大庆长垣北部高台子油层沉积微相研究
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摘要
综合利用测井、地质录井、岩芯分析数据等各种地质资料，采用宏观研究与微观分析相结合的方法，对长垣北部高台子油层砂岩储集层，进行了沉积特征研究，用两种不同方法研制了沉积微相自动识别程序。
通过研究大量测井、录井资料和岩芯资料，依据“旋回对比，分级控制”的原则，把研究区高台子油层划分为GⅠ、GⅡ、GⅢ、GⅣ四个砂层组。通过对研究区岩性、沉积构造、古生物、沉积序列、岩石组合及测井曲线特征进行研究的基础上，认为研究区高台子油层属于三角洲前缘亚相沉积，并将研究区沉积微相划分为：水下分流河道、河口坝、水下分流河道间、水下决口扇、水下天然堤、远砂坝、席状砂等七种微相类型。精细描述了各种沉积微相的特征。
利用测井曲线的数字化资料，进行岩性剖面恢复，通过取芯井建立沉积微相模式及测井相模式，提取测井曲线形态要素参数，用Bayes判别法进行沉积微相自动识别程序。同时还研制了利用神经网络进行沉积微相自动识别程序。利用此程序对研究区进行单井沉积微相的分析及平面相研究。
On the basis of the integrated application of well logging , mud logging information and core analysis data, and using the method from the microcosm to the macrocosm analysis, this paper has studied the sedimentary characteristics of the sandstone oil bearing of the Gaotaizi reservoir in the north part of the Daqing placantieline, the program of automatic identification sedimentary microfacies with two different methods has been compiled.
According to the abundance well logging ,mud logging and core analysis data , Gaotaizi reservoir has been classified into four sand group with the principle of cycle comparison and classified control .Based on the detailed study of lithologic character, sedimentary structure, paleontologic fossils, depositional sequence, rock association and logging data, the microfacies of the studied area are divided into such seven types: subaqueous distributary channel microfacies;river mouth bar microfacies; subaqueous interdistributary channel microfacies; subaqueous crevasse channel microfacies; subaqueous natural barrier microfacies; distal bar microfacies;delta front sheet sand microfacies.The characteristic of each sedimentary microfacies are described detailedly.
Using digital logging data of logging curves to resume lithology section, based on single well sedimentary facies analysis of cored well, typical sedimentary microfacies and the corresponding electrofacies models are established. Each after extracting the essential factors of logging curves, each logging microfacies recognize model are set up to automatically identify sedimentary microfacies by the method of Bayes identification and neural networks technical. The program has been compiled by Visual Basic programming language, based on which the analysis of single well sedimentary microfacies and the study of the plane sedimentary facies are acomplished finally.
通过研究大量测井、录井资料和岩芯资料，依据“旋回对比，分级控制”的原则，把研究区高台子油层划分为GⅠ、GⅡ、GⅢ、GⅣ四个砂层组。通过对研究区岩性、沉积构造、古生物、沉积序列、岩石组合及测井曲线特征进行研究的基础上，认为研究区高台子油层属于三角洲前缘亚相沉积，并将研究区沉积微相划分为：水下分流河道、河口坝、水下分流河道间、水下决口扇、水下天然堤、远砂坝、席状砂等七种微相类型。精细描述了各种沉积微相的特征。
利用测井曲线的数字化资料，进行岩性剖面恢复，通过取芯井建立沉积微相模式及测井相模式，提取测井曲线形态要素参数，用Bayes判别法进行沉积微相自动识别程序。同时还研制了利用神经网络进行沉积微相自动识别程序。利用此程序对研究区进行单井沉积微相的分析及平面相研究。
On the basis of the integrated application of well logging , mud logging information and core analysis data, and using the method from the microcosm to the macrocosm analysis, this paper has studied the sedimentary characteristics of the sandstone oil bearing of the Gaotaizi reservoir in the north part of the Daqing placantieline, the program of automatic identification sedimentary microfacies with two different methods has been compiled.
According to the abundance well logging ,mud logging and core analysis data , Gaotaizi reservoir has been classified into four sand group with the principle of cycle comparison and classified control .Based on the detailed study of lithologic character, sedimentary structure, paleontologic fossils, depositional sequence, rock association and logging data, the microfacies of the studied area are divided into such seven types: subaqueous distributary channel microfacies;river mouth bar microfacies; subaqueous interdistributary channel microfacies; subaqueous crevasse channel microfacies; subaqueous natural barrier microfacies; distal bar microfacies;delta front sheet sand microfacies.The characteristic of each sedimentary microfacies are described detailedly.
Using digital logging data of logging curves to resume lithology section, based on single well sedimentary facies analysis of cored well, typical sedimentary microfacies and the corresponding electrofacies models are established. Each after extracting the essential factors of logging curves, each logging microfacies recognize model are set up to automatically identify sedimentary microfacies by the method of Bayes identification and neural networks technical. The program has been compiled by Visual Basic programming language, based on which the analysis of single well sedimentary microfacies and the study of the plane sedimentary facies are acomplished finally.
引文
[1]施尚明，张树林等．大庆长垣北部地热资源评价研究．大庆石油学院，2001，1—10
[2]陈碧玉．油矿地质学．石油工业出版社，1993，127-155
[3]尹寿鹏，王贵文．测井沉积学研究综述。地球科学进展，1999，14(5)，440-444
[4])O.Serra,Sedimentary environments from wireling logs,Schlumberger,1985
[5]李元元等．自然咖玛数字差别法研究沉积微相．沉积学报，1991，9(4)
[6]王仁锋，利用测井曲线形态特征定量判别沉积相．地球科学，1991，16(3)，303-309
[7]徐开志，唐勇等．用计算机实现自动判别沉积微相方法．新疆石油地质，1999，20(2)，127-129
[8]大庆油田石油地质志编写组．中国石油地质志卷二(上)大庆油田．北京：石油工业出社，1993，55-335
[9]刘丁曾等．大庆多层砂岩油田开发．北京：石油工业出版社，1996，9-30
[10]钟其权．陆相沉积的油层对比方法．北京：石油工业出版社，1985，1-8
[11]刘泽容等．油藏描述原理与方法技术．北京：石油工业出版社，1993，1-4
[12]裘亦楠，陈子琪．油藏描述手册．北京：石油工业出版社，1996，160-178
[13]H．G．里丁．沉积环境和相．科学出版社，1991，133-155
[14]孙永传，李惠生．碎屑岩沉积相和沉积环境，地质出版社，1986，151-177
[15]冯增昭．沉积岩石学(下)．北京：石油工业出版社，1993，138-155
[16]郭荣坤，王贵文，唐为清．测井沉积学解释计算机辅助系统．北京：石油工业出版社，79-106
[17]程玉群，李秀荣，刘铁桩．测井曲线自动分层技术及在杏北地区小层划分中的应用．断块油气田，7(2)，23-25
[18]卢新卫，金章东．前馈神经网络的岩性识别方法．石油与天然气地质，1999，20(1)，82-84
[19]朱爱丽．自动测井相分析及其在油藏描述中的应用．测井在油藏描述中的应用，北京：石油工业出版社，1992，1-11
[20]雍世和．用Bayes判别法定量识别沉积微相．测井技术，1991，15(3)，22-27
[21]焦翠华等．神经网络和分形几何方法在识别测井沉积微相中的应用．沉积学报，15(3)，62-66
[22]蔺景龙，许少华等．沉积微相测井识别．大庆石油地质与开发．1997，16(2)，71-74
[23]冉启全等．神经网络模式识别沉积微相．沉积学报，1996，14(增刊)，186-190
[24]胡俊．模式识别在测井资料划分沉积相中的应用研究．勘探与开发，1999，19(4)，34-36
[25]赵翰卿，付志国．应用精细地质研究准确鉴别古代河流砂体．石油勘探与开发，1995，22(2)
[26]吴胜和，熊琦华等．油气储层地质学．北京：石油工业出版社，1998，216-235
[27]尹寿鹏，王贵文，测井沉积学综述．地球科学进展，1999，14(5)
[28]赵翰卿等．大型河流—三角洲沉积储层精细描述方法．石油学报，北京：2000，21(4)，109-113
[29]陆明德，田时芸．石油天然气数学地质．中国地质大学出版社，1991，134-151
[30]周继承等．人工神经网络．北京：科学普及出版社，1993，47-66
[31]裘亦楠．石油开发地质方法(一)、(二)、(三)．石油勘探与开发，1996，23(2) 43-47，23(3)，48-51，23(4)，42-45
[32]王衡鉴，曹文富．松辽湖盆白垩纪沉积微相模式．杨万里．松辽陆相盆地石油地质．北京：石油工业出版社，1985，127-140
[33]W．A．Ruoff．根据自然电位曲线利用计算机解释砂岩沉积环境的方法．陶淑娴．测井地质学在油气勘探中的应用(译文集)．北京：石油工业出版社，1993，171-179
[34]刘业新，王继贤．综合多种信息的沉积环境自动解释系统．测井在油藏描述中的应用．北京：石油工业出版社，1992，24-28
[35]赵瑾芳，李英辉，谢心宏．沉积相自动判别程序及其地质应用，测井在油藏描述中的应用．北京：石油工业出版社，1992，36-46
[36]张兴金，马世忠．低渗透率砂岩油田开发地质分析．黑龙江科学技术出版社，1994，48-67
[37]雍世和，陈钢花．自动测井相分析．石油学报，1990，11(4)，41-49
[38]张一伟等．陆相油藏描述．北京：石油工业出版社，1997，178-234
[39]Busch JM, Determination of lithology from well logs by statistical analysis, SPE, 1985,14(301)
[40]Bucheb. JA, Evans. HB, Some application of methods used in electrofacies identification ,The Log Analyst, 1994,35(1):14-26
[41]Doveton. JH, Prensky SE, Review paper:Geological application of wireline logs:a synopsis of developments and trends, The Log Analyst, 1992,33(3):286-303
[42]Elphick RY, A determ inistic method for calculating facies from wireline logs, The Log Analyst, 1996,37(2):14-25
[43]Chen h. Sedimentary microfacies of reservoir sandbodied from Dujiatai oil in block human_50.J jianghan petrol inst, 1997,19(2),13-17
[44]Guo s, Kuang 1, Du x. The study of sedimentary microfacies based on integrated well logs in the Yingtai region:an example from the second sandstone section in the third member of the Qingshankou formation. Sediment faxies paleogeogr, 1994,1(1),68-80
[45]Pierre Delfiner, Olivier peyret, Oberto Serra. Automatic determination of Lithology From well Logs. SPE Formation Evaluation, September 1987,303-309
[46]Wu j, Liu K. Study on the electrofacies, lithofacies and sedimentary microfacies of carbonate formation, well logging technol, 1993,17(3),171-182
[47]Kingsolver J. Lithology interpretation, bit selection optimized operating parameters using gamma sonic logs. Cade/caodc spring drilling conf, Calgary, Can, 1995. Proc pap, 1995.95-100
[48]Yong s. Quantitative discrimination of sedimentary microfacies with bayes discriminant analysis. Well logging technol, 1995,19(1),22-27
[49]Ran q. Identification of sedimentary microfacies with an artificial neural networks model. Petrol explor develop, 1995,22(2),59-63
[50]Chen g h. Study of sedimentary microfacies of river facies and electrofacies. Well logging technol, 1996,20(5),335-340
[51]Chen g, Shen h, Yong S. Automatic analysis of lithology and sedimentary microfacies by menas of mathematica statistics.J univ petrol China, 1996,20(4),26-29
[52]Rogers, S.J.,Fang, J.H.,Karr, C.L.,and Stanley, D.A.,Determination of lithology from well log using a neural networks, AAPG Bulletin, 1992,76(5),731-739
[2]陈碧玉．油矿地质学．石油工业出版社，1993，127-155
[3]尹寿鹏，王贵文．测井沉积学研究综述。地球科学进展，1999，14(5)，440-444
[4])O.Serra,Sedimentary environments from wireling logs,Schlumberger,1985
[5]李元元等．自然咖玛数字差别法研究沉积微相．沉积学报，1991，9(4)
[6]王仁锋，利用测井曲线形态特征定量判别沉积相．地球科学，1991，16(3)，303-309
[7]徐开志，唐勇等．用计算机实现自动判别沉积微相方法．新疆石油地质，1999，20(2)，127-129
[8]大庆油田石油地质志编写组．中国石油地质志卷二(上)大庆油田．北京：石油工业出社，1993，55-335
[9]刘丁曾等．大庆多层砂岩油田开发．北京：石油工业出版社，1996，9-30
[10]钟其权．陆相沉积的油层对比方法．北京：石油工业出版社，1985，1-8
[11]刘泽容等．油藏描述原理与方法技术．北京：石油工业出版社，1993，1-4
[12]裘亦楠，陈子琪．油藏描述手册．北京：石油工业出版社，1996，160-178
[13]H．G．里丁．沉积环境和相．科学出版社，1991，133-155
[14]孙永传，李惠生．碎屑岩沉积相和沉积环境，地质出版社，1986，151-177
[15]冯增昭．沉积岩石学(下)．北京：石油工业出版社，1993，138-155
[16]郭荣坤，王贵文，唐为清．测井沉积学解释计算机辅助系统．北京：石油工业出版社，79-106
[17]程玉群，李秀荣，刘铁桩．测井曲线自动分层技术及在杏北地区小层划分中的应用．断块油气田，7(2)，23-25
[18]卢新卫，金章东．前馈神经网络的岩性识别方法．石油与天然气地质，1999，20(1)，82-84
[19]朱爱丽．自动测井相分析及其在油藏描述中的应用．测井在油藏描述中的应用，北京：石油工业出版社，1992，1-11
[20]雍世和．用Bayes判别法定量识别沉积微相．测井技术，1991，15(3)，22-27
[21]焦翠华等．神经网络和分形几何方法在识别测井沉积微相中的应用．沉积学报，15(3)，62-66
[22]蔺景龙，许少华等．沉积微相测井识别．大庆石油地质与开发．1997，16(2)，71-74
[23]冉启全等．神经网络模式识别沉积微相．沉积学报，1996，14(增刊)，186-190
[24]胡俊．模式识别在测井资料划分沉积相中的应用研究．勘探与开发，1999，19(4)，34-36
[25]赵翰卿，付志国．应用精细地质研究准确鉴别古代河流砂体．石油勘探与开发，1995，22(2)
[26]吴胜和，熊琦华等．油气储层地质学．北京：石油工业出版社，1998，216-235
[27]尹寿鹏，王贵文，测井沉积学综述．地球科学进展，1999，14(5)
[28]赵翰卿等．大型河流—三角洲沉积储层精细描述方法．石油学报，北京：2000，21(4)，109-113
[29]陆明德，田时芸．石油天然气数学地质．中国地质大学出版社，1991，134-151
[30]周继承等．人工神经网络．北京：科学普及出版社，1993，47-66
[31]裘亦楠．石油开发地质方法(一)、(二)、(三)．石油勘探与开发，1996，23(2) 43-47，23(3)，48-51，23(4)，42-45
[32]王衡鉴，曹文富．松辽湖盆白垩纪沉积微相模式．杨万里．松辽陆相盆地石油地质．北京：石油工业出版社，1985，127-140
[33]W．A．Ruoff．根据自然电位曲线利用计算机解释砂岩沉积环境的方法．陶淑娴．测井地质学在油气勘探中的应用(译文集)．北京：石油工业出版社，1993，171-179
[34]刘业新，王继贤．综合多种信息的沉积环境自动解释系统．测井在油藏描述中的应用．北京：石油工业出版社，1992，24-28
[35]赵瑾芳，李英辉，谢心宏．沉积相自动判别程序及其地质应用，测井在油藏描述中的应用．北京：石油工业出版社，1992，36-46
[36]张兴金，马世忠．低渗透率砂岩油田开发地质分析．黑龙江科学技术出版社，1994，48-67
[37]雍世和，陈钢花．自动测井相分析．石油学报，1990，11(4)，41-49
[38]张一伟等．陆相油藏描述．北京：石油工业出版社，1997，178-234
[39]Busch JM, Determination of lithology from well logs by statistical analysis, SPE, 1985,14(301)
[40]Bucheb. JA, Evans. HB, Some application of methods used in electrofacies identification ,The Log Analyst, 1994,35(1):14-26
[41]Doveton. JH, Prensky SE, Review paper:Geological application of wireline logs:a synopsis of developments and trends, The Log Analyst, 1992,33(3):286-303
[42]Elphick RY, A determ inistic method for calculating facies from wireline logs, The Log Analyst, 1996,37(2):14-25
[43]Chen h. Sedimentary microfacies of reservoir sandbodied from Dujiatai oil in block human_50.J jianghan petrol inst, 1997,19(2),13-17
[44]Guo s, Kuang 1, Du x. The study of sedimentary microfacies based on integrated well logs in the Yingtai region:an example from the second sandstone section in the third member of the Qingshankou formation. Sediment faxies paleogeogr, 1994,1(1),68-80
[45]Pierre Delfiner, Olivier peyret, Oberto Serra. Automatic determination of Lithology From well Logs. SPE Formation Evaluation, September 1987,303-309
[46]Wu j, Liu K. Study on the electrofacies, lithofacies and sedimentary microfacies of carbonate formation, well logging technol, 1993,17(3),171-182
[47]Kingsolver J. Lithology interpretation, bit selection optimized operating parameters using gamma sonic logs. Cade/caodc spring drilling conf, Calgary, Can, 1995. Proc pap, 1995.95-100
[48]Yong s. Quantitative discrimination of sedimentary microfacies with bayes discriminant analysis. Well logging technol, 1995,19(1),22-27
[49]Ran q. Identification of sedimentary microfacies with an artificial neural networks model. Petrol explor develop, 1995,22(2),59-63
[50]Chen g h. Study of sedimentary microfacies of river facies and electrofacies. Well logging technol, 1996,20(5),335-340
[51]Chen g, Shen h, Yong S. Automatic analysis of lithology and sedimentary microfacies by menas of mathematica statistics.J univ petrol China, 1996,20(4),26-29
[52]Rogers, S.J.,Fang, J.H.,Karr, C.L.,and Stanley, D.A.,Determination of lithology from well log using a neural networks, AAPG Bulletin, 1992,76(5),731-739