西南石油大学学报（自然科学版）2016，Vol.38Issue(3)：75-82,8.DOI:10.11885/j.issn.1674-5086.2014.04.13.03
基于敏感地震属性波形分类的流体预测研究
A Study on Fluid Prediction Based on the Classification of Sensitive Seismic Attributes
摘要
Abstract
The ability to identify gas-bearing and water-bearing sands of nine fluid identification factors has been compared, and the results show that the High-Sensitivity-Fluid-Identification-Factor has a strong ability to identify. The neural network and the Principal-Component-Analysis-neural-network are applied to high-quality 3-D data of HSFIF to perform the waveform analysis during the gas bearing interval in study area L of basin-S and good mapping effect has been achieved. The facies maps were analyzed and compared with the logging′interpretation. It proves the application of the PCA-neural network method can greatly reduce the difficulty of seismic facies interpretation of the map. In this paper,the application range of waveform classification is extended from seismic-sedimentary-facies analysis and reservoir prediction to the analysis and processing of the fluid factors in the target layers,thereby we can predict the fluid in layers. It is a new kind of valuable complement to fluid identification and prediction by using fluid factor only in profile and slice. It is the first time that the waveform classification techniques has been applied to fluid prediction. We believe that the method along with the results of other explanations has a guiding significance to reduce exploration risk and enhance drilling success rate.关键词
地震属性/波形分类/主成份分析/流体因子/流体预测Key words
seismic attribute/waveform classification/PCA/fluid factor/fluid prediction分类
能源科技引用本文复制引用
赵忠泉,贺振华,万晓明,帅庆伟..基于敏感地震属性波形分类的流体预测研究[J].西南石油大学学报（自然科学版）,2016,38(3):75-82,8.基金项目
国家自然科学基金（40774064）。 （）
