Browsing by Subject "Model Calibration"
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Item Application of Fast Marching Method in Shale Gas Reservoir Model Calibration(2013-07-26) Yang, ChangdongUnconventional reservoirs are typically characterized by very low permeabilities, and thus, the pressure depletion from a producing well may not propagate far from the well during the life of a development. Currently, two approaches are widely utilized to perform unconventional reservoir analysis: analytical techniques, including the decline curve analysis and the pressure/rate transient analysis, and numerical simulation. The numerical simulation can rigorously account for complex well geometry and reservoir heterogeneity but also is time consuming. In this thesis, we propose and apply an efficient technique, fast marching method (FMM), to analyze the shale gas reservoirs. Our proposed approach stands midway between analytic techniques and numerical simulation. In contrast to analytical techniques, it takes into account complex well geometry and reservoir heterogeneity, and it is less time consuming compared to numerical simulation. The fast marching method can efficiently provide us with the solution of the pressure front propagation equation, which can be expressed as an Eikonal equation. Our approach is based on the generalization of the concept of depth of investigation. Its application to unconventional reservoirs can provide the understanding necessary to describe and optimize the interaction between complex multi-stage fractured wells, reservoir heterogeneity, drainage volumes, pressure depletion, and well rates. The proposed method allows rapid approximation of reservoir simulation results without resorting to detailed flow simulation, and also provides the time-evolution of the well drainage volume for visualization. Calibration of reservoir models to match historical dynamic data is necessary to increase confidence in simulation models and also minimize risks in decision making. In this thesis, we propose an integrated workflow: applying the genetic algorithm (GA) to calibrate the model parameters, and utilizing the fast marching based approach for forward simulation. This workflow takes advantages of both the derivative free characteristics of GA and the speed of FMM. In addition, we also provide a novel approach to incorporate the micro-seismic events (if available) into our history matching workflow so as to further constrain and better calibrate our models.Item Application of Fast Marching Methods for Rapid Reservoir Forecast and Uncertainty Quantification(2013-05-17) Olalotiti-Lawal, FeyisayoRapid 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.Item Thermomechanical Characterization and Modeling of Shape Memory Polymers(2010-01-16) Volk, Brent L.This work focuses on the thermomechanical characterization and constitutive model calibration of shape memory polymers (SMPs). These polymers have the ability to recover seemingly permanent large deformations under the appropriate thermomechanical load path. In this work, a contribution is made to both existing experimental and modeling efforts. First, an experimental investigation is conducted which subjects SMPs to a thermomechanical load path that includes varying the value of applied deformations and temperature rates. Specifically, SMPs are deformed to tensile extensions of 10% to 100% at temperature rates varying from 1 degree C /min to 5 degree C/min, and the complete shape recovery profile is captured. The results from this experimental investigation show that the SMP in question can recover approximately 95% of the value of the applied deformation, independent of the temperature rate during the test. The data obtained in the experimental investigation are then used to calibrate, in one-dimension, two constitutive models which have been developed to describe and predict the material response of SMPs. The models include a model in terms of general deformation gradients, thus making it capable of handling large deformations. In addition, the data are used to calibrate a linearized version of the constitutive model for small deformations. The material properties required for calibrating the constitutive models are derived from portions of the experimental results, and the model is then used to predict the shape memory effect for an SMP undergoing various levels of deformation. The model predictions are shown to match well with the experimental data.