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Abstract:
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As oil and gas supply decrease , it becomes more important to quantify the uncertainty associated with reservoir models and implementation of field development decisions . Various geostatistical methods have assisted in the development of field scale models of reservoir heterogeneity . Sequential simulation algorithms in geostatistic require an assessment of local uncertainty in an attribute value at a location followed by random sampling from the uncertainty distribution to retrieve the simulation value . Instead of random sampling of an outcome from the uncertainty distrubution , the retrieval of an optimal simulated value at each location by considering an economic loss function is demonstrated in this thesis .
By applying a loss function that depicts the economic impact of an over or underestimation at a location and retrieving the optimal simulated value that minimizes the expected loss , a map of simulated values can be generated that accounts for the impact of permeability as it relates to economic loss . Both an asymmetric linear loss function and a parabolic loss function models are investigated . The end result of this procedure will be a reservoir realization that exhibits the correct spatial characteristics (i .e . variogram reproduction ) while , at the same time , exhibiting the minimum expected loss in terms of the parameters used to construct the loss function .
The process detailed in this thesis provides an effective alternative whereby realizations in the middle of the uncertainty distribution can be directly retrieved by application of suitable loss functions . An extension of this method is to alter the loss function (so as to emphasize either under or over estimation ) , other realizations at the extremes of the global uncertainty distribution can also be retrieved , thereby eliminating the necessity for the generation of a large suite of realizations to locate the global extremes of the uncertainty distribution . |