Significant Advancements in Drilling Automation Presented at the IADC/SPE International Drilling Conference

I’m excited to announce that I presented two papers at the 2024 IADC/SPE International Drilling Conference in Galveston, Texas. The first paper explores the enhancement of drilling incident prediction by incorporating physics-based information alongside traditional data-driven approaches. We compared the performance of anomaly detection models using real-time signals alone versus those that also integrated physics-based data, such as cuttings-transport and torque-and-drag models. Our findings revealed that hybrid models, which combine these data sources, significantly improve prediction accuracy by reducing false alarms and providing more reliable warnings. This approach also helps in predicting anomalies that were previously thought to occur suddenly, offering valuable lead time for prevention and reducing non-productive time (NPT) in drilling operations.

In the second paper, my coauthors and I presented a new method for the real-time characterization of cuttings and cavings during drilling operations. Our method integrates high-resolution images and 3D data collected via a laser-based sensor to accurately determine the volume, size distribution, and morphology of cuttings. This advanced technique overcomes the limitations of traditional methods, which rely on low-frequency sampling and manual characterization, often resulting in biased and delayed evaluations. By providing precise, real-time assessments even in challenging conditions, our method enhances hole cleaning sufficiency and wellbore stability, which are crucial for preventing NPT.

Both papers highlight significant advancements in drilling technology. The first paper demonstrates the added value of hybrid models in predicting drilling anomalies, while the second introduces a comprehensive real-time system for cuttings characterization. These innovations offer practical solutions to common drilling challenges and provide valuable insights for future operations.

For more information, please visit: SPE-217737-MS (Comparing Drilling Anomaly Prediction by Purely Data-Driven and Hybrid Analysis Methods) and SPE-217736-MS (Automatic Determination of Cuttings and Cavings Properties)

The image on the right is taken from our recent publication, SPE-217736-MS