Well Control Space Out: A Deep-Learning Approach for the Optimization of Drilling Safety Operations

Abstract

As drilling of new oil and gas wells increase to meet energy demands, it is essential to optimize processes to ensure the health and safety of the crew as well as the protection of the environment. Drilling operations represent a dynamic and challenging environment with natural and mechanical factors that need to be closely managed. Well control refers to the technique employed while drilling for balancing the hydrostatic and formation pressures to prevent the influx of water, gas, or hydrocarbons that would ultimately result in an uncontrolled flow to the surface. In the event of a well control incident, the crew must take proper and prompt actions to mitigate the risks and shut-in the well. In this study, we introduce the Well Control Space Out technology, an internet-of-things (IoT) environment that couples cameras and an edge server to implement state-of-the-art deep-learning models for the real-time processing of video images recording the drillstring. The computational models automatically perform object detection to keep track of key drilling rig components. The results from the video analysis are displayed on a dashboard describing the state and steps to follow in a well control incident without the need for any time-consuming, manual calculations. The internet-of-things edge foundation laid in drilling can be seamlessly expanded to other upstream sectors, where time-sensitive, critical decisions can be made in real-time, in the field, closer to operations. Finally, this technology can be seamlessly integrated with the current technologies to develop an automated closed-loop control system.

Keywords

• Automation
• deep-learning
• computer vision
• edge computing
• internet-of-things
• oil and gas drilling