Intelligent control system towards autonomous drilling in a subsea drilling platform
K.W. van de Vrie (TU Delft - Mechanical Engineering)
L. Peternel – Mentor (TU Delft - Mechanical Engineering)
Peter Looijen – Mentor
Javier Alonso-Mora – Graduation committee member (TU Delft - Mechanical Engineering)
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Abstract
Offshore geotechnical exploration presents unique challenges due to harsh environments, limited sensor data, and the need for high-fidelity soil characterization. This has led to the deployment of automated solutions, such as subsea platforms, that support exploration activities. These robotic systems are often coupled with intelligent systems capable of performing tasks like drilling autonomously. This work focuses on developing an intelligent advisory system for the Blue Dragon subsea platform to enable autonomous drilling operations. To address the challenges presented by this unique application, the research explores multi-task learning to enhance timeseries forecasting models that predict optimal drilling parameters including thrust, torque, and rotation speed.
Four model architectures were evaluated: a baseline forecasting model, a forecasting model enriched with a separately trained lithology classifier, a jointly trained classifier-forecaster system, and an unsupervised embedding extractor approach. These models were tested on both synthetic vehicle dynamics data and real-world drilling data from Blue Dragon® operations. While the synthetic data demonstrated theoretical soundness of the approaches, evaluation on the drilling dataset revealed significant limitations due to insufficient data quality and quantity, resulting in severe overfitting that prevented conclusive model validation.
The results indicate that although lithology-informed forecasting shows theoretical promise, current data constraints prevent robust generalization to operational conditions. Future work should prioritize acquiring production-grade datasets and developing refined data preprocessing techniques to better isolate drilling-relevant signals. This research establishes foundational methodology for safe, efficient, and adaptive automation in offshore drilling, contributing to the broader objective of autonomous geotechnical exploration.