基于梯度提升决策树算法的岩性智能分类方法

Intelligent lithology classification method based on GBDT algorithm

岩性识别是油气勘探开发领域一项重要的基础工作。针对致密砂岩储层岩石成分复杂、岩性多样和岩性常规测井识别受限等问题,利用机器学习算法在数据分析上的强大功能,采用泛化能力出众的梯度提升决策树(GB⁃DT)算法解决岩性识别中人力和物力耗费大的问题。以鄂尔多斯盆地三叠系延长组长7段致密砂岩储层为研究对象,通过敏感分析选取声波时差、自然伽马、电阻率、泥质含量、自然电位、有效孔隙度、含水饱和度和密度8个测井参数,构建基于GBDT算法的岩性识别模型,结合实际数据进行验证和应用效果分析。与朴素贝叶斯、随机森林、支持向量机和人工神经网络算法岩性识别相比,GBDT算法岩性识别准确率达到了92%,高精度的GBDT算法岩性识别模型为致密砂岩储层岩性精确识别提供了新的解决途径。

Lithology identification is a vital basic work in the field of oil and gas exploration and development.Tight sandstone reservoirs suffer from complex rock composition,diverse lithology,and limited lithology identification by conventional logging.As machine learning is powerful in data analysis,this paper proposed a gradient boosting decision tree(GBDT)algorithm with strong generalization ability to cut down large manpower and material resource consumption in lithology identification.Taking Chang7 Member of Yanchang Formation in Ordos Basin as the research object,it selects eight logging parameters including acoustic time difference(AC),natural gamma ray(GR),resistivity(RT),clay content(SH),natural potential(SP),effective porosity(POR),water saturation(Sw),and density(DEN)through sensitive analysis to build a lithology identification model based on GBDT algorithm.Actual data were applied to verify and analyze the application effect.The accuracy of the GBDT algorithm can reach 92%,compared with that of other methods such as naive Bayes,random forest,support vector machine,and artificial neural network for lithology identification.The high-precision lithology identification model based on the GBDT algorithm provides a new solution for tight sandstone reservoir evaluation.