| 富火山碎屑地层复杂岩性测井分类与识别——以KL16油田为例 |
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| 引用本文： | 冯冲,王清斌,谭忠健,代黎明,刘晓健,赵梦. 富火山碎屑地层复杂岩性测井分类与识别——以KL16油田为例[J]. 石油学报, 2019, 40(Z2): 91-101. DOI: 10.7623/syxb2019S2009 |
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| 作者姓名： | 冯冲 王清斌 谭忠健 代黎明 刘晓健 赵梦 |
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| 作者单位： | 中海石油(中国)有限公司天津分公司 天津 300459 |
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| 基金项目： | 国家科技重大专项"渤海海域勘探新领域及关键技术研究"（2016ZX05024-003）资助。 |
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| 摘 要： | 渤海南部莱州湾凹陷KL16油田沙河街组三段、四段和中生界分布4大类20余种岩性,富含火山碎屑、分布层系多、岩性识别困难。在对岩（壁）心及薄片分析基础上,应用元素测井、成像测井以及常规测井资料对其进行分类和识别。结果表明：自然伽马测井（GR）与深侧向电阻率测井（RD）交会的成分-结构图版可以有效区分熔岩和火山碎屑岩类、富火山碎屑砂砾岩类、湖相碳酸盐岩和混积岩类、硅质砂岩类4大类岩性;建立了研究区火成岩岩石成分和结构识别图版,可将熔岩和火山碎屑岩类细分为安山岩、安山质火山角砾岩、安山质凝灰岩、流纹斑岩、流纹质火山角砾岩和流纹质凝灰岩6种;中子测井（CNCF）和密度测井（ZDEN）曲线交会,结合深侧向电阻率测井（RD）曲线对砂砾岩具有较好的区分效果,可细分为贫泥砂砾岩、灰质砂砾岩、凝灰质砂砾岩以及泥质砂砾岩4种;光电吸收截面指数测井（PE）和声波时差测井（DT）曲线交会,结合成像测井和岩性扫描测井可以识别互层型湖相碳酸盐岩和组分混合型混积岩中的碳酸盐岩富集段。取得的这些认识为具有火山成因沉积盆地的复杂地层岩性测井识别提供了有益借鉴,具有很好的应用价值。
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| 关 键 词： | 复杂岩性 富火山碎屑 测井特征 中生界 沙河街组 KL16油田 莱州湾凹陷 |
| 收稿时间： | 2019-10-09 |
| 修稿时间： | 2019-10-09 |
Logging classification and identification of complex lithologies in volcanic debris-rich formations: an example of KL16 oilfield |
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| Feng Chong,Wang Qingbin,Tan Zhongjian,Dai Liming,Liu Xiaojian,Zhao Meng. Logging classification and identification of complex lithologies in volcanic debris-rich formations: an example of KL16 oilfield[J]. Acta Petrolei Sinica, 2019, 40(Z2): 91-101. DOI: 10.7623/syxb2019S2009 |
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| Authors: | Feng Chong Wang Qingbin Tan Zhongjian Dai Liming Liu Xiaojian Zhao Meng |
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| Affiliation: | Tianjin Branch of CNOOC China Limited, Tianjin 300459, China |
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| Abstract: | Four types of more than 20 lithologies are distributed in the third member of Shahejie Formation, the fourth member of Shahejie Formation, and the Mesozoic in the KL16 oilfield of Laizhou Bay Sag in the southern Bohai Sea. This area is rich in volcanic debris, and it is difficult to identify the lithologies distributed in multiple series of strata. Based on the analysis of (sidewall) cores and thin sections, the lithologies were classified and identified by elemental logging, imaging logging, and conventional logging data. The results show that lava and pyroclastic rocks, volcanic debris-rich glutenites, lacustrine carbonate rocks, hybrid sediments and siliceous sandstones can be effectively distinguished by the composition-structure crossplot of natural gamma logging (GR) and deep lateral resistivity logging (RD). A chart was established for identifying the composition and structure of igneous rocks in the study area, which can subdivide lava and pyroclastic rocks into andesite, andesitic volcanic breccia, andesitic tuff, rhyolite porphyry, rhyolite volcanic breccia, and rhyolite tuff. The crossing of neutron log (CNCF) and density log (ZDEN) curves in combination with deep lateral resistivity logging (RD) curve has a good effect on the identification of glutenites, which can be subdivided into poor-mud glutenite, calcite glutenite, tuffaceous glutenite and shaly glutenite. The enrichment section of carbonate rocks in the interbedded lacustrine carbonate rocks and multi-component mixed hybrid sediments can be identified by the crossing of photoelectric absorption cross-section index logging (PE) and acoustic travel time logging (DT) curves, in combination with imaging logging and lithoscanner logging. These understandings can provide a useful reference for the logging identification of complex formations in volcanogenic sedimentary basins, and have good application value. |
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| Keywords: | complex lithology volcanic debris-rich logging characteristics Mesozoic Shahejie Formation KL16 oilfield Laizhou Bay Sag |
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