基于随钻数据的岩性识别机器学习算法研究进展

机器学习算法是岩性识别领域重点研究内容之一。与传统岩性识别方法相比，通过监测随钻参数变化进行岩性识别，具有高精度、多信息、集成化、智能化的优点。近年来，随着岩性识别技术不断发展，机器学习算法在岩性识别领域的研究和应用日益广泛。利用机器学习算法分析随钻数据，能够提高岩性识别结果的准确性，更高效地识别地层的岩性和构造。为了厘清岩性识别机器学习算法的发展现状，发掘其在岩性识别技术领域中的技术难题，综述了岩性识别机器学习算法的研究进展。首先，简要介绍了机器学习的概念与发展历程；其次，分类阐述能够用于岩性识别领域的机器学习算法；再次，总结了岩性识别领域各类常用机器学习算法的应用现状，比较了各类算法在岩性识别应用中的优缺点；最后，总结了岩性识别算法存在的问题和面临的挑战，并对其下一步发展方向提出了建议，使未来能更加准确高效地利用机器学习算法分析处理随钻数据，实现机器学习算法与岩性识别技术的深度结合。

Research progress of machine learning algorithm for lithology identification based on data while drilling

Machine learning algorithm is one of the key research contents in the field of lithology identification. Compared with traditional lithologic identification methods, lithologic identification by monitoring the changes of parameters while drilling has the advantages of high accuracy, multi-information, integration and intelligence. In recent years, with the continuous development of lithology identification technology, the research and application of machine learning algorithms in the field of lithology identification has become increasingly widespread. Using the machine learning algorithm to analyze the parameters while drilling can improve the accuracy of the lithology identification results, and identify the lithology and structure of the formation more efficiently. In order to clarify the current development status of the machine learning algorithm for lithology identification and to discover its technical challenges in the field of lithology identification technology, the research progress of the machine learning algo-rithm for lithology identification is reviewed. Firstly, the concept and development history of machine learning are briefly introduced; secondly, the machine learning algorithms that can be used in the field of lithology identification are classified and explained. Thirdly, the application status of various commonly used machine learning algorithms in the field of lithology identification is summarized, and the advantages and disadvantages of various algorithms in the application of lithology identification are compared. Finally, the existing problems and challenges of the lithology identification algorithm are summarized, and suggestions for its next development direction are put forward, so that the machine learning algorithm can be used to analyze and process data while drilling more accurately and efficiently in the future, and the deep combination of machine learning algorithm and lithology identification technology can be realized.