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湖相碳酸盐岩研究进展及展望
引用本文:刘震,张军华,王静,于正军,苏朝光.湖相碳酸盐岩研究进展及展望[J].石油地球物理勘探,2022,57(2):487-497.
作者姓名:刘震  张军华  王静  于正军  苏朝光
作者单位:1. 中国石油大学(华东)地球科学与技术学院, 山东青岛 266580;2. 中国石油大学(华东)深层油气重点实验室, 山东青岛, 266580;3. 中国石化胜利油田分公司物探研究院, 山东东营 257022
基金项目:国家自然科学基金项目“含油气盆地低级序断裂的发育规律及其与高级序断裂的成因联系——以渤海湾盆地济阳坳陷为例”(42072169);;中国石油大学(华东)研究生创新工程项目“基于叠前反演的碳酸盐岩裂缝识别及三维立体透明化融合显示技术研究”(YCX2021014);;国家留学基金(202106450009)联合资助;
摘    要:湖相碳酸盐岩油气勘探逐渐受到重视,但目前相关的研究方法、成果尚处于初步阶段,缺乏系统性梳理。为此,首先广泛调研、系统总结近年来湖相碳酸盐岩的地质、测井和地球物理等方面的主要研究进展,并以济阳坳陷为例进行重点解剖;然后对比、分析湖相与海相碳酸盐岩特征,以此展望未来湖相碳酸盐岩的研究技术和方法。研究认为:①目前湖相碳酸盐岩地质理论方面的研究尚处于初期,没有统一、成熟的沉积相划分方案;湖相碳酸盐岩主控因素为古气候、古构造、古水介质条件、古地貌和古物源;储集空间可分原生孔隙、次生孔隙和裂缝三类,影响因素分别为沉积环境、成岩作用和构造作用。②湖相碳酸盐岩具指形、圆弧形、齿形、U形等测井相特征,目前主要利用沉积微相识别储层、分析流体性质。③湖相碳酸盐岩地震振幅切片常具有典型的滩核特征,利用分频成像、90°相移技术可提高识别能力。④湖相碳酸盐岩与海相碳酸盐岩在发育年代、空间分布、沉积相特征、储层类型与特征、烃源岩等诸多方面存在差异。前者发育于中生代和新生代,受古气候影响大;后者则多发育于侏罗系、二叠系、三叠系、奥陶系和寒武系,主要受潮汐影响。⑤未来湖相碳酸盐岩储层预测应围绕断裂和薄互层两个方面展开;湖相碳酸盐岩的沉积理论有望得到进一步发展,以形成统一的相带划分模式;基于大数据分析的深度学习方法,是未来重要的发展方向。

关 键 词:湖相碳酸盐岩  济阳坳陷  沉积相划分  滩核  薄互层  深度学习  
收稿时间:2021-03-25

Review and prospect of research on lacustrine carbonate rocks
LIU Zhen,ZHANG Junhua,WANG Jing,YU Zhengjun,SU Chaoguang.Review and prospect of research on lacustrine carbonate rocks[J].Oil Geophysical Prospecting,2022,57(2):487-497.
Authors:LIU Zhen  ZHANG Junhua  WANG Jing  YU Zhengjun  SU Chaoguang
Affiliation:1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China;2. Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao, Shandong 266580, China;3. Geophysical Research Institute, Shengli Oilfield Company, SINOPEC, Dongying, Shandong 257022, China
Abstract:Oil and gas exploration in lacustrine carbonate rocks has been paid increasing attention to. Nevertheless, related research methods and results are still in a preliminary stage and lack systematic review. In this paper, the main progress in geolo-gical, logging, and geophysical research on lacustrine carbonate rocks in recent years is extensively investigated and systematically summarized, with the focus placed on the Jiyang Depression as an example. Then, the characteristics of lacustrine and marine carbonate rocks are compared to assess the prospect of research techniques and methods for lacustrine carbonate rocks. The observations are as follows: ①Research on geological theories about lacustrine carbonate rocks is still in its infancy, and a unified and proven scheme of sedimentary facies division remains to be developed at present; the main controlling factors for lacustrine carbo-nate rocks are paleoclimate, paleostructure, paleo-water medium condition, paleogeomorphology, and paleosource; the reservoir space can be divided into primary pores, secondary pores, and fractures, and the influencing factors are sedimentary environment, diagenesis, and tectonics. ②Lacustrine carbonate rocks have the logging facies characteristics of finger shape, circular arc shape, tooth shape, and U shape. At present, sedimentary microfacies are mainly used to identify reservoirs and analyze fluid properties. ③Seismic amplitude slices of lacustrine carbonate rocks often have typical beach core characteristics. The identification ability can be improved by frequency-division imaging and 90° phase shift. ④Lacustrine carbonate rocks are different from marine ones in many aspects, such as development age, spatial distribution, sedimentary facies characteristics, reservoir types and characteristics, and source rocks. The former developed in the Mesozoic and Cenozoic and was greatly affected by paleoclimate, while the latter developed in the Jurassic, Permian, Triassic, Ordovician, and Cambrian and was mainly under the influence of tides. ⑤Future prediction of lacustrine carbonate reservoirs should focus on faults and thin interbeds; sedimentary theories about lacustrine carbonate rocks are expected to undergo further development for a unified model of facies zone division to take shape; Deep learning methods based on big data analysis are an important future development direction.
Keywords:lacustrine carbonate rock  Jiyang Depression  sedimentary facies division  beach core  thin interbed  deep learning  
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