提高伊朗西南部碳酸盐岩储层X场裂缝建模精度

IF 1 Q4 GEOSCIENCES, MULTIDISCIPLINARY
S. R. M. Madani, H. Hassani, B. Tokhmechi
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引用次数: 0

摘要

裂缝建模是裂缝性储层研究的重要步骤之一。由于成像测井的成本很高,而且研究区域的大多数井都没有成像测井,因此通常会尝试使用其他可用数据来检测裂缝。本文试图探讨岩石的岩性与裂缝之间的关系。为此,使用图像、中子、密度、岩石密度和NGS测井来模拟岩性。基于这一特征,将研究区划分为6个均匀性部分,并在每个部分确定裂缝概率,以提高裂缝建模的精度。近年来,一种智能方法已被证明是模拟复杂和非线性现象的有效工具。本文将神经网络方法应用于裂缝建模。结果表明,在不增加图像测井成本的情况下,基于岩性研究的油田划分将使研究区域的裂缝建模精度提高7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the accuracy of fracture modeling in carbonate reservoirs X-field in SW of Iran
Fracture modeling is one of the most important steps in the study of fractured reservoirs. Due to the high cost of imaging logs and their absence in most wells of the study area, it is often attempted to use other available data to detect fractures. This paper attempts to investigate the relationship between the lithology and fractures of rocks. For this purpose, the Image, Neutron, Density, Litho-density, and NGS logs have used to simulate the lithology. Based on this feature, the studied area was divided into six homogeneity part, and the fracture probability was determined in each section to improve the accuracy of fracture modeling. Recently, an intelligent method has been proven as an efficient tool for modeling complex and non-linear phenomena. In this paper, neural network methods has been used in fracture modeling. The results show that the division of the field based on lithological studies will  improves the accuracy of fracture modeling in the studied area up to 7 percent without increasing the cost of image logging.
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来源期刊
Iranian Journal of Earth Sciences
Iranian Journal of Earth Sciences GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
1.40
自引率
12.50%
发文量
0
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