新疆石油地质

›› 2015, Vol. 36 ›› Issue (6): 1-1.doi: 10.7657/XJPG20150613

• 论文 •    

邵家洼陷沙四段湖相碳酸盐岩岩性测井解释模型

杨生超1，邱隆伟1，刘魁元2，徐宁宁1，杨勇强1，韩 霄3, 姜嘉诚4   

  1. (1.中国石油大学 地球科学与技术学院,山东 青岛 266580;2.中国石化 胜利油田分公司 河口采油厂,山东 东营 257200;3.中海石油 深圳分公司 研究院,深圳 510240;4.骏马石油装备制造有限公司, 山东 东营 257000)
  • 出版日期:2019-01-01 发布日期:1905-07-16

Lithology Logging Interpretation Model for Lacustrine Carbonate Rocks of the Es4 in Shaojia SubSag, Jiyang Depression, Bohai Bay Basin

YANG Shengchao1, QIU Longwei1, LIU Kuiyuan2, XU Ningning1, YANG Yongqiang1, HAN Xiao3, JIANG Jiacheng4   

  1. (1.School of Geosciences, China University of Petroleum, Qingdao, Shandong 266580, China;2.Hekou Production Plant, Shengli Oilfield Company, Sinopec, Dongying, Shandong 257200, China; 3. Shenzhen Branch Research Institute, CNOOC, Shenzhen 510240, China;4.Junma Petroleum Equipment Manufacturing Group Co., LTD, Dongying, Shandong 257000, China)
  • Online:2019-01-01 Published:1905-07-16

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摘要： 综合岩心、薄片、测井以及分析测试资料，以沉积相带限定范围，按沉积相类型分别建立了邵家洼陷渐新统沙河街组四段碳酸盐岩岩性测井解释模型。研究表明，邵家洼陷沙河街组四段发育白云岩、生物灰岩、砂屑灰岩、鲕粒灰岩、泥晶灰岩、泥灰岩和石膏质灰岩等碳酸盐岩，主要沉积类型为生物礁、近岸灰岩滩与远岸灰岩滩。统计各相带内岩石类型及测井响应特征，按相带分别建立岩性测井解释模型。将模型运用于研究区部分井段，经薄片资料验证，识别效果较好。湖相碳酸盐岩岩性测井解释模型提高了岩性的识别精度，对预测碳酸盐岩有利储集层的分布十分有益。

Abstract: To predict the lithology of lacustrine carbonate rocks is the key of predicting its favorable reservoir distribution. Based on the core data, thin sections, well logs and test information, and taking sedimentary facies as limited ranges, the rock?electrical identification model for the carbonate rocks of Es4 in Shaojia sub?sag is developed by means of the types of sedimentary facies. The study shows that in the Es4 of Shaojia sub?sag, several categories of carbonates occur in this area, such as reef dolomite, bioclastic limestone, calcarenite, oolitic limestone, micrite limestone, packstone, gypsum limestones, etc., and the main sedimentary types are reef, nearshore limestone bank and infralittoral limstone bank. The rocks and their logging characteristics in each facies are collected, followed by making lithology logging interpretation model for each facies. These models are applied to some well sections in this area, which indicate good results from thin?section verifications in identification accuracy and favorable carbonate reservoir prediction

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