Session: 11-04-01 Drilling Automation

Submission Number: 157307

Enhancing Early Mud Loss Detection in Drilling Operations in Real Time: A Digital Twin Approach Using ASM Data Along Wired Drill Pipe 

To automate drilling operations effectively, reliable real-time downhole data and the ability to extract meaningful information about the bottom hole environment are essential. This study utilizes Along String Measurements (ASM) and the digital twin approach to continuously monitor downhole conditions, enabling the detection of the onset and progression of drilling fluid loss. By integrating a physical model with advanced data analytics techniques, we simulate fluid flow dynamics during drilling operations. The physical models are formulated as differential equations based on mass and momentum conservation, which are discretized along the borehole using the finite element method.

This innovative approach combines the finite element solution with Kalman filtering to estimate downhole parameters in real time, effectively managing the challenges posed by fast and noisy ASM data streams. Using high-frequency ASM data from a well on the Norwegian Continental Shelf (NCS), we predict flow rate and pressure distribution in real time. These estimates are compared to baseline values from surface facilities to identify disturbances early. This method allows for the immediate monitoring and detection of downhole events, even when surface data shows no signs of issues.

The results highlight the significance of downhole measurements and the techniques for extracting actionable insights from them in real-time. However, current industry solutions often face limitations due to oversimplified assumptions about the downhole environment, which can hinder effective monitoring. This research addresses these limitations by utilizing a digital twin framework that intelligently applies ASM data, providing a robust solution for real-time prediction and management of downhole mud loss. Our findings underscore the transformative potential of advanced data analytics in enhancing drilling operations, paving the way for more responsive and effective management of downhole conditions.

Presenting Author: Behzad Elahifar Norwegian University of Science and Technology

Presenting Author Biography: Behzad Elahifar is an associate professor at NTNU (Norwegian University of Science and Technology) and a drilling advisor with close to two decades of experience in the drilling industry (like TDE, Enhanced Drilling, NOV, DigiWellData, etc.) worldwide (North Sea, Barents Sea, Caspian Sea, Middle East, and GOM).