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鄂尔多斯盆地杭锦旗地区井漏地质机理分析

Geological Mechanism Analysis of Mud Loss in Hangjinqi Area of Ordos Basin
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摘要 鄂尔多斯盆地北部杭锦旗地区钻井过程中频繁出现井漏现象,为了研究搞清楚井漏产生的地质机理,采用杭锦旗地区的钻井、录井、测井等资料,开展了研究区井漏高发层位、区域、漏失特征等因素的分析。分析研究结果表明:研究区井漏区域主要位于刘家沟组泥岩、下石盒子组储层以及元古界顶部风化壳,漏失机理主要为刘家沟组泥岩本身易破碎,承压能力差,漏失层位主要受断层、褶皱以及风化壳产生的裂缝控制;下石盒子组储层受断层影响,发育高角度裂缝;元古界顶部风化壳附近裂缝发育。基于井漏发生的地质机理提出了以下几种预警方法:井漏大数据分析法、复杂构造识别法、储层改造分析法,对于预防井漏有积极作用。 During the drilling process in the Hangjinqi area of the northern Ordos Basin,mud losses occur frequently.In order to clarify the geological mechanism of mud loss,drilling,mud logging,well logging and other data from the Hangjinqi area were used to analyze thecharacteristics of mud loss,the layers and regions where mud losses occur in the study area.The results indicate that the mud loss areas are mainly located in the mudstone of the Liujiagou Formation,the reservoir of the Lower Shihezi Formation,and the weathered crust at the top of the Proterozoic.The mainmud loss mechanismin the mudstone of the Liujiagou formation lies in its easy breakabilityand poor pressure bearing capacity.The mud loss is mainly controlled by faults,folds,and fractures generated by the weathered crust;The reservoir of the Lower Shihezi Formation is affected by faults and develops high angle fractures;cracks develop near the weathered crust at the top of the Proterozoic.Based on the geological mechanism of mud loss,the following early warning methods are proposed:big data analysis method for mud loss,complex structure identification method,and reservoir exploitation analysis method,which have a positive effect on preventing mud loss.
作者 韩涛 李功强 白彬艳 高小淇 骆淼 Han Tao;Li Gongqiang;Bai Binyan;Gao Xiaoqi;Luo Miao(North China Measurement and Control Company of Sinopec Matrix Corporation,Zhengzhou Henan 450006,China;School of Geophysics and Geomatics,China University of Geosciences,Wuhan Hubei 430074,China)
出处 《工程地球物理学报》 2024年第2期247-255,共9页 Chinese Journal of Engineering Geophysics
基金 国家自然科学基金(编号:41574121)。
关键词 杭锦旗地区 井漏特征 地质机理 预警方法 Hangjinqi area mud loss geological mechanism early warning method
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