Bit dullness grading using a handheld device

为了获取地下资源，无论是为了回收碳氢化合物、勘探矿物，还是为了获取地热储量，都必须从地表钻钻孔。在各种行业中使用的一种常见类型的钻头是带有卡宾或钢体的拖曳钻头，以及由聚晶金刚石压块(PDC)制成的刀片。这些钻头经常暴露在地下的恶劣条件下(热、振动、磨石等)，并在钻井过程中经常损坏。钻头的损害是通过由国际钻井承包商协会(IADC)开发和发布的行业标准迟钝分级程序来量化的，然而，这一过程主要是人工进行的，并且具有很高的主观性。这大大降低了数据的有用性，并使其不适合大多数钻井优化工作流，特别是那些寻求使用机器学习算法开发数据驱动模型的工作流。在最新发布的iPhone 12 Max上使用激光雷达传感器，我们建议评估一种移动解决方案的可行性，该解决方案使用激光雷达传感器和摄像头，使用手机快速构建钻头在钻台上的三维模型，并自动对钻头进行评级。

To access resources in the subsurface, be it for the recovery of hydrocarbons, the explorations for minerals, or to access geothermal reserves, boreholes must be drilled from the surface. A common class of drill bit used throughout the various industries are drag bits with carbine or steel bodies and inserts made of polycrystalline diamond compact (PDC). These drill bits are often exposed to harsh conditions in the subsurface (heat, vibration, abrasive rock, etc) and are often damaged during the drilling process. Damage to drill bits is quantified through an industry-standard dull grading procedure, developed and published by the International Association of Drilling Contractors (IADC), however, this process is predominantly manual and highly subjective. This significantly reduces the usefulness of the data and makes it unsuitable for most drilling optimization workflows, particularly those seeking to develop data-driven models using machine learning algorithms. Using the LIDAR sensor on the newly released iPhone 12 Max, we propose to evaluate the feasibility of a mobile solution that uses the LIDAR sensor and camera to quickly construct a three-dimensional model of the drill bit on the rig floor using a cell phone and automatically grade the bit.