Browsing by Subject "Reservoir Modeling"
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Item Development of reservoir models using economic loss functions(2009-05) Kilmartin, Donovan James; Srinivasan, Sanjay; Lake, Larry W.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.Item Performance of Assisted History Matching Techniques When Utilizing Multiple Initial Geologic Models(2011-11-15) Aggarwal, AkshayHistory matching is a process wherein changes are made to an initial geologic model of a reservoir, so that the predicted reservoir performance matches with the known production history. Changes are made to the model parameters which include rock and fluid parameters (viscosity, compressibility, relative permeability, etc.) or properties within the geologic model. Assisted History Matching (AHM) provides an algorithmic framework to minimize the mismatch in simulation, and aids in accelerating this process. The changes made by AHM techniques, however, cannot ensure a geologically consistent reservoir model. In fact, the performance of these techniques depends on the initial starting model. In order to understand the impact of the initial model, this project explored the performance of the AHM approach using a specific field case, but working with multiple distinct geologic scenarios. This project involved an integrated seismic to simulation study, wherein I interpreted the seismic data, assembled the geological information, and performed petrophysical log evaluation along with well test data calibration. The ensemble of static models obtained was carried through the AHM methodology. I used sensitivity analysis to determine the most important dynamic parameters that affect the history match. These parameters govern the large scale changes in the reservoir description and are optimized using the Evolutionary Strategy Algorithm. Finally, the streamline based techniques were used for local modifications to match the water cut well by well. The following general conclusions were drawn from this study- a) The use of multiple simple geologic models is extremely useful in screening possible geologic scenarios and especially for discarding unreasonable alternative models. This was especially true for the large scale architecture of the reservoir. b) The AHM methodology was very effective in exploring a large number of parameters, running the simulation cases, and generating the calibrated reservoir models. The calibration step consistently worked better if the models had more spatial detail, instead of the simple models used for screening. c) The AHM methodology implemented a sequence of pressure and water cut history matching. An examination of specific models indicated that a better geologic description minimized the conflict between these two match criteria.