A Study on Fluid Prediction Based on the Classification of Sensitive#br#
Seismic Attributes

doi:10.11885/j.issn.1674-5086.2014.04.13.03

Abstract

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.

Key words: seismic attribute, waveform classification, PCA, fluid factor, fluid prediction

CLC Number:

TE132
P631

ZHAO Zhongquan1*, HE Zhenhua2, WAN Xiaoming1, SHUAI Qingwei1. A Study on Fluid Prediction Based on the Classification of Sensitive#br# Seismic Attributes[J]. 西南石油大学学报(自然科学版), 2016, 38(3): 75-81.

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A Study on Fluid Prediction Based on the Classification of Sensitive#br# Seismic Attributes

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