Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification

dc.contributorDatta-Gupta, Akhil
dc.creatorOlalotiti-Lawal, Feyisayo
dc.date.accessioned2015-08-01T05:48:32Z
dc.date.accessioned2017-04-07T20:05:11Z
dc.date.available2015-08-01T05:48:32Z
dc.date.available2017-04-07T20:05:11Z
dc.date.created2013-08
dc.date.issued2013-05-17
dc.description.abstractRapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks and uncertainties in these evaluations. These ideally require several full-scale numerical simulations which is time consuming, impractical, if not impossible to do with conventional (Finite Difference) simulators in real life situations. In this research, the aim will be to improve on the efficiencies associated with these tasks. This involved exploring the applications of Fast Marching Methods (FMM) in both conventional and unconventional reservoir characterization problems. In this work, we first applied the FMM for rapidly ranking multiple equi-probable geologic models. We demonstrated the suitability of drainage volume, efficiently calculated using FMM, as a surrogate parameter for field-wide cumulative oil production (FOPT). The probability distribution function (PDF) of the surrogate parameter was point-discretized to obtain 3 representative models for full simulations. Using the results from the simulations, the PDF of the reservoir performance parameter was constructed. Also, we investigated the applicability of a higher-order-moment-preserving approach which resulted in better uncertainty quantification over the traditional model selection methods. Next we applied the FMM for a hydraulically fractured tight oil reservoir model calibration problem. We specifically applied the FMM geometric pressure approximation as a proxy for rapidly evaluating model proposals in a two-stage Markov Chain Monte Carlo (MCMC) algorithm. Here, we demonstrated the FMM-based proxy as a suitable proxy for evaluating model proposals. We obtained results showing a significant improvement in the efficiency compared to conventional single stage MCMC algorithm. Also in this work, we investigated the possibility of enhancing the computational efficiency for calculating the pressure field for both conventional and unconventional reservoirs using FMM. Good approximations of the steady state pressure distributions were obtained for homogeneous conventional waterflood systems. In unconventional system, we also recorded slight improvement in computational efficiency using FMM pressure approximations as initial guess in pressure solvers.
dc.identifier.urihttp://hdl.handle.net/1969.1/150964
dc.language.isoen
dc.subjectFast Marching Method
dc.subjectGeologic Model Ranking
dc.subjectModel Calibration
dc.subjectTwo-Stage MCMC
dc.subjectGeometric Pressure Approximation
dc.titleApplication of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification
dc.typeThesis

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