测井技术2025，Vol.49Issue(1)：77-87,11.DOI:10.16489/j.issn.1004-1338.2025.01.009
基于支持向量机的潜山储层岩性识别
Lithology Identification of Buried Hill Reservoirs Based on Support Vector Machine
摘要
Abstract
As an unconventional reservoir,the potential mountain bedrock reservoir presents significant challenges for lithology identification compared to traditional clastic reservoirs due to its complex bedrock structure,tectonic features,chemical composition and mineralogy.Conventional logging data and traditional evaluation methods are limited in their effectiveness for identifying lithology in such reservoirs,making it difficult to distinguish between different lithology types.To address this issue,we selecte the HZ depression as the study area and propose a lithology identification model based on the support vector machine(SVM)algorithm.SVM is well-suited for handling high-dimensional data and exhibits strong generalization capability,while being relatively simple to implement,making it ideal for identifying complex reservoir lithology.During the model development process,considering the strong non-linear characteristics of the logging curves in the study area,we combine experimental data and driling information,and use the calculate formation mineral content as a constraint condition for model training.To further enhance the lithological feature learning capability of the SVM model,we selecte logging curve data that are sensitive to lithology,such as natural gamma,acoustic transit time,and compensate density,for feature extraction and use them for training the SVM model.Using this approach,an efficient lithology identification model is established and applied to the lithology identification task of seven wells in the HZ area.The experimental results indicate that the SVM model achieves an average identification accuracy of 94.46%.Compared with the conventional crossplot method and Random Forest algorithm,the SVM algorithm shows significant advantages in this area and effectively addresses the complex lithology identification challenges of the potential mountain reservoir.关键词
潜山储层/岩性识别/支持向量机/矿物含量/HZ凹陷Key words
potential mountain reservoir/lithology identification/support vector machine/mineral content/HZ depression引用本文复制引用
高永德,吴进波,孙殿强..基于支持向量机的潜山储层岩性识别[J].测井技术,2025,49(1):77-87,11.基金项目
国家自然科学基金项目"基于'矿物-岩性-测井'响应机制的火成岩潜山岩性建模井孔硬数据质量增强方法研究"(42472213) （42472213）
