A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty |
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| Authors: | Faruk O Alpak Mark D Barton Jef Caers |
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| Institution: | (1) School of Marine Sciences, University of Maine, Darling Marine Center, 193 Clark’s Cove Rd, Walpole, ME 04573, USA;(2) Applied Marine Physics, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600, Rickenbacker Causeway, Miami, FL 33149, USA;(3) De La Salle University, 2401 Taft Avenue, 1004 Manila, Philippines;(4) Silliman University Angelo King Center for Research and Environmental Management, Silliman University, Dumaguete City, 6200, Philippines;(5) Department of Biological Sciences, Old Dominion University, Norfolk, VA 23529, USA;(6) Great Barrier Reef Marine Park Authority, Townsville, 4810, Australia;(7) School of Marine Biology and Aquaculture, James Cook University, Townsville, QLD, 4811, Australia;(8) International Network on Water, Environment and Health, United Nations University, 175 Longwood Road South Suite 204, Hamilton, ON, L8P OA1, Canada |
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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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