Combining geologic‐process models and geostatistics for conditional simulation of 3‐D subsurface heterogeneity

Abstract

[1] 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 variogrambased 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 processbased model. We demonstrate the approach for a typical subsea depositional complex.

A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

“…The spatial interpolation between measurement points is generally not suited for depicting sharply bounded structures like different sediment facies (Michael et al, 2010). More sophisticated methods like multi-point geostatistical techniques excel indeed at respecting local data and allow more realistic representations of spatial patterns, but are still dependent on the input of training images that already purport the spatial heterogeneity (Hu and Chugunova, 2008).…”

“…Catchment and reservoir modelling approaches distinguish between object-based methods that distribute predefined "geobodies" within a model domain and process-based techniques that describe the physical processes rather than the existing structure (Michael et al, 2010). For instance, Pyrcz et al (2009) developed an object-based model that constructs stochastic pseudo-process-based fluvial models.…”

“…Gross and Small (1998) formulated a process model to simulate the development of river facies, and Miller et al (2008) used a process-modelling approach to generate geologically realistic structures of turbidite fans. Their process-based spatial model was conditioned with measured data using geostatistical techniques (Michael et al, 2010). Such structuregenerating methods produce only stochastically possible realizations representing the reservoir.…”

3D multiple-point statistics simulation using 2D training images

“…To overcome this difficulty, one can, for example, mix different techniques and use a hierarchical approach , but the simulation framework can become complex. The complexity of the algorithms arises in many cases mainly from conditioning the simulations to the observation and taking into account the nonstationarity rather than from the technique itself (see, e.g., Michael et al, 2010).…”

Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

“…MPS has been advanced and tested for several years, e.g. threedimensional (3D) application (Comunian et al 2011Michael et al 2010), direct sampling (Mariethoz et al 2010;Rezaee et al 2013) and geological structure (Huysmans and Dassargues 2009;2011;Rezaee et al 2014), demonstrating the ability of reproducing realistic geological models in a stochastic framework, and allowing a quantification of the uncertainty.…”