Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

Jung, Sun-Young; Lee, Kyungbook; Park, Changhyup; Choe, Jonggeun

doi:10.3390/en11020445

Cited by 22 publications

(4 citation statements)

References 74 publications

(128 reference statements)

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“…1 because they do not consider the geological meaning of reservoir parameters. It has been reported that this problem can be solved by techniques such as localization in many studies (Watanabe and Datta-Gupta 2012;Luo et al 2018;Jung et al 2018). Since this research is not a study to improve the ensemble-based method, only the standard ensemble-based methods are used as a comparison of the proposed method.…”

Section: Characterization Of Channel Connectivitymentioning

confidence: 99%

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

et al. 2018

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Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image (TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter (EnKF) and ensemble smoother (ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

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Section: Characterization Of Channel Connectivitymentioning

confidence: 99%

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

et al. 2018

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“…As for the batch estimation techniques, it is possible to estimate the terms with lower corresponding variances assuming the fact that not only the signal values up to the current time is used but also the future values of the signals can provide information to lower the estimation error variance. EKF-smoother and its computationally efficient version Rauch-Tung-Striebel (RTS) smoother is often preferred to construct the state trajectories and parameter estimations [20,21]. Additionally, there are also Bayesian smoothers that use different approaches where not only the value of the state vector at a given instant is estimated but also the whole state trajectory is determined using dynamical programming techniques [22].…”

Section: Introductionmentioning

confidence: 99%

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Sel

Kasnakoğlu

2021

Energies

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In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

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“…Roughly, these methods for assisted history matching can be divided into two categories [7]: the data assimilation approaches (such as Ensemble Kalman Filter and Ensemble Smoother) and the optimization approaches (such as gradient, evolutionary or data-driven-based algorithms). Ensemble Kalman Filter (EnKF) and Ensemble Smoother (ES) are representative methods for data assimilation [8]. For example, EnKF is a sequential Monte Carlo approximation of the Kalman filter where the correlation between reservoir parameters and observed production data can be estimated from the ensemble with the uncertainty of estimation [9].…”

Section: Introductionmentioning

confidence: 99%

“…To address the limitations, various EnKF extensions were developed through localization, ensemble design scheme and clustering methods, etc. [8,10]. In contrast to the iterative process of EnKF, ES could simultaneously assimilate all the production data in a global update, which is much faster than EnKF [11].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Zhang

Jiang

et al. 2020

Energies

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Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

Cited by 22 publications

References 74 publications

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

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