基于多层集成学习的岩性识别方法

岩性识别是油藏地质解释中的关键问题和难点问题，人工智能特别是机器学习技术的发展和应用为岩性识别问题解决提供了新的技术途径。本文利用支持向量机（Support vector machine,SVM）、多粒度级联森林（Multi-grained cascade forest,GCForest）、随机森林（Random forest，RF）以及XGBoost（eXtreme gradient boosting）等机器学习模型建立一个异构多层集成学习模型，该集成学习模型克服了单一模型对数据集要求高、泛化能力差以及识别精度低等缺点。本文分别利用集成模型和单一模型进行了岩性识别实验。实验结果表明，本文集成模型在岩性分类测试集上平均精度达到96.66%，高于SVM的平均精度75.53%、GCForest的平均精度96.21%、随机森林的平均精度95.06%和XGBoost的平均精度95.77%。该集成模型能有效地用于油藏地质分析中的岩性识别和分类任务，适应性强，识别精度高。

Lithology Identification Method Based on Multi-layer Ensemble Learning

Lithology identification is a key and difficult problem in reservoir geological interpretation. The development and application of artificial intelligence, especially machine learning technology, provides a new technical way to solve lithology identification problems. This paper uses machine learning models such as support vector machine (SVM), multi-grained cascade forest (GCForest), random forest (RF) and eXtreme gradient boosting (XGBoost) to build a heterogeneous multi-layer integrated learning model. The integrated learning model overcomes the shortcomings of single model such as high requirement for data sets, poor generalization ability and low recognition accuracy. In this paper, lithology recognition experiments are carried out using integrated models and single models. The experimental results show that the average accuracy of the integrated model is 96.66%, higher than that of SVM (75.53%), GCForest (96.21%), random forest (95.06%) and XGBoost (95.77%). The integrated model can be effectively applied to lithology identification and classification tasks in reservoir geological analysis with strong adaptability and high recognition accuracy.