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Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers

A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. As opposed to the traditional, time-consuming video-analytics method, the proposed model can implement a real-time classification and achieve remarkable accuracy. The approach is composed of three modules. Compared with results manually labeled by engineers, the model can achieve highly accurate results in real time without dropping frames.

A complete work flow already exists to guide the maintenance and cleaning of the borehole for many oil and gas companies. Brandt VSM 300 Secondary Screen

Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers

The Journal of Petroleum Technology, the Society of Petroleum Engineers’ flagship magazine, presents authoritative briefs and features on technology advancements in exploration and production, oil and gas industry issues, and news about SPE and its members.

Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers

For Nov Brandt Vsm300 Screen ISSN: 1944-978X (Online) ISSN: 0149-2136 (Print)