Use of Quantitative 4D-Seismic Data in Automatic History Match

Portella, Ricardo Cunha Mattos; Emerick, Alexandre A.

doi:10.2523/94650-ms

Cited by 7 publications

(4 citation statements)

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“…It is becoming common for time-lapse (4D) seismic surveys to be used through the production life of a reservoir in order to better monitor fluid displacement (Hatchel et al 2002;Waggoner et al 2002;Vasco et al 2003;Lygren et al 2003;Portella and Mezghani et al 2004;Emerick 2005;Huang and Lin 2006;Staples et al 2006;Emerick et al 2007). On the other hand, using assisted and automatic methods for history matching (Arenas et al 2001;Gosselin et al 2001;Aanonsen et al 2003;Clifford et al 2003;Wang et al 2005;Dong and Oliver 2005;Stephen et al 2006;Jin et al 2009;Aanonsen et al 2009;Kazemi and Stephen 2009; helps us to find a better representation of the reservoir geologically as well as improving predictions of fluid flow behaviour.…”

Section: Introductionmentioning

confidence: 99%

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Stephen

Kazemi

2014

Geophysical Prospecting

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Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.

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Section: Introductionmentioning

confidence: 99%

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Stephen

Kazemi

2014

Geophysical Prospecting

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“…Several studies have been reported over the past 10 years in which quantitative approaches were investigated for incorporating the large number of data associated with seismic acquisition into existing workflows for assisted history matching of production data (e.g., Arenas et al 2001;Fagervik et al 2001;Gosselin et al 2001;van Ditzhuijzen et al 2001;Dong and Oliver 2005;Haverl et al 2005;Portella and Emerick 2005;Stephen et al 2006;Dadashpour et al 2007;Roggero et al 2007). For computational reasons, the workflow used in most of these studies requires significant reduction of the uncertainty space, or they produce only a single history-matched model.…”

Section: Introductionmentioning

confidence: 99%

Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter

Trani

Arts²,

Leeuwenburgh³

2012

SPE Journal

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Time-lapse seismic data provide information on the dynamics of multiphase reservoir fluid flow in places where no production data from wells are available. This information, in principle, could be used to estimate unknown reservoir properties. However, the amount, resolution, and character of the data have long posed significant challenges for quantitative use in assisted-historymatching workflows. Previous studies, therefore, have generally investigated methods for updating single models with reduced parameter-uncertainty space. Recent developments in ensemblebased history-matching methods have shown the feasibility of multimodel history and matching of production data while maintaining a full uncertainty description. Here, we introduce a robust and flexible reparameterization for interpreted fluid fronts or seismic attribute isolines that extends these developments to seismic history matching. The seismic data set is reparameterized, in terms of arrival times, at observed front positions, thereby significantly reducing the number of data while retaining essential information. A simple 1D example is used to introduce the concepts of the approach. A synthetic 3D example, with spatial complexity that is typical for many waterfloods, is examined in detail. History-matching cases based on both separate and combined use of production and seismic data are examined. It is shown that consistent multimodel history matches can be obtained without the need for reduction of the parameter space or for localization of the impact of observations. The quality of forecasts based on the history-matched models is evaluated by simulating both expected production and saturation changes throughout the field for a fixed operating strategy. It is shown that bias and uncertainty in the forecasts of production both at existing wells and in the flooded area are reduced considerably when both production and seismic data are incorporated. The proposed workflow, therefore, enables better decisions on field developments that require optimal placement of infill wells.

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“…Portella and Emerick (2005) already reported the importance of properly considering the weights for both production and seismic data terms when computing the objective function. Portella and Emerick (2005) already reported the importance of properly considering the weights for both production and seismic data terms when computing the objective function.…”

Section: Introductionmentioning

confidence: 99%

The Marlim Field: Incorporating Time-Lapse Seismic in the Assisted History Matching

Brito

Moraes

Emerick

2010

SPE Latin American and Caribbean Petroleum Engineering Conference

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History matching is one of the most important and time consuming tasks in reservoir simulation. In traditional history matching approaches engineers attempt to honor production data by manually modifying their simulation models. Even though such approaches are being gradually replaced by assisted history matching methods the engineer is still responsible for choosing which simulation model parameters must be modified. On the other hand optimization algorithms available in assisted history matching tools allow changing parameter values automatically and also to consider 4D survey data in the objective function. Transmissibility multipliers associated to regions located between wells are among the most traditional simulation model parameters used for history matching purposes. Such procedure is very efficient in order to match water production data, but usually leads to transmissibility maps which have no geological meaning. This paper describes the results of the assisted history matching study of Marlim Field, the biggest offshore field in Brazil. The assisted history matching presented here included both production and 4D seismic. The seismic information was used qualitatively and quantitatively, i.e., to delimitate regions associated to transmissibility multipliers and also in the objective function itself. The assisted 4D history matching was carried out with a commercial history matching tool, which allows the synthetic seismic calculation.The production history data of most part of the wells was matched with similar accuracy one would expect to obtain through a careful and time consuming manual history matching approach. However the total elapsed time achieved here was orders of magnitude smaller. In addition the shape of the regions associated with model parameter values was totally supported by 4D seismic information.It turns out even with the aid of assisted history matching tools, the quality of the results strongly depends on adequately choosing which simulation model parameters must be modified in order to match the production data. Once 4D seismic data can give a realistic picture concerning reservoir fluid replacement it can also highlight preferential permeability pathways. The results of the present study have shown these indications can be very helpful to delimitate regions with history matching purposes that have geological significance.

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Use of Quantitative 4D-Seismic Data in Automatic History Match

Cited by 7 publications

References 0 publications

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter

The Marlim Field: Incorporating Time-Lapse Seismic in the Assisted History Matching

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