基于流动单元分类与优选的致密砂岩储层测井渗透率评价方法  

Permeability Evaluation from Logs in Tight Sandstone Reservoirs Based on Classification and Optimization of Flow Units

作　　者：杨清 管耀 冯进 王清辉 王思宇 谭茂金[2,3] YANG Qing;GUAN Yao;FENG Jin;WANG Qinghui;WANG Siyu;TAN Maojin(Shenzhen Branch of CNOOC Limited,Shenzhen,Guangdong,518000,China;School of Geophysics and Information Technology,China University of Geosciences,Beijing,100083,China;Borehole Detection Technology Branch,National Engineering Research Center for Offshore Oil and Gas Exploration,Beijing,100083,China)

机构地区：[1]中海石油(中国)有限公司深圳分公司,广东深圳518000 [2]中国地质大学(北京)地球物理与信息技术学院,北京100083 [3]海洋油气勘探国家工程研究中心井筒探测技术分中心,北京100083

出　　处：《石油钻探技术》2025年第2期181-190,共10页Petroleum Drilling Techniques

基　　金：国家自然科学基金项目“深层页岩气藏岩石物理多尺度融合与储层品质井震智能评价方法”(编号:42430810);中国海洋石油有限公司“十四五”重大科技项目“海上深层/超深层油气勘探技术”(编号:KJGG2022-0406);海洋油气勘探国家工程研究中心主任基金(2024)联合资助。

摘　　要：致密砂岩储层非均质性强,常规测井解释模型没有考虑储层纵向上渗流特征的差异性,导致渗透率解释精度低。为此,采用储层流动单元描述致密砂岩储层非均质特征,建立了具有不同渗流单元的渗透率解释模型,以提高渗透率预测精度。首先,结合岩心实验流动单元指数频数分布与累计分布频率,建立流动单元分类标准,并优选流动单元分类数目,分类构建渗透率模型;然后,引入深度神经网络,结合常规测井和核磁测井数据,预测流动单元指数;最后,基于分类渗透率解释模型,计算储层渗透率。珠江口盆地惠州凹陷古近系恩平组应用该渗透率计算方法进行计算,流动单元分为5类最佳,测井尺度的流动单元识别分类与沉积相具有较好的一致性,渗透率计算准确度相比核磁模型明显提高。研究结果为深层致密砂岩储层渗透率评价提供了新的计算方法。Due to the strong heterogeneity of tight sandstone reservoirs,conventional log interpretation models usually overlook the difference in the longitudinal flow properties of the reservoirs,leading to low accuracy in the calculation and interpretation of permeability.Therefore,the flow units(FUs)of reservoirs were utilized to describe and quantify the heterogeneity characteristics of tight sandstone reservoirs.Permeability interpretation models of different FUs were constructed to improve the accuracy of permeability predictions.First,the FU classification standard was established by combining the frequency distribution and cumulative distribution probability of the experimental FU indicator(IFZ)from cores,and the optimal classification number of FUs was selected to establish permeability models in different categories.Additionally,deep neural networks(DNN)were introduced to predict IFZ by combining conventional logging and nuclear magnetic resonance(NMR)logging data.Finally,reservoir permeability was calculated based on the permeability interpretation models in different categories.This permeability calculation method was applied to the Paleogene Enping Formation in Huizhou Sag,Pearl River Mouth Basin,and five FU categories were selected for optimal classification.The identification and classification of FUs at the log scale were in good agreement with sedimentary facies,and the accuracy of permeability calculation was significantly improved compared to the NMR model.The research results provide a new calculation method for evaluating the permeability of deep tight sandstone reservoirs.

关 键 词：致密砂岩 渗透率评价 流动单元 深度学习 

分 类 号：P631.81[天文地球—地质矿产勘探]