测井技术2026，Vol.50Issue(1)：108-120,13.DOI:10.16489/j.issn.1004-1338.2026.01.010
基于CSSOA-DSRF模型的致密砂岩储层流体测井智能识别
Intelligent Well Logging Fluid Identification for Tight Sandstone Reservoirs Based on the CSSOA-DSRF Model
摘要
Abstract
Reservoir fluid identification is of great significance for the evaluation and development of tight sandstone oil and gas reservoirs.Tight sandstone reservoirs have characteristics such as low porosity,low permeability,and strong heterogeneity,which result in complex gas-water relationships.Traditional reservoir fluid identification methods mainly rely on data from resistivity logging and other techniques,making it difficult to identify reservoir fluids with weak conductivity contrast.With the development of machine learning and artificial intelligence technologies,the integration of well logging techniques and intelligent algorithms has played a key role in fluid identification.However,traditional machine learning models lack the ability to distinguish highly redundant and imbalanced samples,limiting their prediction capabilities.This paper proposes an intelligent well logging fluid identification model for tight sandstone reservoirs based on the chaos sparrow search optimization algorithm-double cost sensitive random forest(CSSOA-DSRF)model.The double cost sensitive random forest(DSRF)introduces cost-sensitive learning during both the feature selection and ensemble voting stages of the random forest algorithm.By assigning weight coefficients to different fluid types,it enhances the model's focus on minority class samples,making feature selection more targeted and selecting a set of decision trees that are more sensitive to minority class data,thus solving the issue of sample class imbalance.To overcome the limitations of traditional optimization methods that easily fall into local optima,the chaos sparrow search optimization algorithm(CSSOA)incorporates an improved Tent chaotic mapping and Gaussian mutation mechanism within the framework of the sparrow search algorithm(SSA),enhancing population diversity and global search capabilities,and reducing the risk of premature convergence.The model combines five well logging response feature curves from the study area:acoustic time-difference logging,compensated neutron logging,density logging,natural gamma logging,and deep lateral resistivity logging,to output the corresponding fluid type prediction results.The prediction accuracy based on the borehole results is 90.46%.Compared with DSRF,random forest(RF),K-nearest neighbors(KNN),and support vector machine(SVM),the method demonstrates high accuracy and maintains good robustness and stability,providing a feasible solution for fluid identification in tight sandstone reservoirs.关键词
致密砂岩/机器学习/随机森林/支持向量机/麻雀搜索算法/遗传算法/决策树/种群Key words
tight sandstone/machine learning/random forest/support vector machine/sparrow search algorithm/genetic algorithm/decision tree/population分类
天文与地球科学引用本文复制引用
展硕硕,李可赛,刘岩,林行杰,雷铠铖,郑明明,刘彦君,冯国栋..基于CSSOA-DSRF模型的致密砂岩储层流体测井智能识别[J].测井技术,2026,50(1):108-120,13.基金项目
国家自然科学基金项目"多尺度裂缝性储层中随钻方位电磁波测井响应机理研究"(42404144) （42404144）
国家科技重大专项项目"深层页岩气开发机理与开发关键技术"(2025ZD1404100) （2025ZD1404100）
国家科技重大专项课题"地下水多点协同监测及迁移影响机理"(2025ZD1404107) （2025ZD1404107）
