AI-Driven Permanent Cuttings Monitoring Enables Safer and Faster Drilling

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211759, “Safer and Faster Drilling Through AI-Driven Permanent Cuttings Monitoring: An Operator’s Approach,” by Georg Ripperger, SPE, and Jörg Peisker, OMV, and Patrick Oberschmidleitner, Montanuniversität Leoben, et al. The paper has not been peer reviewed. _ The project outlined in the complete paper demonstrates the feasibility of using deep-learning and machine-learning (ML) approaches to introduce camera-based solids monitoring to the drilling industry. Despite a short development time, the project proved that it was possible to recognize cuttings, cavings, and anomalies in the solids output using proprietary ML models and regular off-the-shelf hardware. Computer-Vision Techniques for Cuttings Detection Several technologies, such as 2D vision, stereo vision, structured light, or time-of-flight, can be used to detect objects such as drill cuttings on a shale shaker. Depending on the physics behind them, these techniques can recognize individual objects and their dimensions or generate depth maps by measuring the physical distance between the sensor and a defined point on the image. Single 2D vision is the simplest and most cost-effective approach but has limitations in measuring depth. Such cameras do not require any special sensors and can be directly installed close to the area of interest. The captured 2D images are usually converted into optical arrays and further segmented. The stereo-vision setup uses two cameras placed at a defined distance to one another. Both cameras capture an image, which is then processed by changing the resolution and filtering out the noise. The resulting depth map might require higher processing times for higher resolutions. Structured light makes use of predefined patterns of light to detect objects and requires active illumination of the scene. Structured-light techniques can be classified into single-shot and multishot. The multishot technique can be used for static objects and may result in more-accurate results. The time-of-flight technique requires only one device, which must be equipped with a light emitter (usually infrared) and a receiver. During the procedure, the object is irradiated with photons and the time that elapses until these photons are reflected is measured. For each pixel, the distance is measured and a depth map is generated. One challenge with this technique is that the sensor also responds to sunlight, which requires the isolation of the camera and the shaker to ensure reliability. Methodology The first point of contact for a human observer with the drilled cuttings is at the outlet of the shale shakers. At this point, the cuttings are distributed as evenly as possible and mostly free of mud. For a camera-based system, this poses the perfect opportunity to detect individual cuttings (and cavings, if present). If an algorithm can identify individual cuttings or cavings, further geometric parameters such as size and shape can be analyzed. A training data set of video footage filmed in different conditions is divided into subsets used for training, verification, and testing of ML models. As outlined in Fig. 1, the procedure consists of taking video footage from the shale shaker, followed by the extraction of video frames and preprocessing. The data are then fed into the ML algorithm, where the model is trained. After sufficient training, such a model can detect and analyze cuttings and cavings in real time.