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Premium Combining geologic‐process models and geostatistics for conditional simulation of 3‐D subsurface heterogeneity
Author(s)
Michael H. A.,
Li H.,
Boucher A.,
Sun T.,
Caers J.,
Gorelick S. M.
Publication year2010
Publication title
water resources research
Resource typeJournals
PublisherAmerican Geophysical Union
The goal of simulation of aquifer heterogeneity is to produce a spatial model of the subsurface that represents a system such that it can be used to understand or predict flow and transport processes. Spatial simulation requires incorporation of data and geologic knowledge, as well as representation of uncertainty. Classical geostatistical techniques allow for the conditioning of data and uncertainty assessment, but models often lack geologic realism. Simulation of physical geologic processes of sedimentary deposition and erosion (process‐based modeling) produces detailed, geologically realistic models, but conditioning to local data is limited at best. We present an aquifer modeling methodology that combines geologic‐process models with object‐based, multiple‐point, and variogram‐based geostatistics to produce geologically realistic realizations that incorporate geostatistical uncertainty and can be conditioned to data. First, the geologic features of grain size, or facies, distributions simulated by a process‐based model are analyzed, and the statistics of feature geometry are extracted. Second, the statistics are used to generate multiple realizations of reduced‐dimensional features using an object‐based technique. Third, these realizations are used as multiple alternative training images in multiple‐point geostatistical simulation, a step that can incorporate local data. Last, a variogram‐based geostatistical technique is used to produce conditioned maps of depositional thickness and erosion. Successive realizations of individual strata are generated in depositional order, each dependent on previously simulated geometry, and stacked to produce a fully conditioned three‐dimensional facies model that mimics the architecture of the process‐based model. We demonstrate the approach for a typical subsea depositional complex.
Subject(s)computer science , geology , geostatistics , kriging , mathematics , operating system , process (computing) , soil science , spatial variability , statistics , variogram
Language(s)English
SCImago Journal Rank1.863
H-Index217
eISSN1944-7973
pISSN0043-1397
DOI10.1029/2009wr008414

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