井眼清洁不彻底和井筒几何形状导致的卡钻是油气钻井工程中的主要问题。现有的研究主要集中于机器学习和摩擦计算来分析卡钻趋势。然而，这些方法往往会忽略导致卡钻的关键机理参数，导致准确性较低。针对这些问题，本文开发了一种下钻作业中钻柱摩擦系数的预测方法，可用于卡钻预警。
介绍
目前，卡管预测方法主要有两类，即知识驱动方法和数据驱动方法。
该方法可提前预警管道堵塞
本文介绍了一种预测起下钻作业过程中钻柱摩擦系数的方法，可用于卡钻的早期预警。
来源：SPE 220991。
井眼清洁不彻底和井筒几何形状导致的卡钻是油气钻井工程中的主要问题。现有的研究主要集中于机器学习和摩擦计算来分析卡钻趋势。然而，这些方法往往会忽略导致卡钻的关键机理参数，导致准确性较低。针对这些问题，本文开发了一种下钻作业中钻柱摩擦系数的预测方法，可用于卡钻预警。
目前，卡管预测方法主要有两类，即知识驱动方法和数据驱动方法。
Stuck pipe caused by borehole uncleaning and wellbore geometry is a major issue in oil and gas drilling engineering. Existing research mainly focuses on machine learning and friction calculation for analyzing stuck-pipe trends. However, these methods often overlook key mechanism parameters contributing to stuck pipe, resulting in low accuracy. To solve these problems, a prediction method for drillstring-friction coefficient in tripping operations has been developed that can be used for early warning of stuck pipe.
Currently, two primary categories of stuck-pipe-prediction approaches are used widely: knowledge-driven and data-driven methods.
