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Abstract

Faults in reservoir simulation models are typically represented as discontinuities between grid cells with homogeneous transmissibility multipliers used to represent their effect on fluid flow. Transmissibility multipliers may be manually altered in a trial-and-error fashion to condition the model to production data, often at the expense of geological and physical realism. Methods developed in the past decade incorporate prior geological constraints on fault permeability and thickness to produce more realistic heterogeneous transmissibility multipliers. As a typical simulation grid may have several thousand faulted cell connections, conditioning of these properties to production data is not routine. We present a method to condition geologically-derived estimates of fault transmissibility to production data. Appropriate probability density functions are estimated from databases of fault permeability and thickness. Together with parameters for grid properties, these are used as input to a geostatistical property simulation workflow in a 3D geomodelling tool. Multiple property realisations are generated, with fault permeability being averaged into grid permeability and exported for use in numerical fluid flow simulations. Conditioning to production data is achieved using the Ensemble Kalman Filter (EnKF), which has been proved as an efficient approach for such problems. Since the EnKF can handle a large number of uncertain parameters and requires the forward model only as a ‘black box’, it allows for consideration of geological features, such as faults, as objects with spatially continuous heterogeneous properties that can be conditioned to production data. The method is illustrated using a producing North Sea reservoir. Grid and fault properties can be conditioned to production data while honouring the input probability distributions and spatial continuity model. Case-specific and more general conclusions can be drawn about the advantages of the method; potential improvements and extensions are discussed.

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/content/papers/10.3997/2214-4609.20146422
2008-09-08
2020-10-26
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20146422
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