钻柱摩阻扭矩智能实时分析与卡钻趋势预测

钻柱摩阻扭矩的实时分析对提高钻井效率、规避钻井卡钻风险具有重要作用，目前摩阻扭矩分析以钻前预测为主，但钻井过程中摩阻扭矩的实时分析尚不成熟。针对当前井底钻压扭矩预测不准、钻柱摩阻系数的确定存在盲目性等问题，提出一种钻柱摩阻扭矩智能实时分析方法。该方法利用神经网络模型实时计算井底钻压扭矩，结合摩阻扭矩刚杆模型采用二分法实时反演摩阻系数，准确分析钻柱受力。考虑到钻柱摩阻系数在一定程度上表征钻柱卡钻趋势，进一步利用该方法对钻井卡钻趋势进行预测。将该方法应用于现场数据，发现某井钻柱摩阻系数在6 000~6 100 m区间整体呈现逐渐增大的趋势，且在6 100 m处附近，钻柱摩阻系数从0.35附近陡增至0.75，变化极为剧烈，说明即将发生卡钻。经过对该井的钻井日志查证，该井在6 100 m处附近蹩停顶驱钻具卡死。说明利用该方法对卡钻趋势进行预测具有良好的效果，便于现场实时调整钻井参数，有效规避卡钻风险,提高钻井效率。

Intelligent real-time drag and torque analysis and sticking trend prediction of drill string

The real-time analysis on the drag and torque of drill string plays an important role in improving drilling efficiency and avoiding sticking risks. At present, the drag and torque analysis is dominated by pre-drilling prediction while the real-time drag and torque analysis in the process of drilling is not developed. Currently, the bottom hole weight on bit (WOB) and torque are not predicted accurately and the friction coefficient of drill string is not determined specifically. To solve these problems, this paper proposed an intelligent real-time analysis method of drag and torque of drill string. In this method, the bottom hole WOB and torque are calculated in real time by using the neural network model and then the friction coefficient is inverted by means of the dichotomy method, combined with the rigid model of drag and torque, so that the force applied on the drill string can be analyzed accurately. When this method is applied to field data, it is shown that the friction coefficient of drill string in one certain well presents the overall trend of increasing gradually in the interval of 6000-6100 m and then increases abruptly from 0.35 to 0.75 near 6100 m, indicating sticking will happen. The drilling log of this well describes that the top-drive drilling tool is stopped and frozen near 6100 m. It is indicated that this method can well predict sticking trend, help field real-time adjustment of drilling parameters, effectively avoid sticking risks and improve drilling efficiency.