A method for performing history matching of reservoir flow models using 4D seismic data

Lygren, M.; Fagervik, K.; Valen, T.S.; Hetlelid, A.; Berge, Geir Thore; Dahl, Geir; Sønneland, L.; Lie, H.E.; Magnus, I.

doi:10.1144/1354-079302-532

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…This method of conditioning static and dynamic modelling with 4D seismic data, is similar to 4D integrated reservoir modelling (4D IRM) workflows described elsewhere (e.g. Staples et al 2002Staples et al , 2005Lygren et al 2003;Waggoner et al 2003).…”

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confidence: 99%

Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion Field, UK

Gainski

MacGregor

Freeman

et al. 2010

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This paper discusses integration of production surveillance techniques, focusing on the use of 4D seismic data to identify reservoir compartmentalization. We present two examples of recently drilled compartments that were successfully identified following integration of surveillance data with detailed reservoir modelling work. Our examples are from the faulted, Paleocene channelized turbidite reservoirs of the Schiehallion oil field, offshore West of Shetlands, U.K. The first example provides a good case history of a 4D Integrated Reservoir Modelling (4D IRM) approach -which involved integration of dynamic well data with 4D seismic, and iterative revision of geological and reservoir simulation models. Two newly identified targets were drilled and completed successfully using these techniques. The second example illustrates a situation where 4D seismic interpretation was key in identifying a new infill target.Production in the Schiehallion Field started in 1998, and the current development scheme totals 46 wells (22 producers and 24 water injectors). During the early years of production it became apparent that geological connectivity, fluid flow and pressure communication between wells was not as inter-connected as expected. As a result the number of wells required to maximize the recovery has more than doubled to that specified in the original development plan, and the number is expected to increase further as the field matures. Continuous collection of bottom hole pressure data from permanently installed gauges, well testing and production logging (PLT) supported by a regular time-lapse (4D) seismic programme are used to update conceptual thinking and thus constrain geological and flow simulation modelling. This data integration results in improved understanding of the static and dynamic reservoir compartmentalization and well connectivity.

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confidence: 99%

Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion Field, UK

Gainski

MacGregor

Freeman

et al. 2010

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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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“…These involve computation of a misfit between predicted and observed data, and then a mathematical routine is used to choose new models either stochastically or deterministically. Many researchers have investigated different inversion algorithms, parameterization schemes, and appropriate methods to compare data (Huang et al 1997;Gosselin et al 2001;Aanonsen et al 2001;Arenas et al 2003;Lygren et al 2003;Kretz et al 2002;Mantica et al 2001;Vasco et al 2003;Portella and Emerick 2005;Jin et al 2007;Roggero et al 2007;Skjervheim et al 2007). Often, observed acoustic-impedance changes, which may be of undefined units, are compared to equivalent synthetic seismic data (Guerillot and Pianelo 2000;Clifford et al 2003;Mezghani et al 2004;Emerick et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

Kazemi¹,

Stephen²,

Shams³

2011

SPE Reservoir Evaluation &Amp; Engineering

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History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching.We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data.The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse.Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil., .

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A method for performing history matching of reservoir flow models using 4D seismic data

Cited by 16 publications

References 8 publications

Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion Field, UK

Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion Field, UK

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 Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

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