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Fracture characterization and estimation of fracture porosity of naturally fractured reservoirs with no matrix porosity using stochastic fractal models

机译:随机分形模型在无基质孔隙度的天然裂缝性储层的裂缝表征和裂缝孔隙度估算中的应用

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摘要

Determining fracture characteristics at the laboratory scale is a major challenge. It isknown that fracture characteristics are scale dependent; as such, the minimum samplesize should be deduced in order to scale to reservoir dimensions. The main factoraffecting mechanical and hydrological characteristics of natural fractures is aperturedistribution, which is a function of scale and confining pressure, rather than roughnessof one fracture surface. Scale and pressure dependencies of artificial and naturalfractures were investigated in this study using an X-Ray CT Scanner. Fractal dimension,D, and amplitude parameter, A, of fracture aperture approaches a constant value withincreased sampling area, similar to the behavior of fracture roughness. In addition, bothparameters differ under different confining pressures for a reference sampling area.Mechanical properties of fracture-fracture deformation behavior and fracture normalstiffness were obtained from CT scan data as well.Matrix porosity is relatively easy to measure and estimate compared to fractureporosity. On the other hand, fracture porosity is highly heterogeneous and very difficult to measure and estimate. When matrix porosity of naturally fractured reservoirs (NFR)is negligible, it is very important to know fracture porosity to evaluate reservoirperformance. Since fracture porosity is highly uncertain, fractal discrete fractal network(FDFN) generation codes were developed to estimate fracture porosity. To reflect scaledependent characteristics of fracture networks, fractal theories are adopted. FDFNmodeling technique enables the systematic use of data obtained from image log andcore analysis for estimating fracture porosity. As a result, each fracture has its ownfracture aperture distribution, so that generated FDFN are similar to actual fracturesystems. The results of this research will contribute to properly evaluating the fractureporosity of NFR where matrix porosity is negligible.
机译:在实验室规模上确定断裂特性是一项重大挑战。众所周知,断裂特性是与尺度有关的。因此,应推算出最小样本量,以适应​​储层尺寸。影响天然裂缝的力学和水文特征的主要因素是孔径分布,它是水垢和围压的函数,而不是一个裂缝表面的粗糙度。在这项研究中,使用X射线CT扫描仪研究了人工和天然裂缝的规模和压力依赖性。裂缝孔径的分形维数D和振幅参数A接近增加的采样区域内的恒定值,类似于裂缝粗糙度的行为。此外,两个参数在参考采样区域的不同围压下均不同,并且还从CT扫描数据中获得了断裂-断裂变形行为和断裂法向刚度的力学性能,与断裂孔隙度相比,基质孔隙度相对易于测量和估算。另一方面,裂缝孔隙度高度不均一,很难测量和估计。当天然裂缝性储层(NFR)的基质孔隙度可忽略不计时,了解裂缝孔隙度对评估储层性能非常重要。由于裂缝孔隙度具有高度不确定性,因此开发了分形离散分形网络(FDFN)生成代码来估算裂缝孔隙度。为了反映裂缝网络的尺度相关特性,采用了分形理论。 FDFNmodeling技术可以系统地使用从图像测井和岩心分析获得的数据来估算裂缝孔隙度。结果,每个裂缝都有其自己的裂缝孔径分布,因此生成的FDFN与实际裂缝系统相似。这项研究的结果将有助于适当评估基质孔隙度可忽略不计的NFR的裂缝孔隙度。

著录项

  • 作者

    Kim Tae Hyung;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 en_US
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