钻井/完井液

数字振动台监控在深水钻井平台上使用计算机视觉

本文介绍了在深水钻井平台上部署和操作计算机视觉系统的实验，使用远程监控摄像系统实时测量钻屑。

图 1——图像检测示例。
图 1——图像检测示例。
来源：OTC 35585。

本文介绍了在深水钻井平台上部署和运行计算机视觉系统的试验，该系统利用远程监控摄像系统实时测量钻屑。通过自主表征钻屑，该系统旨在提高钻井效率、优化钻进速度并防止封隔事件。图像分析在数字振动筛监控中的成功应用，代表了钻屑回收率测量领域一项充满希望的进步。

问题陈述

找到钻井参数和泥浆特性的最佳组合，以避免钻屑在井筒中过度堆积并带来相关风险，是一项重大挑战。如果较大尺寸的岩石碎片（称为塌方）意外从井壁落入井筒，情况会更加恶化。

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原文链接/JPT
Drilling/completion fluids

Digital Shaker Surveillance Uses Computer Vision on a Deepwater Rig

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.

Fig. 1—Example of image detection in action.
Fig. 1—Example of image detection in action.
Source: OTC 35585.

This paper describes a 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. By autonomously characterizing drilled cuttings, the system aims to enhance drilling efficiency, optimize penetration rates, and prevent packoff events. The successful application of image analysis to digital shaker surveillance represents a promising advancement in cuttings-recovery-rate measurement.

Problem Statement

Finding the optimal combination of drilling parameters and mud characteristics to avoid the excessive accumulation of cuttings in the wellbore and associated risks can be a major challenge. The situation is worsened if rock fragments of larger dimensions (known as cavings) unexpectedly fall into the wellbore from the borehole wall.

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