World First Digital Shaker Surveillance Using Computer Vision Technology on a Deepwater Rig

Abstract This paper describes an experimentation trial deploying and operating a computer vision system on a deepwater rig to measure drilled cuttings in real-time using a remotely monitored camera system. The objective was to demonstrate that autonomous cuttings characterization could provide useful data to improve drilling performance. Several wells, in the Mediterranean Sea and in the Atlantic Ocean, were monitored consecutively over one year. The system used an optical sensor and image analysis software to quantify and qualify cuttings on the rig's shakers during drilling operations. Data acquired at the shakers during different drilling activities showed strong correlation between cuttings load and perturbation in various drilling parameters, leading to improved hole cleaning and wellbore stability assessments. The study also highlighted safety benefits from reducing crew exposure to hazardous areas. By autonomously characterizing drilled cuttings, the system aims to enhance drilling efficiency, optimize penetration rates, and prevent pack-off events. The successful application of image analysis to digital shaker surveillance represents a promising advancement in cuttings recovery rate measurement.