelopes, resulting in more than a 40% reduction in vibration intensity and more than a 30% increase in rate of penetration (ROP) in one field application. These improvements directly translated into lower mechanical stress, reduced bit wear, improved borehole stability, and extended tool life. The models demonstrated high computational efficiency, making them suitable for integration into existing drilling control platforms. Overall, the AI-based framework offers a practical, data-driven solution for real-time vibration monitoring and optimization, bridging the gap between physics-based modeling and field application. This study presents one of the first successful integrations of AI and Galerkin-based modeling for real-time drilling dynamics analysis. The methodology enables accurate vibration estimation using only surface data, significantly reducing dependence on downhole tools. Its modular design allows easy integration with autonomous drilling control systems, representing a step toward intelligent, self-optimizing drilling operations, especially valuable in heterogeneous and unpredictable subsurface environments.