Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

doi:10.1109/jstars.2016.2518119

Cited by 18 publications

(13 citation statements)

References 57 publications

(88 reference statements)

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“…Transform. An efficient reduction of the number of parameters can contribute to improving the history matching performance [1,15,17]. Discrete cosine transform (DCT) presents finite data points in a sum of coefficients of cosine functions at different frequencies [44].…”

Section: Extraction Of Geologic Features Using Discrete Cosinementioning

confidence: 99%

“…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%

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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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Section: Extraction Of Geologic Features Using Discrete Cosinementioning

confidence: 99%

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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“…Meanwhile, a sufficiently large N lib needs to be chosen to cover a variety of geologically plausible scenarios in Y. Previous investigators adopted N lib in the range of 1000 to 2000 [31,37]. In this paper, N lib is a constant of 3000 for maintaining the diversity of library models.…”

Section: Construction Of Geologic Dictionaries Using Sparsementioning

confidence: 99%

“…where • F is the Frobenius norm and Y′ = DX. More specifically, matrix decomposition is performed by iterating sparse coding that is an orthogonal matching pursuit (OMP) [38,39] followed by K-SVD [29], as described by Sana et al [31]. The first step of matrix decomposition is to initialize D and X.…”

Section: Construction Of Geologic Dictionaries Using Sparsementioning

confidence: 99%

“…Extracting essential geologic features and reducing the number of reservoir variables can be accomplished using sparse coding, thereby facilitating ensemble-based history matching [27]. Aharon et al [29] showed the efficacy of K-singular value decomposition (K-SVD) resulting from the accelerated convergence for image reconstruction, which led Sana et al [31] to build an archive of essential geologic features called sparse geologic dictionaries from thousands of static reservoir models using K-SVD and calibrate reservoir models with the dictionaries using EnKF. One drawback of K-SVD is its large size of sparse geologic dictionaries.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Real-Time Intelligent Sensor Selection for Subsurface CO2 Flow and Fracture Monitoring

Katterbauer,

Al Shehri,

Al Qasim

2022

Artificial Intelligence Trends in Systems

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Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

Cited by 18 publications

References 57 publications

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

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

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

Real-Time Intelligent Sensor Selection for Subsurface CO2 Flow and Fracture Monitoring

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