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气测资料在低孔低渗气层识别中的应用

发表时间:2012-03-31  浏览量:1105  下载量:507
全部作者: 程道解,王慧,令狐松
作者单位: 中国石油集团测井有限公司油气评价中心;中国石油集团测井有限公司长庆事业部解释中心
摘 要: L-N地区气、水层测井响应差异较小,用常规解释图版很难将其区分。尝试利用气测资料研究该区的气、水层特征,并调研比较常用的油气层气测资料解释方法。建立气测全烃组分与气、水层对应关系,并利用人工神经网络(artificial neural network, ANN)进行训练和识别。结果发现:人工神经网络在该区的气、水层识别中效果显著,可以较好地弥补该区测井油气层识别工作的不足。
关 键 词: 矿产普查与勘探;气测资料;全烃;气层识别;人工神经网络
Title: Application of gasometry data in identification of gas bearing reservoirs in low porosity & permeability reservoirs
Author: CHENG Daojie, WANG Hui, LINGHU Song
Organization: Hydrocarbon Evaluation Center, China National Petroleum Corporation Logging; Interpretation Center, Changqing Business Division, China National Petroleum Corporation Logging
Abstract: Difference in logging response between gas bearing reservoirs and water bearing reservoirs is little in L-N area, which makes it is impossible to distinguish them with normal interpretation method. This paper attempts to find out the features of gas bearing reservoirs and water bearing reservoirs with gasometry data. Frequently-used interpretation methods for hydrocarbon bearing reservoirs with gasometry data are investigated and surveyed. The mapping relationship between gaseous hydrocarbon of gasometry and gas/water bearing reservoirs is set up, while training and identification is done with artificial neural network (ANN) consequently. It works effectively in identification of gas/water bearing reservoirs, which complements the identification work of the hydrocarbon bearing reservoirs with logging data.
Key words: mineral resource prospecting and exploration; gasometry data; gaseous hydrocarbon; identification of gas bearing reservoir; artificial neural network
发表期数: 2012年3月第6期
引用格式: 程道解,王慧,令狐松. 气测资料在低孔低渗气层识别中的应用[J]. 中国科技论文在线精品论文,2012,5(6):555-558.
 
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