Browsing by Subject "Reservoir model"
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Item Growthism: A Complete Framework for Integrating Static and Dynamic Data Into Reservoir Models(2008-05) Eskandaridalvand, Kiomars; Srinivasan, SanjayAccurate characterization of subsurface oil reservoirs is an essential prerequisite to the design and implementation of enhanced oil recovery (EOR) scenarios. Specifically, in reservoir characterization, integrating static and dynamic data into reservoir models to construct accurate and realistic models has received considerable attention. Unlike most of the conventional geostatistical approaches of integrating data into reservoir models that are based on semi-variograms (two-point statistics) as a measure of spatial connectivity, a complete multiple-point statistics framework is presented in this dissertation. In contrast to two-point statistical methods, multiple pointstatistics-based methods are capable of reproducing curvilinear geological structures. The algorithm starts with extracting multiple-point (mp) statistics from training images (conceptual geological description) using an optimal spatial template. After collecting different patterns (data configurations) and building the multiple -point histogram, the pattern reproduction process commences. It begins from data locations (simulatable nodes) and then grows to fill the entire reservoir domain. The algorithm accounts for three main practical issues: uncertainty in geological scenarios, deriving scanning template and non-stationarity. The current algorithm, unlike others, for the cases with many possible geological scenarios ranks the training images based on the consistency between the training images and hard conditioning data. A fast and robust algorithm to derive optimal spatial templates is presented that is based on a semi-automated procedure. Results prove that pattern reproduction using the optimal template is better than using just an arbitrary template specified by the user. Growthsim is capable of integrating data from multiple data sources. Non-stationarity, in terms of variations in facies proportion, can be represented and synthetic and field examples are presented in this dissertation. The conventional approach to integrate production information into reservoir models is by iterative perturbation of the reservoir model until the production history of the reservoir is matched. Iterative methods have been applied to random fields that are completely characterized by a two-point covariance function. An alternate novel technique, as implemented in this research, is based on the merging of multiple-point statistics inferred from history matched and geological models. Pattern growth is performed subsequently by sampling from the merged multiple-point histograms. History matched models, using the presented approach, show an excellent agreement with underlying geological descriptions and match production history. It is demonstrated that the procedure yields a reliable reservoir model best suited for flow prediction.Item Stochastic Characterization of Carbonate Buildup Architectures, Using Two-And Multiple-Point Statistics, and Statistical Evaluation of These Methods(2009-05) Madriz, Darrin Dudley; Lake, Larry W.; Janson, XavierAccurate reproduction of complex geological architectures is necessary to build realistic reservoir models, especially since these architectures are often important to flow behavior.Classical geostatistics presents severe modeling limitations because it only accounts for one-and two-point structural information. Complex geological structures cannot be captured based on these very restraining measures of spatial continuity (Caers, 2005). A particular heterogeneous architecture,significant to the oil industry, is carbonate buildups. The objective of this thesis is to “better”characterize common geo-structures and architectural elements that are often present in carbonate buildups. A typical example from the Sacramento Mountains in Southern New Mexico is analyzed, thenmodeled,using two main geostatistical algorithms: two-point statistics (TPS)-based SISIM, and multiple-point statistics (MPS)-based SNESIM. Both models are conditioned to facies pseudo-wells interpreted from high-resolution LIght Detecting And Ranging (LIDAR) images of outcrops, themselves located in three canyons that intersect the buildups.MPS-based algorithms extract structural information from conceptual 3D representations of reservoirs, called training images, that depict prior geological understanding. Algorithms are designed and coded to generate geologically realistic training images of carbonate buildups. Advanced MPS techniques, such as subgrid and multigrid simulations, targeted global proportions, and vertical proportion maps,are required to adequately model this complex carbonate stratigraphy. The MPS-based model is compared to a TPS-based model, representative of traditional geostatistical models, by quantifying their similarities to the training image (true reference) using statistical and heterogeneity measures. Assuming that visual, statistical and flow behavior similarity to the training image indicates a“better” model, we conclude that the MPS-based model, as compared to its TPS analog, is the “better” alternative for the particular algal buildup studied. Despite this result, this thesis does not suggest that MPS techniques are, in general, the “superior” alternative. On the contrary, based on the parallel modeling performed of both simple and complex architectural elements, we observe that the more “appropriate” stochastic technique is not only application-specific, but also goal-and data-driven.Item A Study on the Use of Proxy Reservoir Models in Field Development Optimization and Value of Information Problems(2008-08) Purwar, Suryansh; Jablonowski, Christopher J.; Nguyen, Quoc P.Mitigating uncertainty in subsurface variables to optimize field development is an intriguing problem still lacking a robust solution. The complexity is further increased by design constraints and quasi-irreversible capital investments. Although versatile optimization techniques and powerful reservoir simulators are available, the primary reason for the lack of a comprehensive solution is the disconnect between optimization and reservoir simulation studies. In this work, we propose an approach that offers a reasonable way to do this. For handling the uncertainties, the proven approach of Experimental Design was used to create cases of possible combination of reservoir variables which were then run in CMG simulator to generate production profiles. Response Surface methodology was employed to generate numerical or ‘proxy’ oil production models from the reservoir simulation results. The numerical models thus generated were plugged into an optimizing model in GAMS to get the desired facility plan. A workflow with these components also allows us to look at the value of buying more information to mitigate uncertainty and the value of incorporating a ‘real’ option of modifying facilities after n years to support initial capital investment decisions