Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification

Katterbauer, Klemens; Arango, Santiago; Sun, Shuyu; Hoteit, Ibrahim

doi:10.1016/j.petrol.2015.02.016

Cited by 27 publications

(10 citation statements)

References 62 publications

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“…Ensemble-based data-assimilation methods provide a flexible framework under which any uncertain model parameters can be considered and various types of measurements can be readily incorporated (Chen and Oliver, 2014;Katterbauer et al, 2015). The flowchart shown in Fig.…”

Section: Ensemble-based History Matching Frameworkmentioning

confidence: 99%

“…Katterbauer et al (2016) presented a history matching study using EM data attribute (conductivity, assumed to be known) to estimate the components of a compositional reservoir model with the EnKF, in which the uncertainty in Archie's parameters and the variance of observation error was also considered. In addition to EM conductivity attribute, Katterbauer et al (2015) also incorporated seismic, gravimetry and InSAR data for history matching using the EnKF to study the synergy effect that could result from combing them. Matching enhancements were observed when all data sets were integrated, and the most influential impact from EM data was suggested.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework

2019

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Section: Ensemble-based History Matching Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework

2019

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“…The EnKF is a popular Monte Carlo variant of the standard Kalman filter (KF) and has been successfully applied in a variety of history matching problems [8,39,36,40]. As the KF, the EnKF estimation process is based on forecast and analysis cycles but differs in the way the first two moments of the system state are represented by an ensemble of state vectors, approximating the KF estimate and associated error covariance matrix by the ensemble sample mean and covariance.…”

Section: The Ensemble Kalman Filter (Enkf)mentioning

confidence: 99%

A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

Sana

Ravanelli

Al-Naffouri

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Therefore, there is a high demand from the petroleum industry for efficient methods of big data analytics to extract, analyze and utilize the information from big 4D seismic data. Similar problems are also faced in many other fields that involve abundant data obtained through, for example, satellite remote sensing [ 4 ], medical imaging [ 5 ], geophysical surveys [ 6 , 7 ], and so on. As a result, big (geophysical) data assimilation has become an important topic in practice.…”

Section: Introductionmentioning

confidence: 99%

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

et al. 2018

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Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D benchmark case, the Brugge field. In this benchmark case study, the total number of seismic data is in the order of . We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy.

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Multi-data reservoir history matching for enhanced reservoir forecasting and uncertainty quantification

Cited by 27 publications

References 62 publications

Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework

Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework

A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

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