岩石可钻性分级方法研究及展望

Research and Prospect of Rock Drillability Classification Method

摘要: 地层可钻性是钻头选型及钻头结构设计的重要依据，在钻井工程中准确评价可钻性级值能提高钻速、降低钻井成本，快速可靠地进行可钻性分级显得尤为重要。本文将可钻性分级方法的发展应用过程划分为三个阶段进行综述，阐述了各阶段可钻性分级方法的基本原理、具体的试验或建模过程，并对各种分级方法的特点做出评价。结果表明：传统方法测定可钻性级值精度最高，但不连续；数理分析方法计算简便快捷，但精度不高；机器学习预测方法可预测连续的地层可钻性级值，精度较传统实验测定法有所降低。本文还提出了一种在现行方法的基础之上利用新一代人工智能ChatGPT进行可钻性预测的展望，以期相关学者进行参考。

Abstract: Formation drillability is an important basis for bit selection and structure design. In drilling engineering, accurate evaluation of drillability level can improve drilling rate and reduce drilling cost, so it is particularly important to conduct drillability classification quickly and reliably. This paper summarizes the development and application process of drillability classification method into three stages, describes the basic principle of drillability classification method at each stage, the specific test or modeling process, and evaluates the characteristics of each classification method. The results show that the traditional method has the highest accuracy, but it is not continuous. Mathematical analysis method is simple and fast, but the accuracy is not high. The machine learning prediction method can predict continuous formation drillability level values with lower accuracy than the traditional experimental measurement method. Based on the existing methods, a new generation of artificial intelligence ChatGPT is proposed for drillability prediction, which is expected to be referred to by relevant scholars.