完整论文中概述的项目展示了使用深度学习和机器学习 (ML) 方法将基于摄像头的固体监测引入钻井行业的可行性。尽管开发时间很短，但该项目证明可以使用专有的 ML 模型和常规现成硬件来识别固体输出中的切屑、崩落和异常情况。
2D 视觉、立体视觉、结构光或飞行时间等多种技术可用于检测振动筛上的钻屑等物体。根据其背后的物理原理，这些技术可以识别单个物体及其尺寸，或者通过测量传感器与图像上定义点之间的物理距离来生成深度图。
单一 2D 视觉是最简单且最具成本效益的方法，但在测量深度方面存在局限性。此类摄像机不需要任何特殊传感器，可以直接安装在感兴趣区域附近。
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.
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.
