Assisted History Matching of Channelized Models Using Pluri-Principal Component Analysis

Chen, C..; Gao, Guohua; Ramírez, Benjamín; Vink, Jeroen C.; Girardi, Alejandro

doi:10.2118/173192-ms

Cited by 19 publications

(6 citation statements)

References 34 publications

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“…This fact lead several researches to propose a variate of parameterizations to adapt these methods for models with non-Gaussian priors, such as models generated with object-based (Deutsch and Journel, 1998) and multiple-point geostatistics (Mariethoz and Caers, 2014). Among these parameterizations, we can cite, for example, truncated plurigaussian simulation (Liu and Oliver, 2005;Agbalaka and Oliver, 2008;Sebacher et al, 2013;Zhao et al, 2008); level-set functions (Moreno et al, 2008;Chang et al, 2010;Moreno and Aanonsen, 2011;Lorentzen et al, 2012;Ping and Zhang, 2014); discrete cosine transform (Jafarpour and McLaughlin, 2008;Zhao et al, 2016;Jung et al, 2017); Wavelet transforms (Jafarpour, 2010); K-singular value decomposition (Sana et al, 2016;Kim et al, 2018); kernel principal component analysis (KPCA) (Sarma et al, 2008;Sarma and Chen, 2009); PCA with thresholds defined to honor the prior cumulative density function (Chen et al, 2014(Chen et al, , 2015Gao et al, 2015;Honorio et al, 2015) and optimization-based PCA (OPCA) (Vo and Durlofsky, 2014;Emerick, 2017). There are also works based on updating probability maps followed by re-sampling steps with geostatistical algorithms (Tavakoli et al, 2014;Chang et al, 2015;Jafarpour and Khodabakhshi, 2011;Le et al, 2015;Sebacher et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Canchumuni

Emerick

Pacheco

2019

Computers & Geosciences

129

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History matching is a jargon used to refer to the data assimilation problem in oil and gas reservoirs. The literature about history matching is vast and despite the impressive number of methods proposed and the significant progresses reported in the last decade, conditioning reservoir models to dynamic data is still a challenging task. Ensemble-based methods are among the most successful and efficient techniques currently available for history matching. These methods are usually able to achieve reasonable data matches, especially if an iterative formulation is employed. However, they sometimes fail to preserve the geological realism of the model, which is particularly evident in reservoir with complex facies distributions. This occurs mainly because of the Gaussian assumptions inherent in these methods. This fact has encouraged an intense research activity to develop parameterizations for facies history matching. Despite the large number of publications, the development of robust parameterizations for facies remains an open problem.Deep learning techniques have been delivering impressive results in a number of different areas and the first applications in data assimilation in geoscience have started to appear in literature. The present paper reports the current results of our investigations on the use of deep neural networks towards the construction of a continuous parameterization of facies which can be used for data assimilation with ensemble methods. Specifically, we use a convolutional variational autoencoder and the ensemble smoother with multiple data assimilation. We tested the parameterization in three synthetic history-matching problems with channelized facies. We focus on this type of facies because they are among the most challenging to preserve after the assimilation of data. The parameterization showed promising results outperforming previous methods and generating well-defined channelized facies. However, more research is still required before deploying these methods for operational use.

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Section: Introductionmentioning

confidence: 99%

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Canchumuni

Emerick

Pacheco

2019

Computers & Geosciences

129

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“…Also, as is commonly the case, the match is certainly not perfect and fairly significant mismatches remain (e.g., in the water rates of producers 1 and 2). One way to proceed would be to find a better model, for example, apply an AHM procedure that can also modify facies type as in Chen, et al (2015). In this way the impact of under-modelling can be reduced.…”

Section: Best Match Resultsmentioning

confidence: 99%

“…The field under consideration is a deep-water channelized reservoir, shown in Figure 5, which was previously used to investigate novel AHM workflows using the pluri-PCA reparameterization technique (Chen, et al, 2014(Chen, et al, , 2015. Since we want to use the model not only for history matching but also to Figure 4 -Posterior forecast uncertainty in a model that cannot reproduce the truth case due to "under-modeling".…”

Section: Model Descriptionmentioning

confidence: 99%

“…investigate the uncertainty range for forecast results, we chose to ignore the historic data and generate synthetic data using the best-match model produced in Chen, et al (2015). To mimic a fairly common history matching challenge, the base-case model uses a "P50" geological realization, which is different from the truth case.…”

Section: Model Descriptionmentioning

confidence: 99%

“…However, we do claim that they represent a realistic set of model parameters that could be used in this type of history matching workflows. Since this model was used in previous AHM studies as well, we refer to Chen, et al (2014Chen, et al ( , 2015 and Yeh, et al (2014) for further details on the model.…”

Section: Model Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Style History Matching: Another Way to Under-Estimate Forecast Uncertainty?

Vink

Gao

Chen

2015

SPE Annual Technical Conference and Exhibition

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Current theoretical formulations of assisted history matching (AHM) problems within the Bayesian framework, e.g., ensemble Kalman filter (EnKF) and randomized maximum likelihood (RML), are typically based on the assumption that simulation models can accurately reproduce field data within the measurement error. However, this assumption does not hold for AHM problems of real assets. This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular it demonstrates the risk of underestimating uncertainty, when conditioning real-life models to large numbers of field data. Even though it is well-known, that model error and under-modeling impacts Bayesian methods, the practical effect that uncertainty may be severely underestimated, simply by using all available data is not well appreciated. Besides highlighting this effect, also a mitigation strategy to counteract this problem will be proposed and shown to be effective for the analytical toy model as well as for the real field case used as tests in this paper.After briefly reviewing the Bayesian method and its underlying assumptions, limitations of AHM approaches within the Bayesian framework are analyzed using a simple analytical model in which forecast uncertainty can be computed both with and without constraints due to historic data. In particular the model can be used to illustrate the impact of using an inaccurate, or incomplete, simulation model. The observations from this analytical work can then be generalized to real-life workflows that are currently implemented in many commercial and proprietary tools. To mitigate the observed problem, a fairly simple but effective modification of the AHM workflow is proposed and tested on the analytical test case.The same mitigation procedure is then also applied to improve uncertainty quantification of production forecasts using a real asset model. In order to see if the proposed workflow indeed leads to a more credible uncertainty assessment for forecast results, a specific realization of the asset model is used to generate synthetic production data. The model used for history matching and uncertainty quantification uses a different geological realization and hence can never reproduce the production results (which are assumed to have negligible noise). Also in this realistic setting, it is shown that forecasts easily can be underestimated when large numbers of data are used to constrain forecast uncertainty in an imperfect model with accurate data. This undesired effect comes out as the flip-side of the attractive property of Bayesian methods that model parameters can be inferred with increased accuracy if the number of data is increased in a perfect model with noisy data.

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Waterflooding optimization with the INSIM-FT data-driven model

2018

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Assisted History Matching of Channelized Models Using Pluri-Principal Component Analysis

Cited by 19 publications

References 34 publications

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Bayesian Style History Matching: Another Way to Under-Estimate Forecast Uncertainty?

Waterflooding optimization with the INSIM-FT data-driven model

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