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

Zhao, Yu; Forouzanfar, Fahim; Reynolds, Albert C.

doi:10.1007/s10596-016-9604-1

Cited by 41 publications

(20 citation statements)

References 34 publications

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“…DCT is also used to extract the main information of a static model in a history matching area [27][28][29][30][31][32][33][34][35]. After original data are transformed to coefficients of discrete cosine functions, the coefficients are arranged in descending order of frequencies of cosine functions.…”

Section: Combined Distancementioning

confidence: 99%

“…The selection of a subset of DCT coefficients depends on the objective of the research. For example, to characterize channel reservoirs, several papers applied DCT to permeability data to extract only the main channel trends in the ensemble models with low frequencies' coefficients [27][28][29][30][31][32]34,35]. The channel trends in reservoirs can be described with only a small number of coefficients.…”

Section: + ( − 3) =mentioning

confidence: 99%

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Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

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This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

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Section: Combined Distancementioning

confidence: 99%

Section: + ( − 3) =mentioning

confidence: 99%

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

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“…These methods typically involve the following steps: (1) estimating a prior ensemble of facies distribution using indicator geostatistical models, (2) translating the ensemble facies distribution to continuous shape parameters that describe the facies boundaries, (3) performing EDA on the shape parameters to improve the match between model predictions and observations, and (4) updating the facies distribution using the updated shape parameters. All of the aforementioned parameterization methods impose no spatial structure constraints on facies (e.g., facies volume proportions, correlation lengths, or juxtapositional tendencies) in the process of data assimilation, which may lead to an unrealistic discontinuity and/or overfitting in resulted facies distribution (Jung et al, 2017;Ma & Jafarpour, 2018;Nejadi et al, 2015;Vo & Durlofsky, 2016;Zhao et al, 2017). Chang and Zhang (2014) proposed to alleviate the facies discontinuity issue by updating facies indicators on a coarse representation of the computational grid and then using interpolation to generate the rest of the facies field.…”

Section: Introductionmentioning

confidence: 99%

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Song

Dai

et al. 2019

Water Resources Research

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The facies‐based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble‐based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. We introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability‐based geostatistics. A level‐set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies‐based permeability field. The first example is a two‐dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three‐dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.

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“…If essential features are adequately acquired, updating the features can also yield an improved history matching performance over calibrating original parameters. For these reasons, discrete cosine transform (DCT) [14,15], discrete wavelet transform [1], K-singular value decomposition (K-SVD) [16,17], and autoencoder (AE) [18] have been employed as ancillary parameterizations of ensemblebased methods. For history matching of channelized reservoirs, DCT has been utilized to preserve channel properties because DCT figures out overall trends and main patterns of channels by using only essential DCT coefficients [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA

et al. 2019

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For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. As a training dataset of the SDAE, the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise: salt and pepper noise and Gaussian noise. The SDAE learns how to eliminate the noise and restore the clean reservoir models. It does this through encoding and decoding processes using the noise realizations as inputs and the original realizations as outputs of the SDAE. The trained SDAE is embedded in the ES-MDA. The posterior reservoir models updated using Kalman gain are imported to the SDAE which then exports the purified prior models of the next assimilation. In this manner, a clear contrast among rock facies parameters during multiple data assimilations is maintained. A case study at a gas reservoir indicates that ES-MDA coupled with the noise remover outperforms a conventional ES-MDA. Improvement in the history matching performance resulting from denoising is also observed for ES-MDA algorithms combined with dimension reduction approaches such as discrete cosine transform, K-singular vector decomposition, and a stacked autoencoder. The results of this study imply that a well-trained SDAE has the potential to be a reliable auxiliary method for enhancing the performance of data assimilation algorithms if the computational cost required for machine learning is affordable.

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

Cited by 41 publications

References 34 publications

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA

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