Multiscale Method for History Matching Channelized Reservoirs Using Level Sets

2016 ◽  
Author(s):  
Jichao Han ◽  
Rongqiang Chen ◽  
Akhil Datta-Gupta
Keyword(s):  
Level Sets ◽  
History Matching ◽  
Multiscale Method ◽  
Channelized Reservoirs

scholarly journals Integration of an Iterative Update of Sparse Geologic Dictionaries with ES-MDA for History Matching of Channelized Reservoirs

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Sungil Kim ◽  
Baehyun Min ◽  
Kyungbook Lee ◽  
Hoonyoung Jeong
Keyword(s):  
History Matching ◽  
Gas Reservoirs ◽  
Dynamic Data ◽  
Multiple Data ◽  
Ensemble Smoother ◽  
Water Rate ◽  
Non Gaussian ◽  
Value Decomposition ◽  
Channelized Reservoirs ◽  
Channel Properties

This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelized reservoir such as pattern and continuity. Integration of ES-MDA, DCT, and K-SVD is conducted in a complementary way as the initially static dictionaries are updated with dynamic data in each assimilation of ES-MDA. This update of dictionaries allows the coupled algorithm to yield an ensemble well conditioned to static and dynamic data at affordable computational costs. Applications of the proposed algorithm to history matching of two channelized gas reservoirs show that the hybridization of DCT and iterative K-SVD enhances the matching performance of gas rate, water rate, bottomhole pressure, and channel properties with geological plausibility.

Rapid Updating of Stochastic Models by Use of an Ensemble-Filter Approach

SPE Journal ◽  
2013 ◽  
Vol 19 (03) ◽  
pp. 500-513 ◽  
Author(s):  
Reza Tavakoli ◽  
Sanjay Srinivasan ◽  
Mary F. Wheeler
Keyword(s):  
Stochastic Models ◽  
State Vector ◽  
History Matching ◽  
Dimensional Space ◽  
Gaussian Random Fields ◽  
Dynamic Responses ◽  
Dissimilarity Matrix ◽  
Dimension Space ◽  
Channelized Reservoirs ◽  
Prior State

Summary Applying an ensemble Kalman filter (EnKF) is an effective method for reservoir history matching. The underlying principle is that an initial ensemble of stochastic models can be progressively updated to reflect measured values as they become available. The EnKF performance is only optimal, however, if the prior-state vector is linearly related to the predicted data and if the joint distribution of the prior-state vector is multivariate Gaussian. Therefore, it is challenging to implement the filtering scheme for non-Gaussian random fields, such as channelized reservoirs, in which the continuity of permeability extremes is well-preserved. In this paper, we develop a methodology by combining model classification with multidimensional scaling (MDS) and the EnKF to create rapidly updating models of a channelized reservoir. A dissimilarity matrix is computed by use of the dynamic responses of ensemble members. This dissimilarity matrix is transformed into a lower-dimensional space by use of MDS. Responses mapped in the lower-dimension space are clustered, and on the basis of the distances between the models in a cluster and the actual observed response, the closest models to the observed response are retrieved. Model updates within the closest cluster are performed with EnKF equations. The results of an update are used to resample new models for the next step. Two-dimensional, waterflooding examples of channelized reservoirs are provided to demonstrate the applicability of the proposed method. The obtained results demonstrate that the proposed algorithm is viable both for sequentially updating reservoir models and for preserving channel features after the data-assimilation process.

History matching of multi-facies channelized reservoirs using ES-MDA with common basis DCT

2016 ◽  
Vol 21 (5-6) ◽  
pp. 1343-1364 ◽  
Author(s):  
Yu Zhao ◽  
Fahim Forouzanfar ◽  
Albert C. Reynolds
Keyword(s):  
History Matching ◽  
Common Basis ◽  
Channelized Reservoirs

History Matching of Channelized Reservoirs With Vector-Based Level-Set Parameterization

SPE Journal ◽  
2014 ◽  
Vol 19 (03) ◽  
pp. 514-529 ◽  
Author(s):  
Jing Ping ◽  
Dongxiao Zhang
Keyword(s):  
Level Set ◽  
History Matching ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Matching Problem ◽  
Level Set Function ◽  
Level Function ◽  
Set Up ◽  
Non Gaussian ◽  
Channelized Reservoirs

Summary For channelized reservoirs with unknown channel distributions, identifying the continuous and sinuous features of channel distributions is crucial for determining their production behaviors. However, traditional history-matching methods are not appropriate because the pixel-based rock-property fields are usually highly non-Gaussian. In this work, a vector-based level-set parameterization technique for channelized reservoirs is presented. We also propose a combination of this parameterization method and a frequently used history-matching approach, the ensemble Kalman filter (EnKF). To properly represent the continuity and sinuosity of its embedded features, the channelized reservoir is parameterized with a vector that consists of level-set function, real radius, and virtual radius on a representing node system. The level-function value indicates the existence of the particular facies; the real radius of a circle in two dimensions or a sphere in three dimensions signifies the size of the facies; and the virtual radius is used to ensure the continuity of the channelized facies. The 2D and 3D examples of channelized reservoirs are set up to demonstrate the capability of the proposed method. It is found that this method is effective to deal with the history-matching problem of channelized reservoirs.

Sign in / Sign up

Export Citation Format

Share Document