Influence of the Kalman gain localization in adaptive ensemble smoother history matching

Ranazzi, Paulo Henrique; Sampaio, Misako U.

doi:10.1016/j.petrol.2019.04.079

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Several authors reported the need to use some localization technique when the assimilation is performed in largescale models and using a limited ensemble size [e.g., 18,[24][25][26][27][28][29]31]. One widely method used in the history matching applies the localization directly on the Kalman gain matrix (…”

Section: Localizationmentioning

confidence: 99%

“…The case study selected to verify the ensemble influence is the benchmark UNISIM-I, since some works have already performed history matching in UNISIM-I using the ES-MDA (e.g., Morosov and Schiozer [19], Silva et al [24], Soares et al [25], Emerick [26], Ranazzi and Sampaio [27]). In Ranazzi and Sampaio [28], the influence of the localization was verified in the adaptive scheme, where lower correlation lengths resulted in more required iterations to perform the entire algorithm. In Sect.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble size investigation in adaptive ES-MDA reservoir history matching

Ranazzi

Sampaio

2019

J Braz. Soc. Mech. Sci. Eng.

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In this work, we study the ensemble size influence on an adaptive ensemble-based methodology for history matching of petroleum reservoirs. The assimilation scheme used is an adaptive ensemble smoother with multiple data assimilation (ES-MDA) in which both the total number of assimilations and the inflation factor of each iteration are defined automatically by the algorithm. This fact leads to the assumption that the predefined algorithm parameters may have influence in the total number of assimilations and the inflation factors. One main parameter that can be investigated is the number of ensemble members used in the assimilation, also called ensemble size. The ensemble size influence was analyzed by applying the adaptive ES-MDA in a synthetic large-scale reservoir model. As a result of the investigation, the ensemble size showed influence on the reduction in the uncertainty of the posterior models, but it did not show any influence on the total number of assimilations and on the inflation factor selection.

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Section: Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble size investigation in adaptive ES-MDA reservoir history matching

Ranazzi

Sampaio

2019

J Braz. Soc. Mech. Sci. Eng.

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“…The case study selected to check the ensemble influence is the benchmark UNISIM-I, since some works already performed history matching in UNISIM-I using the ES-MDA (e.g.,Morosov and Schiozer (2017),Silva et al (2017),Soares, Maschio and Schiozer (2018), Emerick (2018),Ranazzi and Sampaio (2018)). InRanazzi and Sampaio (2019), the influence of the localization was verified in the adaptive scheme, where lower correlation lengths resulted in more required iterations to perform the entire algorithm. In Section 2.2, we define the history matching problem and the adaptive ES-MDA methodology, in Section 2.3 we describe the benchmark UNISIM-I and the experiments that we run to the discussion.…”

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confidence: 96%

Analysis of main parameters in adaptive ES-MDA history matching.

Ranazzi¹

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In reservoir engineering, history matching is the technique that reviews the uncertain parameters of a reservoir simulation model in order to obtain a response according to the observed production data. Reservoir properties have uncertainties due to their indirect acquisition methods, that results in discrepancies between observed data and reservoir simulator response. A history matching method is the Ensemble Smoother with Multiple Data assimilation (ES-MDA), where an ensemble of models is used to quantify the parameters uncertainties. In ES-MDA, the number of iterations must be defined previously the application by the user, being a determinant parameter for a good quality matching. One way to handle this, is by implementing adaptive methodologies when the algorithm keeps iterating until it reaches good matchings. Also, in large-scale reservoir models it is necessary to apply the localization technique, in order to mitigate spurious correlations and high uncertainty reduction of posterior models. The main objective of this dissertation is to evaluate two main parameters of history matching when using an adaptive ES-MDA: localization and ensemble size, verifying the impact of these parameters in the adaptive scheme. The adaptive ES-MDA used in this work defines the number of iterations and the inflation factors automatically and distance-based Kalman gain localization was used to evaluate the localization influence. The parameters influence was analyzed by applying the methodology in the benchmark UNISIM-I-H: a synthetic large-scale reservoir model based on an offshore Brazilian field. The experiments presented considerable reduction of the objective function for all cases, showing the ability of the adaptive methodology of keep iterating until a desirable overcome is obtained. About the parameters evaluated, a relationship between the localization and the required number of iterations to complete the adaptive algorithm was verified, and this influence has not been observed as function of the ensemble size.

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A review on optimization algorithms and surrogate models for reservoir automatic history matching

Zhao,

Luo,

et al. 2024

Geoenergy Science and Engineering

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Influence of the Kalman gain localization in adaptive ensemble smoother history matching

Cited by 7 publications

References 30 publications

Ensemble size investigation in adaptive ES-MDA reservoir history matching

Ensemble size investigation in adaptive ES-MDA reservoir history matching

Analysis of main parameters in adaptive ES-MDA history matching.

A review on optimization algorithms and surrogate models for reservoir automatic history matching

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