基于岩石硬度与钻井参数的钻头磨损预测模型

A Prediction Model for Bit Wear Based on Rock Hardness and Drilling Parameters

摘要: 钻具磨损是钻井工程中影响施工效率与作业成本的关键因素，其磨损程度受岩石磨蚀性与钻井参数的共同影响。在影响岩石磨蚀性的诸多因素中，矿物硬度是本质性指标，莫氏硬度作为矿物硬度的分级标准，对钻具磨损具有重要控制作用，但现有研究对二者在磨损过程中的耦合机制关注不足。为此，本文以岩性莫氏硬度为核心因子，采用矿物加权平均法量化地层整体莫氏硬度，分析不同莫氏硬度等级下钻具磨损类型与磨损速率的变化特征，在此基础上引入钻压、转速、扭矩等关键钻井参数，构建考虑莫氏硬度与钻井参数交互影响的钻具磨损多元回归评估模型。利用某油田5口井的856组现场数据进行验证，结果表明，该模型对钻具磨损等级的预测准确率(误差 ≤ 1级)为82.5%，决定系数R²为0.79。本研究为钻井过程中钻具磨损预测与钻井参数优化提供了可量化的评估方法。

Abstract: Drill tool wear is a critical factor affecting drilling efficiency and operational cost. The degree of wear is jointly influenced by rock abrasiveness and drilling parameters. Among the factors controlling rock abrasiveness, mineral hardness is a fundamental property, and Mohs hardness, as a standardized scale of mineral hardness, plays a key role in drill tool wear. However, the coupling mechanism between Mohs hardness and drilling parameters in the wear process remains underexplored. In this study, Mohs hardness is taken as the core factor. A mineral-weighted average method is adopted to quantify the overall Mohs hardness of the formation. The variation patterns of wear type and wear rate under different Mohs hardness levels are analyzed. On this basis, key drilling parameters including weight on bit, rotational speed, and torque are incorporated, and a multiple regression model considering the interaction between Mohs hardness and drilling parameters is established. The model is validated using 856 sets of field data from five wells in a certain oilfield. Results show that the prediction accuracy of wear grade (error ≤ 1 grade) reaches 82.5%, with an R² of 0.79. This study provides a quantifiable evaluation method for drill tool wear prediction and drilling parameter optimization.