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A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
High-resolution truncated plurigaussian simulations for the characterization of heterogeneous formations
Integrating geological concepts, such as relative positions and proportions
of the different lithofacies, is of highest importance in order to render
realistic geological patterns. The truncated plurigaussian simulation method
provides a way of using both local and conceptual geological information to
infer the distributions of the facies and then those of hydraulic parameters.
The method (Le Loc'h and Galli 1994) is based on the idea of truncating at
least two underlying multi-Gaussian simulations in order to create maps of
categorical variable. In this manuscript we show how this technique can be used
to assess contaminant migration in highly heterogeneous media. We illustrate
its application on the biggest contaminated site of Switzerland. It consists of
a contaminant plume located in the lower fresh water Molasse on the western
Swiss Plateau. The highly heterogeneous character of this formation calls for
efficient stochastic methods in order to characterize transport processes.Comment: 12 pages, 9 figure
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