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Petroleum Geoscience

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Integrating production history into reservoir models using streamline-based time-of-flight ranking

Department of Petroleum Engineering, Stanford University, Stanford, CA 94305-2220, USA(e-mail: kovscek@pangea.stanford.edu)

The reservoir models generated by geostatistical techniques, but unconstrained to production history, provide equally probable reservoir descriptions that honour observed geology. However, flow simulation results on these models may vary widely where there is geological uncertainty. Constraining geostatistical models to known production history reduces this uncertainty. To this end, a streamline-based algorithm is proposed for ranking geostatistical realizations with regard to production history. First, a rapid, streamline-based inversion method is applied to obtain a history-matched reservoir model. Then the streamline geometries and properties, such as the time-of-flight, are computed without full flow simulation for the history-matched model and the geostatistical models examined. Each model is compared to the history-matched model with regard to streamline properties. In this way, reservoir models that match production history and honour known geological information are obtained. Synthetic examples using up to 600 distinct reservoir models demonstrate computational efficiency and also show that the method readily selects the most appropriate permeability fields. Flow simulations confirm that the selected permeability fields are satisfactory. The technique also appears to be appropriate for downscaling history-matched reservoir models from coarse to fine grids.

KEYWORDS: history matching, streamlines, data integration, inverse modelling