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Evaluation of volcanic reservoirs with the 'QAPM mineral model' using a genetic algorithm

机译:使用遗传算法的“QAPM矿物模型”评估火山储层

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摘要

Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups: Q(quartz), A (Alkaline feldspar), P (plagioclase), M (mafic) and F (feldspathoid). We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm (GA). The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples.
机译:在中国松辽盆地深处发现了含气火山储层。由于矿物成分复杂且矿物含量多变,很难为测井评估选择适当的解释参数。基于国际地质科学联合会给出的QAPF分类方案,提出了一种利用测井数据和遗传算法确定火山岩矿物含量的方法。根据QAPF方案,火山岩中的矿物分为五组:Q(石英)、A(碱性长石)、P(斜长石)、M(镁铁质)和F(长石)。我们提出了一个称为QAPM的模型,包括用于储层体积分析的孔隙度。密度、表观中子孔隙度、传输时间、伽马射线和体积光电截面指数的对数响应方程首先使用斯伦贝谢对数矿物参数手册中的矿物参数建立。然后使用遗传算法(GA)计算基质中四种矿物的体积。基于解释参数计算出的孔隙度可以与岩心孔隙度进行比较,论文中根据四种矿物含量进行QAPF分类给出的岩石名称与岩心样品化学分析得出的岩石名称相符。

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