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A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty

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Abstract

Geologic uncertainties and limited well data often render recovery forecasting a difficult undertaking in typical appraisal and early development settings. Recent advances in geologic modeling algorithms permit automation of the model generation process via macros and geostatistical tools. This allows rapid construction of multiple alternative geologic realizations. Despite the advances in geologic modeling, computation of the reservoir dynamic response via full-physics reservoir simulation remains a computationally expensive task. Therefore, only a few of the many probable realizations are simulated in practice. Experimental design techniques typically focus on a few discrete geologic realizations as they are inherently more suitable for continuous engineering parameters and can only crudely approximate the impact of geology. A flow-based pattern recognition algorithm (FPRA) has been developed for quantifying the forecast uncertainty as an alternative. The proposed algorithm relies on the rapid characterization of the geologic uncertainty space represented by an ensemble of sufficiently diverse static model realizations. FPRA characterizes the geologic uncertainty space by calculating connectivity distances, which quantify how different each individual realization is from all others in terms of recovery response. Fast streamline simulations are employed in evaluating these distances. By applying pattern recognition techniques to connectivity distances, a few representative realizations are identified within the model ensemble for full-physics simulation. In turn, the recovery factor probability distribution is derived from these intelligently selected simulation runs. Here, FPRA is tested on an example case where the objective is to accurately compute the recovery factor statistics as a function of geologic uncertainty in a channelized turbidite reservoir. Recovery factor cumulative distribution functions computed by FPRA compare well to the one computed via exhaustive full-physics simulations.

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Authors and Affiliations

  1. Shell International Exploration and Production Inc., 3737 Bellaire Blvd., Houston, TX, 77025, USA

    Faruk O. Alpak & Mark D. Barton

  2. Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Green Earth Sciences 353, Stanford, CA, 94305, USA

    Jef Caers

Authors
  1. Faruk O. Alpak
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  2. Mark D. Barton
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  3. Jef Caers
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Correspondence to Faruk O. Alpak.

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Alpak, F.O., Barton, M.D. & Caers, J. A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty. Comput Geosci 14, 603–621 (2010). https://doi.org/10.1007/s10596-009-9175-5

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  • DOI: https://doi.org/10.1007/s10596-009-9175-5

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