成像测井在灯影组微生物岩岩相识别中的应用

The Application of Image Logging in the Identification of Microbialite Facies in Dengying Formation, Sichuan Basin

田瀚 张建勇 李昌 李文正 姚倩颖

TIANHan ZHANGJianyong LIChang LIWenzheng YAOQianying

中国石油杭州地质研究院, 浙江 杭州 310023 中国石油勘探开发研究院四川盆地研究中心, 四川 成都 610041 中国石油天然气集团公司碳酸盐岩储集层重点实验室, 浙江 杭州 310023

Hangzhou Research Institute of Geology, PetroChina, Hangzhou, Zhejiang 310023, China Research Institute of Sichuan Basin, PetroChina Research Institute of Petroleum Exploration & Development, Chengdu, Sichuan 610041, China CNPC Key Laboratory of Carbonate Reservoirs, Hangzhou, Zhejiang 310023, China

岩相识别是沉积储层研究的基础，针对未取芯井开展测井岩相识别工作至关重要。对于四川盆地震旦系灯影组碳酸盐岩地层而言，由于经历过多期成岩改造作用，使得不同岩相的常规测井响应特征区分度较低，准确识别难度大。为了建立有效的测井岩相识别方法，在前人岩石分类的基础上，通过选取多口岩芯、薄片和测井等资料齐全的代表性钻井作为关键井，在充分发挥成像测井优势基础上，明确不同岩相典型成像特征，建立成像测井相岩相的转换模型，并采用多点地质统计学方法开展成像测井全井眼图像处理，提取图像典型特征，结合所建立的岩相转换模型，开展全井段岩相识别，并推广应用于研究区其他未取芯井。通过实际效果验证表明，相比常规测井，基于成像测井所建立的岩相识别方法岩性识别准确率更高，能为后续沉积微相和储层研究提供有力支撑。

The lithology identification is the basis of study of the sedimentary facies and reservoirs, and it is very important to identify well logging lithofacies for uncored wells. The carbonate of Dengying Formation of Sinian system in Sichuan Basin, has undergone strong digenesis that led to the low discrimination for log response characteristics of different lithofacie, poses great challenge for conventional logs to identify carbonate lithofacies. In order to establish an effective identification method of log facies, on the basis of previous classification, the wells with complete core, thin section and logging data of the fourth Member of Dengying Formation in Gaoshiti-Moxi Area were selected as key wells. We conduct fine description of cores, extract the different typical imaging features of lithofacies, and establish the transformation model of the image logging facies and lithofacies. Finally, we use multi-point geostatistics method to carry out the whole wellbole imaging process. We extract image features, combine the established lithofacies identification model to carry out the lithofacies identification, then apply the method sto other uncored wells in the study area. The results show that the lithofacies identification method based on image logging has a high identification rate, which can provide a strong support for the subsequent studies of sedimentary microfacies and reservoir development mechanism.

10.11885/j.issn.1674-5086.2019.04.14.01