Session: 11-03-01 Data Science Applications in Drilling

Submission Number: 157340

Rate of Penetration Optimal Control Considering Drillstring Vibration via Data Analytics 

This paper presents an innovative approach to managing the
Rate of Penetration (ROP) in drilling operations, with a focus
on mitigating the effects of drillstring vibrations and integrating
founder point detection and a rule-based model for ROP adjust-
ment. ROP is a critical parameter in drilling, directly influencing
both operational efficiency and costs. However, maintaining a
high ROP is often hindered by drillstring vibrations, which in-
crease the risk of equipment failure, non-productive time (NPT),
and operational hazards.

To address these challenges, we introduce a vibration cost
term that integrates key metrics such as oscillation magni-
tude, frequency, change rate, entropy, and founder point detec-
tion—allowing for early identification of transitions between dif-
ferent drilling conditions. These metrics enable a comprehensive
assessment of vibration severity, and the founder point detection
further enhances the system’s ability to preemptively adjust pa-
rameters. Additionally, a rule-based model is employed to pro-
vide targeted ROP adjustments based on real-time conditions, en-
suring drilling efficiency and system stability.

Simulation results validate the effectiveness of this ap-
proach, demonstrating its ability to maintain an optimal ROP,
reduce vibrations, and enhance the robustness of the control sys-
tem. The integration of founder point detection and a rule-based
model further enhances the adaptability of the method, making
it a promising solution for improving drilling performance and
reducing operational risk. This methodology is well-suited for
addressing the complexities of modern drilling operations and
offers significant potential for real-world applications.

Presenting Author: Hamed Sahebi Department of Energy and Petroleum Engineering

Presenting Author Biography: Mr. Hamed Sahebi is a researcher at the University of Stavanger's Faculty of Science and Technology, within the Department of Energy and Petroleum Engineering. His work focuses on drilling optimization, wellbore trajectory design, and the application of artificial intelligence in drilling operations. Mr. Sahebi has contributed to the development of AI-aided drilling environments utilizing micro-service systems. His research aims to enhance drilling efficiency and safety through innovative methodologies and technologies.