Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

Свободный доступ

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.v1.i1.40
pages 49-76

ASSIMILATION OF COARSE-SCALEDATAUSINGTHE ENSEMBLE KALMAN FILTER

Santha Akella
The Johns Hopkins University, Baltimore, MD 21218, USA
A. Datta-Gupta
Department of Petroleum Engineering, Texas A&M University, College Station, TX 77843, USA
Yalchin Efendiev
Department of Mathematics and Institute for Scientific Computation (ISC), Texas A&M University, College Station, TX 77840, USA; Multiscale Model Reduction Laboratory, North-Eastern Federal University, Yakutsk, Russia, 677980

Краткое описание

Reservoir data is usually scale dependent and exhibits multiscale features. In this paper we use the ensemble Kalman filter (EnKF) to integrate data at different spatial scales for estimating reservoir fine-scale characteristics. Relationships between the various scales is modeled via upscaling techniques. We propose two versions of the EnKF to assimilate the multiscale data, (i) where all the data are assimilated together and (ii) the data are assimilated sequentially in batches. Ensemble members obtained after assimilating one set of data are used as a prior to assimilate the next set of data. Both of these versions are easily implementable with any other upscaling which links the fine to the coarse scales. The numerical results with different methods are presented in a twin experiment setup using a two-dimensional, two-phase (oil and water) flow model. Results are shown with coarse-scale permeability and coarse-scale saturation data. They indicate that additional data provides better fine-scale estimates and fractional flow predictions. We observed that the two versions of the EnKF differed in their estimates when coarse-scale permeability is provided, whereas their results are similar when coarse-scale saturation is used. This behavior is thought to be due to the nonlinearity of the upscaling operator in the case of the former data. We also tested our procedures with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data yielded improved estimates. With better coarse-scale modeling and inversion techniques as more data at multiple coarse scales is made available, the proposed modification to the EnKF could be relevant in future studies.


Articles with similar content:

AN ERROR SUBSPACE PERSPECTIVE ON DATA ASSIMILATION
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 6
Haiyan Cheng, Adrian Sandu
A SIMULATION-BASED UPSCALING TECHNIQUE FOR MULTISCALE MODELING OF ENGINEERING SYSTEMS UNDER UNCERTAINTY
International Journal for Multiscale Computational Engineering, Vol.12, 2014, issue 6
Seung-Kyum Choi, Recep Gorguluarslan
GRID-BASED INVERSION OF PRESSURE TRANSIENT TEST DATA WITH STOCHASTIC GRADIENT TECHNIQUES
International Journal for Uncertainty Quantification, Vol.2, 2012, issue 4
Fikri Kuchuk, Richard Booth, Kirsty Morton, Mustafa Onur
EXPERIMENTAL APPARATUS TO INVESTIGATE CALCIUM CARBONATE SCALE GROWTH RATES
Proceedings of an International Conference on Mitigation of Heat Exchanger Fouling and Its Economic and Environmental Implications, Vol.0, 1999, issue
D.K. Baker, G.C. Vliet, D.F. Lawler
USING PARALLEL MARKOV CHAIN MONTE CARLO TO QUANTIFY UNCERTAINTIES IN GEOTHERMAL RESERVOIR CALIBRATION
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 3
M. J. O'Sullivan, G. K. Nicholls, C. Fox, Tiangang Cui