Browsing by Subject "Model reduction"
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Item Heterogeneous Reservoir Characterization Utilizing Efficient Geology Preserving Reservoir Parameterization through Higher Order Singular Value Decomposition (HOSVD)(2015-01-21) Afra, SardarPetroleum reservoir parameter inference is a challenging problem to many of the reservoir simulation work flows, especially when it comes to real reservoirs with high degree of complexity and non-linearity, and high dimensionality. In fact, the process of estimating a large number of unknowns in an inverse problem lead to a very costly computational effort. Moreover, it is very important to perform geologically consistent reservoir parameter adjustments as data is being assimilated in the history matching process, i.e., the process of adjusting the parameters of reservoir system in order to match the output of the reservoir model with the previous reservoir production data. As a matter of fact, it is of great interest to approximate reservoir petrophysical properties like permeability and porosity while reparameterizing these parameters through reduced-order models. As we will show, petroleum reservoir models are commonly described by in general complex, nonlinear, and large-scale, i.e., large number of states and unknown parameters. Thus, having a practical approach to reduce the number of reservoir parameters in order to reconstruct the reservoir model with a lower dimensionality is of high interest. Furthermore, de-correlating system parameters in all history matching and reservoir characterization problems keeping the geological description intact is paramount to control the ill-posedness of the system. In the first part of the present work, we will introduce the advantages of a novel parameterization method by means of higher order singular value decomposition analysis (HOSVD). We will show that HOSVD outperforms classical parameterization techniques with respect to computational and implementation cost. It also, provides more reliable and accurate predictions in the petroleum reservoir history matching problem due to its capability to preserve geological features of the reservoir parameter like permeability. The promising power of HOSVD is investigated through several synthetic and real petroleum reservoir benchmarks and all results are compared to that of classic SVD. In addition to the parameterization problem, we also addressed the ability of HOSVD in producing accurate production data comparing to those of original reservoir system. To generate the results of the present work, we employ a commercial reservoir simulator known as ECLIPSE. In the second part of the work, we will address the inverse modeling, i.e., the reservoir history matching problem. We employed the ensemble Kalman filter (EnKF) which is an ensemble-based characterization approach to solve the inverse problem. We also, integrate our new parameterization technique into the EnKF algorithm to study the suitability of HOSVD based parameterization for reducing the dimensionality of parameter space and for estimating geologically consistence permeability distributions. The results of the present work illustrates the characteristics of the proposed parameterization method by several numerical examples in the second part including synthetic and real reservoir benchmarks. Moreover, the HOSVD advantages are discussed by comparing its performance to the classic SVD (PCA) parameterization approach. In the first part of the present work, we will introduce the advantages of a novel parameterization method by means of higher order singular value decomposition analysis (HOSVD). We will show that HOSVD outperforms classical parameterization techniques with respect to computational and implementation cost. It also, provides more reliable and accurate predictions in the petroleum reservoir history matching problem due to its capability to preserve geological features of the reservoir parameter like permeability. The promising power of HOSVD is investigated through several synthetic and real petroleum reservoir benchmarks and all results are compared to that of classic SVD. In addition to the parameterization problem, we also addressed the ability of HOSVD in producing accurate production data comparing to those of original reservoir system. To generate the results of the present work, we employ a commercial reservoir simulator known as ECLIPSE. In the second part of the work, we will address the inverse modeling, i.e., the reservoir history matching problem. We employed the ensemble Kalman filter (EnKF) which is an ensemble-based characterization approach to solve the inverse problem. We also, integrate our new parameterization technique into the EnKF algorithm to study the suitability of HOSVD based parameterization for reducing the dimensionality of parameter space and for estimating geologically consistence permeability distributions. The results of the present work illustrate the characteristics of the proposed parameterization method by several numerical examples in the second part including synthetic and real reservoir benchmarks. Moreover, the HOSVD advantages are discussed by comparing its performance to the classic SVD (PCA) parameterization approach.Item Quantifying and mitigating wind power variability(2015-12) Niu, Yichuan; Santoso, Surya; Arapostathis, Aristotle; Baldick, Ross; Longoria, Raul G.; Tiwari, MohitUnderstanding variability and unpredictability of wind power is essential for improving power system reliability and energy dispatch in transmission and distribution systems. The research presented herein intends to address a major challenge in managing and utilizing wind energy with mitigated fluctuation and intermittency. Caused by the varying wind speed, power variability can be explained as power imbalances. These imbalances create power surplus or deficiency in respect to the desired demand. To ameliorate the aforementioned issue, the fluctuating wind energy needs to be properly quantified, controlled, and re-distributed to the grid. The first major study in this dissertations is to develop accurate wind turbine models and model reductions to generate wind power time-series in a laboratory time-efficient manner. Reliable wind turbine models can also perform power control events and acquire dynamic responses more realistic to a real-world condition. Therefore, a Type 4 direct-drive wind turbine with power electronic converters has been modeled and designed with detailed aerodynamic and electric parameters based on a given generator. Later, using averaging and approximation techniques for power electronic circuits, the order of the original model is lowered to boost the computational efficiency for simulating long-term wind speed data. To quantify the wind power time-series, efforts are made to enhance adaptability and robustness of the original conditional range metric (CRM) algorithm that has been proposed by literatures for quantitatively assessing the power variability within a certain time frame. The improved CRM performs better under scarce and noisy time-series data with a reduced computational complexity. Rather than using a discrete probability model, the improved method implements a continuous gamma distribution with parameters estimated by the maximum likelihood estimators. With the leverage from the aforementioned work, a wind farm level behavior can be revealed by analyzing the data through long-term simulations using individual wind turbine models. Mitigating the power variability by reserved generation sources is attempted and the generation scenarios are generalized using an unsupervised machine learning algorithm regarding power correlations of those individual wind turbines. A systematic blueprint for reducing intra-hour power variations via coordinating a fast- and a slow- response energy storage systems (ESS) has been proposed. Methods for sizing, coordination control, ESS regulation, and power dispatch schemes are illustrated in detail. Applying the real-world data, these methods have been demonstrated desirable for reducing short-term wind power variability to an expected level.Item Toward understanding predictability of climate: a linear stochastic modeling approach(Texas A&M University, 2004-11-15) Wang, FamingThis dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation. Subsequently, predictabilities of two specific physical systems are investigated within such framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection.Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However, a Predictable Component Analysis (PrCA) shows that advection can play a role in redistributing the predictable variance. This analytical result is further tested in a more realistic two-dimensional North Atlantic model with observed mean currents. The second is a linear coupled model of tropical Atlantic atmosphere-ocean system. Eigen-analysis reveals that the system has two types of coupled modes: a decadal meridional mode and an interannual equatorial mode. The meridional mode, which manifests itself as a dipole pattern in SST, is controlled by thermodynamic feedback between wind, latent heat flux, and SST, and modified by ocean heat transport. The equatorial mode, which manifests itself as an SST anomaly in the eastern equatorial basin, is dominated by dynamic feedback between wind, thermocline, upwelling, and SST. The relative strength of thermodynamic vs dynamic feedbacks determines the behavior of the coupled system, and enables the tropical Atlantic variability to be more predictable than the passive-ocean scenario.