地震多属性分析技术预测和评价盐下碳酸盐岩储层厚度分布  

Seismic Multi-attributes Analysis Method to Predict and Evaluate Thickness Distribution of Carbonate Reservoir in Pre-salt

作　　者：张勇刚 范国章 王红平 王朝锋 左国平 杨柳 刘艳红 ZHANG Yong-gang;FAN Guo-zhang;WANG Hong-ping;WANG Chao-feng;ZUO Guo-ping;YANG Liu;LIU Yan-hong(Petrochina Hangzhou Research Institute of Geology,Hangzhou,310023,China)

机构地区：[1]中国石油杭州地质研究院,杭州310023

出　　处：《盐湖研究》2022年第3期72-82,共11页Journal of Salt Lake Research

基　　金：国家科技重大专项“海外海域油气地质条件与关键评价技术研究”(2019D-4309)。

摘　　要：某海上盐下L区火成岩发育、碳酸盐岩储层厚度预测难度大,地震单属性分析已不能满足研究需求,本文尝试利用地震多属性分析技术进行了储层参数预测。通过属性与已钻井储层参数交会图分析进行地震属性集优选,通过主成分分析(PCA法)和K-L变换进行地震属性压缩,通过人工神经网络技术进行全区定量预测储层厚度,计算结果与实钻井误差范围不超过5.5%,大部分井多属性方法预测精度明显高于单属性方法,储层厚度平面分布特征与地质规律吻合。实践结果表明此方法在原理和实际应用上都是可行的,能够有效地提高储层定量预测精度,为探明油田储层分布特征和后续部署开发井网方案提供了参考。Seismic single attribute to predict reservoir’s parameters can’t satisfy demand for pre-salt carbonate reservoir interbedded with some igneous rocks,the paper attempts to predict reservoir parameters using seismic multi-attributes analysis technologies for improving accuracy and reliability.Insensitive attributes are expelled by manual experience through the crossplots between surface attributes and reservoir parameters from drilling wells,the remaining seismic attributes or groups are the optimum combination to resolve desired questions;The rest of seismic attributes maybe be too much and needed to decrease dimension,principal component analysis and K-L transformation are used to compress seismic attributes;artificial neural network algorithm is used to attribute pattern recognition,the reservoir parameters in the whole region are quantitatively predicted or estimated.In view of some questions such as igneous rock development、pre-salt carbonate reservoir prediction difficulty in Block L offshore,pre-salt carbonate reservoir thickness is quantitatively computed with seismic multi-attributes analysis technology,It is proved that the error is less than 5.5%between results and verified wells,the prediction accuracy is obviously higher than using one single attribute method,and reservoir thickness distribution matches geologic sedimentation pattern.The practical results show that this method is feasible in both concept and practical application from the case study,which can efficiently improve the accuracy of reservoir quantitative prediction,and provide suggestions for the exploration of reservoir distribution characteristics and subsequent deployment of development well network scheme.

关 键 词：地震多属性 属性优化 属性压缩 主成分分析 神经网络 盐下碳酸盐岩 储层厚度预测 

分 类 号：P631.42[天文地球—地质矿产勘探]