Reducing Reservoir Prediction Uncertainty by Updating a Stochastic Model Using Seismic History Matching
- Karl D. Stephen (Heriot-Watt U.) | Colin Macbeth (Edinburgh Anisotropy Project UK)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2008
- Document Type
- Journal Paper
- 991 - 999
- 2008. Society of Petroleum Engineers
- 7.2.2 Risk Management Systems, 5.5.8 History Matching, 4.3.4 Scale, 5.6.10 Seismic (Four Dimensional) Monitoring, 5.1.5 Geologic Modeling, 5.2.1 Phase Behavior and PVT Measurements, 5.6.1 Open hole/cased hole log analysis, 5.2 Reservoir Fluid Dynamics, 5.1.8 Seismic Modelling, 5.5 Reservoir Simulation, 2.4.3 Sand/Solids Control, 4.1.2 Separation and Treating, 5.1.1 Exploration, Development, Structural Geology, 5.1 Reservoir Characterisation, 5.1.9 Four-Dimensional and Four-Component Seismic, 1.6 Drilling Operations, 1.2.3 Rock properties, 2.3.4 Real-time Optimization, 4.1.5 Processing Equipment, 3.3 Well & Reservoir Surveillance and Monitoring, 5.5.3 Scaling Methods, 5.1.2 Faults and Fracture Characterisation
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We have developed a method in which spatial and dynamic information offered by time-lapse, or 4D, seismic surveys is used in history matching of reservoir simulations. Improved predictions of both recovery and areal sweep are then obtained by reducing uncertainty. Flow simulations are converted to predictions of seismic-impedance attributes using a petroelastic transform and suitable rescaling. The resulting misfit between the model and observed data is combined with an equivalent measure for well data, and these are used to constrain simulations by iteratively updating the model. Updated-model probabilities can then be used to analyze uncertainty.
The method has been applied to the Schiehallion UK Continental Shelf (UKCS) reservoir. We first found a good match to production and seismic data in the field. From the updated probability distribution of the parameters, we then took the best models from the history-matching process and made predictions to determine the most likely outcomes.
We have found that the 4D data reduces uncertainty in predictions of the areal sweep and the pressure distribution. The seismic response is strongest at the injector wells but also helps in the interwell regions. Conventional history matching often struggles to constrain parameters in these regions because of the inherent nonuniqueness of the problem. The uncertainty of permeability- and fault-transmissibility multipliers was also determined in those areas.
Reservoir management may be improved if the present state of the field is known and if changes may be predicted. The former requires information about current fluid-sweep and pressure changes, while the latter requires accurate reservoir description and a predictive tool, such as a simulation model. With this information, important decisions can then be made including facility maintenance and well optimization, but more importantly, unswept areas can be identified and new wells drilled.
Conventionally, simulation models have been used to estimate the possible reservoir state and predict behavior. The modeling commonly begins with the geologist who creates a number of static geomodels, often constrained to log and core data from wells in addition to preproduction 2D- or 3D-seismic data. The models may be upscaled by the geologist and then modified by an engineer so that they match static and dynamic well data, including fluid-production rates and pressures [e.g., Ertekin et al. (2001)]. Conventionally, this history-matching approach involves changing model parameters manually, though automated methods are increasingly being used. Nonuniqueness of the models can hamper the process because of missing information, particularly regarding changes in the fluid distributions and pressures between wells.
4D-seismic surveys can provide this information and reduce the nonuniqueness. Repeated 3D-seismic surveys can be compared to identify changes in fluid saturation and/or pressures and this is performed qualitatively, and almost routinely, in a number of North Sea and Gulf of Mexico fields [e.g., various papers in Parker et al. (2003)]. The goal for many geoscientists and engineers is to integrate this data so that it may be used quantitatively to constrain simulation models and improve predictive capability [e.g., Gosselin et al. (2003) and Mezghani et al. (2004)].
To obtain such improvements, we have developed an automated-history-matching method that includes 4D seismic data along with production data, on the basis of an integrated workflow (Fig. 1). The method uses a quasiglobal stochastic method for choosing new models on the basis of calculated misfits between observed and predicted data. We have applied our method to the Schiehallion UKCS turbidite reservoir, in which 4D seismic data have shown great promise (Chapin et al. 2000; Parr et al. 2000; Saxby et al. 2001). We updated the operator's model and obtained an improved match to seismic data while retaining the good match to production data already present. Finally, the uncertainty of the parameters and predicted behavior was analyzed.
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