Advancing Drilling Safety and Efficiency: Automated Shale Shaker and Borehole Instability Monitoring with AI and Computer Vision

Evaluating the effectiveness of hole cleaning and ensuring wellbore stability is crucial for preventing unwanted events such as kicks or stuck pipes, with consequent minor or major non-productive time (NPT) that, in severe cases, may lead to well abandonment. Beyond the economic implications, these scenarios pose risks of environmental damage and jeopardise the safety of rig personnel. Shale shakers are the first indicator of emerging borehole cleaning and wellbore stability problems and as such, they are a fundamental component of the drilling rig. Monitoring the shakers and periodically collecting samples are tasks typically assigned to humans. These processes lack continuity in monitoring and rely on subjective interpretation of observed samples, often requiring humans to spend significant time in hazardous zones. Real-time machine learning-based automated detection and interpretation of the shaker screens can substantially improve rig safety by reducing the need for humans to be present in hazardous conditions with fumes and noises when their direct intervention is unnecessary. A novel Computer Vision System has been implemented for automated and uncrewed shale shaker visual monitoring, coupled with Deep Learning (DL) Artificial Intelligence (AI) models. The system produces high-frequency objectively interpreted real-time data that can be recorded and plotted along drilling parameters. It aims to replace the traditional human-based monitoring approach by giving a continuous objective detection of shaker performance and events and enabling safer and more effective drilling operations.