利用测井资料进行地层岩性识别是石油勘探工程的关键环节，基于机器学习的岩性分类器因其优异性能成为目前研究热点。针对现有方法过分依赖相关人员调参经验的问题，结合XGBoost集成分类器的强鲁棒性和差分进化算法的快速搜索能力，提出了一种可自动调参的机器学习岩性识别方法，通过差分进化对XGBoost分类器参数种群迭代得到全局最优岩性识别模型。在大庆油田实际测井数据基础上，分别开展了基于BP神经网络、RandomForest、XGBoost等方法的岩性识别对比实验，结果表明所提方法在总体准确度、各子类识别精度以及识别稳定性均获得较好表现。

Using logging data to identify stratum lithology is a key link in petroleum exploration engineering. The lithology classifier based on machine learning has become a current research hotspot due to its excellent performance. Aiming at the problem that existing methods rely too much on relevant personnel's parameter adjustment experience, combined with the strong robustness of the XGBoost integrated classifier and the fast search ability of the differential evolution algorithm, a machine learning lithology identification method that can automatically adjust parameters is proposed. Differential evolution iterates the parameter population of XGBoost classifier to obtain the global optimal lithology recognition model. Based on the actual logging data of Daqing Oilfield, comparative experiments on lithology identification based on BP network, Randomforest, XGBoost have been carried out. The results show that the proposed method has good performance in overall accuracy, stability and recognition accuracy of each sub-category.

国家自然科学基金项目（面上项目，重点项目，重大项目）