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Volume 32 Issue 4
Aug 2021
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Shengli Li, Y.Zee Ma, Ernest Gomez. Importance of Modeling Heterogeneities and Correlation in Reservoir Properties in Unconventional Formations: Examples of Tight Gas Reservoirs. Journal of Earth Science, 2021, 32(4): 809-817. doi: 10.1007/s12583-021-1430-2
Citation: Shengli Li, Y.Zee Ma, Ernest Gomez. Importance of Modeling Heterogeneities and Correlation in Reservoir Properties in Unconventional Formations: Examples of Tight Gas Reservoirs. Journal of Earth Science, 2021, 32(4): 809-817. doi: 10.1007/s12583-021-1430-2

Importance of Modeling Heterogeneities and Correlation in Reservoir Properties in Unconventional Formations: Examples of Tight Gas Reservoirs

doi: 10.1007/s12583-021-1430-2
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  • Corresponding author: Shengli Li, slli@cugb.edu.cn
  • Received Date: 27 Apr 2020
  • Accepted Date: 03 Feb 2021
  • Publish Date: 16 Aug 2021
  • We present lithofacies classifications for a tight gas sandstone reservoir by analyzing hierarchies of heterogeneities. We use principal component analysis (PCA) to overcome the two level of heterogeneities, which results in a better lithofacies classification than the traditional cutoff method. The classical volumetric method is used for estimating oil/gas in-place resources in the petroleum industry since its inception is not accurate because it ignores the heterogeneities of and correlation between the petrophysical properties. We present the importance and methods of accounting for the heterogeneities of and correlation between petrophysical properties for more accurate hydrocarbon volumetric estimations. We also demonstrate the impacts of modeling the heterogeneities and correlation in porosity and hydrocarbon saturation for hydrocarbon volumetric estimations with a tight sandstone gas reservoir. Furthermore, geoscientists have traditionally considered that small-scale heterogeneities only impact subsurface fluid flow, but not impact the hydrocarbon resource volumetric estimation. We show the importance of modeling small-scale heterogeneities using fine cell size in reservoir modeling of unconventional resources for accurate resource assessment.

     

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