Application of Real-time Video Streaming and Analytics to Breakdown Rig Connection Process
- Chiranth Hegde (The University of Texas at Austin) | Omar Awan (Shell Global Solutions, US, Inc) | Tim Wiemers (Shell Global Solutions, US, Inc)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 30 April - 3 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Offshore Technology Conference
- 1.10 Drilling Equipment, 1.6 Drilling Operations, 1.12.4 Sensor Technology, 1.10 Drilling Equipment, 7.6.6 Artificial Intelligence, 4.2 Pipelines, Flowlines and Risers, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.10.5 Offshore Drilling Units, 4.5 Offshore Facilities and Subsea Systems
- Drilling Connection, Video Analytics, Well Digitilization, Deep Learning
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- 293 since 2007
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Invisible lost time (ILT) has been estimated to contribute significantly to well construction costs. Reduction of drilling connection time can lead to significant savings, especially in offshore drilling projects. The overall time of a drilling connection can easily be measured using conventional sensors on the rig. However, to gain further insight into the source of operational inefficiencies it is beneficial to breakdown the overall drilling connection process into smaller sub-processes and quantify the time spent doing each. Current sensor technology does not allow us to easily measure these sub-processes on the rig. In this paper, we outline a deep learning-based computer vision method to analyze and measure such sub-processes within the overall drilling connection process using a real-time video feed.
Video of the drilling connections is streamed in real-time using a novel IT framework. In a proof of concept exercise, we applied image recognition techniques to enable us to breakdown and classify the sub-processes during the drilling connections. Convolutional neural networks (CNNs) - a deep learning algorithm - are used to analyze these video images frame by frame to identify rig activities.
The workflow includes an analytics pipeline to capture video on the rig, transfer of data to the office, video recognition analysis, tabulation of results, and delivery of this result to a graphical interface for cognitive analytics. We performed experiments wherein CNNs were used on the video images to diagnose rig activities with good accuracy. Incorporating the temporal information using a recurrent network has been shown to further improve analysis accuracy.
The described workflow is a suggested method for automating the detection of inefficiencies on drilling rigs using rig floor video and machine learning. For the demonstration case, we successfully analyzed video captured on an offshore rig and derived high value useful information using machine learning. This video analysis information can be further combined with conventional drilling sensor data to boost accuracy to identify and confirm rig activities and to quantify invisible lost time during each connection. The presented techniques demonstrate the potential of video analytics as a reliable, low-cost means to effectively "micro-analyze" various rig operations with the aim to identify and replicate best composite performance.
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