Real-Time 3D Computer Vision Shape Analysis of Cuttings and Cavings
- Runqi Han (The University of Texas at Austin) | Pradeepkumar Ashok (The University of Texas at Austin) | Mitchell Pryor (The University of Texas at Austin) | Eric van Oort (The University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 1.6 Drilling Operations, 1.10 Drilling Equipment, 6.1 HSSE & Social Responsibility Management, 6.1.5 Human Resources, Competence and Training, 6 Health, Safety, Security, Environment and Social Responsibility, 1.11 Drilling Fluids and Materials
- Computer Vision, Wellbore instability, Machine Learning, Automation, Cuttings Cavings Monitoring
- 3 in the last 30 days
- 257 since 2007
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Excessive cuttings and cavings accumulation due to poor hole cleaning and borehole instability can cause costly stuck pipe incidents. Currently, there is no surface instrument to monitor cuttings volume and detect cavings in real-time. An automated 3D real-time computer vision monitoring system can quantify cuttings return volume, detect cavings presence, and analyze cavings shape. This makes pro-active prevention and mitigation of non-productive time (NPT) caused by poor hole cleaning and wellbore instability possible.
In this paper, we present a real-time computer vision system to measure cuttings properties and detect cavings. The proposed design consists of a 2D high-resolution camera and a 3D profile laser scanner, which collect point cloud/depth data of cuttings/cavings after passing the shale shaker. We apply cutting-edge computer vision algorithms and feature recognition techniques to quantify cuttings volume, detect cavings, and characterize cavings shape. The angularity, flatness, and other geometrical features of cuttings/cavings can be determined from the point cloud 3D data.
A prototype computer vision system was constructed and tested in the lab and test yard to evaluate the system capability to measure cuttings/cavings properties. In a controlled laboratory environment, a sensing algorithm was designed and tested in the presence of drilling fluid. To improve measurement accuracy, both artificial and field cavings were used to simulate realistic scenarios and train a data pool. The system was then validated in a test yard shale shaker testing facility. The accuracy, repeatability, and robustness of the sensors were evaluated against external lighting variances, dust, humidity, etc. The proposed automated cuttings/cavings monitoring system can identify cavings and analyze shape characteristics. By diagnosing potential hazards, the system warns the driller on adverse wellbore conditions and the likelihood of stuck pipe events. This paper proposes and demonstrates a novel 3D depth-sensing system to measure cuttings volume, identify abnormal cavings, and analyze shape. This state-of-art, non-intrusive system evaluates cuttings/cavings quantitatively and delivers algorithms that automate downhole condition monitoring to reduce drilling-related NPT in the field.
|File Size||2 MB||Number of Pages||16|
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