Unbiased estimation of intrinsic permeability with cumulants beyond the lognormal assumption

Vargas-Guzmán, J. A. (2009) Unbiased estimation of intrinsic permeability with cumulants beyond the lognormal assumption. SPE Journal, 14 4: 805-809. doi:10.2118/117439-PA


Author Vargas-Guzmán, J. A.
Title Unbiased estimation of intrinsic permeability with cumulants beyond the lognormal assumption
Journal name SPE Journal   Check publisher's open access policy
ISSN 1086-055X
1930-0220
Publication date 2009-12
Sub-type Article (original research)
DOI 10.2118/117439-PA
Volume 14
Issue 4
Start page 805
End page 809
Total pages 5
Place of publication Richardson, TX, U.S.A.
Publisher Society of Petroleum Engineers (SPE)
Language eng
Formatted abstract
The usual practice is to transform intrinsic-permeability data with logarithms to get a linear relation with porosity. Such transformations are known to produce undesired effects in 3D geocellular-models (i.e., smoothing data, elimination of extreme values, and biased estimates). Ideally, logarithms of data may lead to Gaussian distributions, which are easily handled by classical spatial statistics and linear collocated-cokriging. The exponential model from back-transformed linear regression "underestimates" permeability. If the lognormal assumption is not met, a second order correction may "overestimate" permeability. Transformations of permeability data may uncover the fact that a truly non-Gaussian statistical distribution cannot be avoided, and this brings complications to the modeling of permeability. Shortcomings of transforms for non-Gaussian cases may affect the quality of reservoir models for history match forcing to the use of arbitrary multipliers. Systematically biased flow predictions might be avoided by proper modeling of flow parameters including intrinsic permeability. Truly non-Gaussian modeling of permeability is developed in this paper to find a solution to these problems. The analysis starts by linking the exponential model to power transformations from Taylor series. Residual-terms (RTs) are introduced for correct back-transformation of estimates. An advanced result, for the truly non-Gaussian case, is that the RTs take the form of numerical higher order cumulants and not moments. RTs represent the contribution of spatial heterogeneous components that occur when spatial permeability varies beyond a pair-wise covariance or variogram. Results in this paper show that "higher-order cumulants" improve permeability estimates significantly in rocks with large dispersion of values dominated by higher permeabilities (e.g., shaley sands and laminated grainstone carbonates). This study has proposed new ways for spatial modeling of permeability, avoiding under or overestimations, and the proposed approach provides the basis for the use of cumulants for spatial higher-order estimation.
Keyword Transformations
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Sun, 03 Jan 2010, 00:00:11 EST