Integration of Markov mesh models and data assimilation techniques in complex reservoirs

We present a methodology that allows conditioning the spatial distribution of geological and petrophysical properties of reservoir model realizations on available production data. The approach is fully consistent with modern concepts depicting natural reservoirs as composite media where the distribu...

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Bibliographic Details
Published in:Computational geosciences 2016-06, Vol.20 (3), p.637-653
Main Authors: Panzeri, M., Della Rossa, E. L., Dovera, L., Riva, M., Guadagnini, A.
Format: Article
Language:English
Subjects:
Algorithms
Data assimilation
Data collection
Earth and Environmental Science
Earth Sciences
Finite element method
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Kalman filters
Markov analysis
Markov models
Mathematical Modeling and Industrial Mathematics
Mathematical models
Methodology
Oil recovery
Original Paper
Reservoirs
Soil Science & Conservation
Spatial analysis
Spatial distribution
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Summary:We present a methodology that allows conditioning the spatial distribution of geological and petrophysical properties of reservoir model realizations on available production data. The approach is fully consistent with modern concepts depicting natural reservoirs as composite media where the distribution of both lithological units (or facies) and associated attributes are modeled as stochastic processes of space. We represent the uncertain spatial distribution of the facies through a Markov mesh (MM) model, which allows describing complex and detailed facies geometries in a rigorous Bayesian framework. The latter is then embedded within a history matching workflow based on an iterative form of the ensemble Kalman filter (EnKF). We test the proposed methodology by way of a synthetic study characterized by the presence of two distinct facies. We analyze the accuracy and computational efficiency of our algorithm and its ability with respect to the standard EnKF to properly estimate model parameters and assess future reservoir production. We show the feasibility of integrating MM in a data assimilation scheme. Our methodology is conducive to a set of updated model realizations characterized by a realistic spatial distribution of facies and their log permeabilities. Model realizations updated through our proposed algorithm correctly capture the production dynamics.
ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-015-9540-5