基于模糊融合预测的页岩地质甜点识别技术——以胜利油区渤南洼陷为例  

Geological sweet spot identification technology of shale based on fuzzy fusion prediction:A case study of Bonan Subsag in Shengli Oilfield

作　　者：王长江[1] 颜世翠 张娟 刘庆敏[1] 徐仁 仲保温 WANG Changjiang;YAN Shicui;ZHANG Juan;LIU Qingmin;XU Ren;ZHONG Baowen(Exploration and Development Research Institute,Shengli Oilfield Company,SINOPEC,Dongying City,Shandong Province,257015,China)

机构地区：[1]中国石化胜利油田分公司勘探开发研究院,山东东营257015

出　　处：《油气地质与采收率》2024年第4期73-83,共11页Petroleum Geology and Recovery Efficiency

基　　金：中国石化科技攻关项目“地质模式约束的非均质储层精细刻画”(P22161)。

摘　　要：针对常规地震在页岩地质甜点预测中多解性较强的问题,充分挖掘一维的全岩分析数据和测井数据、二维的地质数据、三维的叠后地震数据以及五维的OVT资料方位信息,开展基于模糊融合预测的页岩地质甜点识别技术研究,提高页岩地质甜点预测准确率。首先,分别统计研究区断裂大致展布方向和有利岩相分布区,优选敏感方位角度段对OVT资料进行分方位叠加;其次,开展基于叠前优势方位的裂缝平面分析和基于前馈神经网络的页岩有利岩相三维预测;最后,研发基于改进Sigmoid和Takagi-Sugeno(TS)函数的模糊融合技术,根据页岩地质甜点控制因素的重要性程度确定主控因素的发挥作用,将方位各向异性裂缝平面预测结果和神经网络页岩岩相预测结果有效融合,实现页岩地质甜点决策融合。应用该技术在渤南洼陷沙三段下亚段开展页岩地质甜点分类分级评价,在裂缝和岩相预测基础上,剔除裂缝不发育区和不利岩相对页岩地质甜点分析的干扰,将研究区页岩地质甜点划分为3类,裂缝发育+有利岩相叠合区为一类甜点,预测结果与实钻井吻合程度较高,取得较好应用效果。研究结果表明,基于模糊融合预测的页岩地质甜点识别技术,实现了页岩地质甜点分类分级评价,提高了预测可信度,为页岩油勘探提供了可靠的技术支撑。In response to the strong multi-solution problem of conventional seismic exploration methods in geological sweet spot prediction of shale,this paper fully explored one-dimensional whole rock analysis data and well logging data,two-dimensional geo‐logical data,three-dimensional post-stack seismic data,and five-dimensional offset vector tile(OVT)orientation information.In addition,the paper researched geological sweet spot identification technology of shale based on fuzzy fusion prediction to improve the accuracy of geological sweet spot prediction of shale.Firstly,the approximate distribution direction of faults in the study area and the distribution area of favorable shale lithofacies were statistically analyzed,and the sensitive azimuthal section was selected to stack the OVT data based on azimuth.Then,the fractures on the plane were analyzed based on pre-stack preferred azimuth,and the two-dimensional prediction of favorable shale lithofacies was carried out using a feedforward neural network.Finally,a fuzzy fu‐sion technology based on an improved Sigmoid and Takagi-Sugeno(TS)function was developed,which could weigh the signifi‐cance of controlling factors in geological sweet spot identification of shale according to their degree of influence and effectively inte‐grate the predicted results of azimuthal anisotropy fractures on the plane with those of neural network-based shale lithofacies,so as to realize decision-making integration for identifying geological sweet spots of shale.This technology has been applied in the classi‐fication and grading evaluation of geological sweet spots of shale in the Lower Submember of the 3rd Member of Shahejie formation(Es3U)in Bonan Depression.Areas with poorly developed fractures and unfavorable lithofacies that could interfere with the analysis of geological sweet spots of shale were filtered out.Based on the predictions of fractures and lithofacies,The geological sweet spots of shale in the study area were categorized into three classes.The areas characterized by

关 键 词：方位各向异性 页岩地质甜点 裂缝识别 岩相预测 SIGMOID函数 Takagi-Sugeno函数 

分 类 号：TE357[石油与天然气工程—油气田开发工程]