西南石油大学学报(自然科学版) ›› 2007, Vol. 29 ›› Issue (2): 78-81.DOI: 10.3863/j.issn.1000-2634.2007.02.021

• 地质勘探 • 上一篇    下一篇

兴城气田深层火山岩气藏岩性识别技术研究

王拥军1 闫 林1 冉启全1 邱红枫2 高 涛2   

  1. (1.中国石油勘探开发研究院,北京 100083; 2.大庆油田勘探开发研究院,黑龙江 大庆 163712)
  • 收稿日期:2006-10-25 修回日期:1900-01-01 出版日期:2007-04-20 发布日期:2007-04-20
  • 通讯作者: 王拥军

LITHOLOGIC IDENTIFICATION OF DEEP VOLCANIC ROCK GAS RESERVOIR IN XINGCHENG GAS FIELD

WANG Yong-Jun YAN Lin RAN Qi-quan et al.   

  1. (Research Institute of Petroleum Exploration and Development,Petrochina,Beijing 100083,China)
  • Received:2006-10-25 Revised:1900-01-01 Online:2007-04-20 Published:2007-04-20

摘要: 火山岩气藏已逐渐成为重要的勘探目标和油气储量的增长点。研究区火山岩SiO2含量平均为74.2%,里特曼指数平均2.26,以酸性钙碱性火山岩为主。针对火山岩气藏埋藏深、岩石类型多、成分差异小、岩石定名和识别难度大的特点,采用“成分+结构+成因”的三级综合方法对火山岩进行分类。在岩芯观察、薄片鉴定和全岩分析的基础上,应用Elemental Capture Spectroscopy(ECS)测量火山岩主要造岩元素及造岩矿物的百分含量,确定火山岩成分;应用Fullbore Formation MicroImager(FMI)成像测井识别火山岩结构和构造;应用主成分分析法,利用常规测井资料,从放射性、物性和导电性等方面综合识别火山岩。研究结果表明:综合应用ECS、FMI成像测井及常规测井是解决火山岩岩性识别问题的最佳途径。

关键词:

Abstract: Volcanic gas reservoirs have become important exploration target and increasing factor of oil and gas reserves. the average content of SiO2 is 74.4%, and the average Littmann Index is 2.26 in the area of interest. Most volcanic rocks in this area belong to acidic and calcalkaline volcanic suite. Because of deep buring, more rock types and less component difference, the volcanic rocks are difficult to be named and identified. The naming rule of “component + texture+ origin” is used to classify volcanic rocks in this case. Based on core observation, thin section analysis and the whole rock analysis, elemental capture spectroscopy(ECS) logging data is used to measure the content of the main petrogenetic elements and minerals and determine lithologic composition of volcanic rocks;fullbore formation microImager(FMI) image is used to identify rock texture and structure of volcanic rocks;conventional logging data reflects many characteristics of rocks, including the radioactivity, physical property and conductivity etc. So, it is used to identify synthetically volcanics by the method of principal component analysis. The results of research show that it is the most efficient method to identify volcanic rocks synthetical applying ECS, FMI and conventional logging data.

Key words: volcanic rocks, lithologic identification, principal components

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