【作者】 文慧俭；

【导师】 施尚明；

【作者基本信息】 大庆石油学院 ， 矿产普查与勘探， 2002， 硕士

【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。更多还原

【Abstract】 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.更多还原