The importance of localization in the assimilation of 4D seismic data in the data assimilation process using the EnKF

Trani, M.; Arts, Rob; Leeuwenburgh, O.; Brouwer, J.; Douma, Sippe G.

doi:10.1190/1.3255667

Cited by 2 publications

(4 citation statements)

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“…All realizations are simulated for 1500 days under a prescribed constant rate constraint of 5435 BPD for the injector and a constant BHP constraint of 250 Bar for the producers. The model is identical to that used by Trani et al [37] who investigated the impact of localization for a case in which saturation and pressure changes, as obtained from an inversion of time-lapse seismic AVO data, were assimilated with the EnKF. Pressure and rate data are available every 50 days up to day 500 and seismic data every 150 days up to day 450, including a base survey before the start of production.…”

Section: D Five-spot Modelmentioning

confidence: 99%

“…This approach limits updates to model grid regions which are connected to a particular well by streamlines. The relative merits of distancebased vs streamline-based localization for the incorporation of seismic data were studied by Trani et al [37]. Synthetic 3D examples for a single five-spot well pattern showed that both types of localization help to better assimilate time-lapse seismic data, and to improve the quality of the permeability estimation, the history match and the production forecast compared to the case where no localization is applied.…”

Section: Localizationmentioning

confidence: 99%

“…The seismic attributes that have been mostly used in realistic case studies are acoustic (P-wave) impedance and Poisson ratio, which can be obtained fairly easily by applying a rock physics model to the simulator output, but which require more elaborate inversion schemes to obtain from observed seismic amplitudes. Trani [37] extended the inversion scheme of Landrø [24] with travel-time differences and suggested assimilating the resulting saturation and pressure differences with the EnKF.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble-based conditioning of reservoir models to seismic data

2010

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While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in incorporating 3D or time-lapse seismic data into the history matching process in a more quantitative manner. The common approach in recent studies has been to invert the seismic data to elastic or to dynamic reservoir properties, typically acoustic impedance or saturation changes. Here we consider the use of both 3D and time-lapse seismic amplitude data based on a forward modeling approach that does not require any inversion in the traditional sense. Advantages of such an approach may be better estimation and treatment of model and measurement errors, the combination of two inversion steps into one by removing the explicit inversion to state space variables, and more consistent dependence on the validity of assumptions underlying the inversion process. In this paper, we introduce this approach with the use of an assisted history matching method in mind. Two ensemble-based methods, the ensemble Kalman filter and the ensemble randomized maximum likelihood method, are used to investigate issues arising from the use of seismic amplitude data, and possible solutions are presented. Experiments with a 3D synthetic reservoir model show that additional information on the distribution of reservoir fluids, and on rock properties such as porosity and permeability, can be extracted from the seismic data. The role for localization and iterative methods are discussed in detail.

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Section: D Five-spot Modelmentioning

confidence: 99%

Section: Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble-based conditioning of reservoir models to seismic data

2010

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“…time-lapse seismic data, can result in over-fitting. There have been several efforts to balance the degrees of freedom of the problem and the information content in the data, including use of localization [8], reduction of data using machine learning techniques [9,10], reduction in data size using the correlation between the data and wells' cumulative production [11], sparse representation of data using a wavelet transform [12], assimilation of only the saturation front or transformation of the data into position of fluid fronts [13][14][15], combination of coarsening the data and coarse model simulations [16], and projection of data into ensemble subspace in combination with local analysis [17].…”

Section: Introductionmentioning

confidence: 99%

Sequential multilevel assimilation of inverted seismic data

et al. 2023

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We consider estimation of absolute permeability from inverted seismic data. Large amounts of simultaneous data, such as inverted seismic data, enhance the negative effects of Monte Carlo errors in ensemble-based Data Assimilation (DA). Multilevel (ML) models consist of a selection of models with different fidelities. Multilevel Data Assimilation (MLDA) attempts to obtain a better statistical accuracy with a small sacrifice of the numerical accuracy. Spatial grid coarsening is one way of generating an ML model. It has been shown that coarsening the spatial grid results in a problem with weaker nonlinearity, and hence, in a less challenging problem than the problem on the original fine grid. Accordingly, formulating a sequential MLDA algorithm which uses the coarser models in the first steps of the DA, followed by the finer models, helps to find an approximation to the solution of the inverse problem at the first steps and gradually converge to the solution. We present two variants of a sequential MLDA algorithm and compare their performance with both conventional DA algorithms and a simultaneous (i.e., using all the models on the different grids simultaneously) MLDA algorithm using numerical experiments. Both posterior parameters and posterior model forecasts are compared qualitatively and quantitatively. The results from numerical experiments suggest that all MLDA algorithms generally perform better than the conventional DA algorithms. In estimation of the posterior parameter fields, the simultaneous MLDA algorithm and one of the variants of sequential MLDA (SMLES-H) perform similarly and slightly better than the other variant (SMLES-S). While in estimation of the posterior model forecasts, SMLES-S clearly performs better than both the simultaneous MLDA algorithm and SMLES-H.

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The importance of localization in the assimilation of 4D seismic data in the data assimilation process using the EnKF

Cited by 2 publications

References 4 publications

Ensemble-based conditioning of reservoir models to seismic data

Ensemble-based conditioning of reservoir models to seismic data

Sequential multilevel assimilation of inverted seismic data

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