Effect of Scale Dependent Data Correlations in an Integrated History Matching Loop Combining Production Data and 4D Seismic Data

Aanonsen, Sigurd Ivar; Aavatsmark, Ivar; Barkve, T.; Cominelli, Alberto; Gonard, R.; Gosselin, Olivier; Kolasinski, M.; Rème, H.

doi:10.2118/79665-ms

Cited by 63 publications

(23 citation statements)

References 17 publications

(12 reference statements)

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“…Note that the seismic data clearly indicate the waterflooded (increased Poisson's ratio) and gasflooded (decreased Poisson's ratio) areas. Also note that these data are the result of a considerable effort in data preparation (Haverl et al 2005), including a window averaging as proposed by Aanonsen et al (2003). The corresponding result from a base-case simulation is shown in Fig.…”

Section: Field Casementioning

confidence: 96%

Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter

et al. 2007

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Summary A method based on the ensemble Kalman filter (EnKF) for continuous model updating with respect to the combination of production data and 4D seismic data is presented. When the seismic data are given as a difference between two surveys, a combination of the ensemble Kalman filter and the ensemble Kalman smoother has to be applied. Also, special care has to be taken because of the large amount of data assimilated. Still, the method is completely recursive, with little additional cost compared to the traditional EnKF. The model system consists of a commercial reservoir simulator coupled with a rock physics and seismic modeling software. Both static variables (porosity, permeability, and rock physic parameters) and dynamic variables (saturations and pressures) may be updated continuously with time based on the information contained in the assimilated measurements. The method is applied to a synthetic model and a real field case from the North Sea. In both cases, the 4D seismic data are different variations of inverted seismic. For the synthetic case, it is shown that the introduction of seismic data gives a much better estimate of reservoir permeability. For the field case, the introduction of seismic data gives a very different permeability field than using only production data, while retaining the production match. Introduction The Kalman filter was originally developed to update the states of linear systems (Kalman 1960). For a presentation of this method in a probabilistic, linear least-squares setting, see Tarantola (2005). However, this method is not suitable for nonlinear models, and the ensemble Kalman filter (EnKF) method was introduced in 1994 by Geir Evensen for updating nonlinear ocean models (Evensen 1994). The method may also be applied to a combined state and parameter estimation problem (Evensen 2006; Lorentzen 2001; Anderson 1998). Several recent investigations have shown the potential of the EnKF for continuous updating of reservoir simulation models, as an alternative to traditional history matching (Nævdal et al. 2002a, b; Nævdal et al. 2005; Gu and Oliver 2004; Gao and Reynolds 2005; Wen and Chen 2005). The EnKF method is a Monte Carlo type sequential Bayesian inversion, and provides an approximate solution to the combined parameter and state-estimation problem. The result is an ensemble of solutions approximating the posterior probability density function for the model input parameters (e.g., permeability and porosity), state variables (pressures and saturations), and other output data (e.g., well production history) conditioned to measured, dynamic data. Conditioning reservoir simulation models to seismic data is a difficult task (Gosselin et al. 2003). In this paper, we show how the ensemble Kalman filter method can be used to update a combined reservoir simulation/seismic model using the combination of production data and inverted 4D seismic data. There are special challenges involved in the assimilation of the large amount of data available with 4D seismic, and the present work is based on the work presented by Evensen (2006, 2004) and Evensen and van Leeuwen (2000). In the following, the combined state and parameter estimation problem is described in a Bayesian framework, and it is shown how this problem is solved using the EnKF method, with emphasis on the application to 4D seismic data. When the seismic data are given as a difference between two surveys, a combination of the ensemble Kalman filter and the ensemble Kalman smoother has to be applied. Special challenges involved when the amount of data is very large are discussed. The validity of the method is examined using a synthetic model, and finally, a real case from the North Sea is presented.

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Section: Field Casementioning

confidence: 96%

Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter

et al. 2007

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“…This is one difference between a synthetic case and a real field case, where up-scaling and downscaling are almost inevitable. 22,23 The number of model parameters (porosity and permeability in each gridblock, totaling 28,910) is still larger than the number of data even after the seismic impedance change data are integrated (impedance change in each gridblock plus production data, totaling 14,635).…”

Section: Semisynthetic Modelmentioning

confidence: 99%

Quantitative Use of 4D Seismic Data for Reservoir Description

Dong

Oliver

2005

SPE Journal

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The major contribution of this paper is the description of an efficient, gradient-based method for incorporating time-lapse seismic impedance data in an automatic history-matching procedure. We use a finite-difference, black-oil reservoir simulator, with rock physics included, to predict seismic impedance as a function of saturation and pressure. The adjoint method is used to compute the derivatives of seismic impedance change in each gridblock with respect to porosity, permeability, pressure, and phase saturation. With a quasi-Newton minimization method to efficiently minimize the difference between observed impedance changes and the impedance changes predicted using the reservoir simulator, our procedure can be applied to field-scale problems. The feasibility of this method of utilizing time-lapse seismic data is illustrated with an application to a semisynthetic model created from a relatively large Middle East oil field.

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“…The domain of petro-elastic properties, calculated from the simulation model, has been the most popular in recent studies found in literature (Waggoner et al, 2002;Gosselin et al, 2003;Aanonsen et al, 2003;Mezghani et al, 2004;El Quair et al, 2005;Stephen et al, 2006;Roggero et al, 2007;Dong et al, 2008). Authors usually note the advantages of this domain in that no forward seismic data prediction is included in the optimization loop.…”

Section: Introductionmentioning

confidence: 99%

Assisted Seismic History Matching in Different Domains: What Seismic Data Should We Compare?

Sagitov¹,

Stephen²

2012

All Days

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Time-lapse (4D) seismic data can be integrated into history matching by comparing predicted and observed data in various domains. These include the time domain (time traces), seismic attributes, or petro-elastic properties such as acoustic impedance. Each domain requires different modelling methods and assumptions as well as data handling workflows. The aim of this work is to investigate the degree to which the choice of domain influences theoutcome of history matching on the choice of best model and associated uncertainties. Another aspect of history matching is that long simulations often pose an obstacle for an automatic approach. In this study we use appropriately upscaled models manageable in the automatic history matching loop.We apply manual and assisted seismic history matching to the Schiehallion field. In the assisted approach, the optimization loop is driven by a stochastic algorithm, while the manual workflow is based on a qualitative comparison of 4D seismic maps. By upscaling we obtained an order of magnitude gain in performance. Accurate upscaling was ensured by thorough volume and transmissibility calculation within regions. The parameterisation of the problem is based on a pattern of seismically derived geobodies with specified transmissibility multipliers between the regions. Seismic predictions are made through petroelastic modelling, 1D convolution, coloured inversion and calculation of different attributes.We were able to achieve a reasonable match of production and 4D seismic data using coarse scale models in manual and assisted approaches. We observed that the misfit surfaces are different when working in the various seismic domains considered. Use of equivalent domains for observed and predicted data was found to give a more unique misfit response and better result.Accurate comparison of predicted and observed 4D seismic data in different domains is necessary for tackling nonuniqueness of the inverse problem and hence reducing the uncertainty of field development predictions.

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Effect of Scale Dependent Data Correlations in an Integrated History Matching Loop Combining Production Data and 4D Seismic Data

Cited by 63 publications

References 17 publications

Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter

Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter

Quantitative Use of 4D Seismic Data for Reservoir Description

Assisted Seismic History Matching in Different Domains: What Seismic Data Should We Compare?

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