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首页> 外文期刊>Journal of natural gas science and engineering >Fault and non-fault areas detection based on seismic data through min/max autocorrelation factors and fuzzy classification
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Fault and non-fault areas detection based on seismic data through min/max autocorrelation factors and fuzzy classification

机译：通过最小/最大自相关因子和模糊分类的地震数据进行故障和非故障区域检测

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摘要

Accurate detection of faulted and non-faulted areas is significant step in oil and gas exploration and production. Different methods such as Discrete Fracture Network Detection, seismic attribute study, ant tracking, and meta-attributes are used in fault detection in seismic data. The method proposed in this research is a geostatistical approach which is based on combination of Minimum and maximum Autocorrelation Factor (MAF) and fuzzy logic applied on a set of seismic attributes. It is common in advanced seismic data interpretation to have multi variables of interest (seismic attributes), those which are spatially correlated. MAF approach is a geostatistical technique to obtain uncorrelated attributes by modifying the coordination axis in two steps and reducing the dimensions without losing the information. In order to develop fuzzy logic method to predict the faulted and non-faulted areas based on combination of normalized factors, the low value of faults and fractures in the seismic attributes is eliminated and attributes which have common point on the faulted or non-faulted areas, are superimposed on a fuzzy system and introduced as faults. According to the size of membership degree between symptoms and causations to detect and eliminate faults of gas turbine. The results have shown this fuzzy mathematics method has reliable and suitable detection of faults containing gas and oil in actual and complex environment. (C) 2015 Elsevier B.V. All rights reserved.
机译：准确检测故障和非故障区域是油气勘探和生产中的重要一步。在地震数据的故障检测中使用了不同的方法，例如离散断裂网络检测，地震属性研究，蚂蚁跟踪和元属性。本研究提出的方法是一种地统计学方法，该方法基于最小和最大自相关因子（MAF）以及应用于一组地震属性的模糊逻辑的组合。在高级地震数据解释中，通常具有多个感兴趣的变量（地震属性），这些变量在空间上是相关的。 MAF方法是一种地统计技术，它通过分两步修改坐标轴并减小尺寸而不会丢失信息来获取不相关的属性。为了发展基于归一化因子组合的模糊逻辑方法来预测断层和非断层区域，消除了地震属性中断层和裂缝的低值，并在断层或非断层区域具有共同点的属性，被叠加在模糊系统上，并作为故障引入。根据症状和因果关系之间的隶属度大小，检测并消除燃气轮机故障。结果表明，该模糊数学方法在实际和复杂环境下具有可靠，合适的含油气故障检测能力。 （C）2015 Elsevier B.V.保留所有权利。

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