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Abstract: To characterize a petroleum reservoir and to develop a reliable static and dynamic model for assessing resources and reserves, the rock and fluid properties of the subsurface formation are modeled. In most cases, multiple reservoir properties (categorical and continuous variables) are jointly utilized to represent rock properties. Standard practice is to model and characterize the categorical properties of the reservoir such as facies, before modeling the continuous petrophysical properties. A framework that applies the Hierarchical Truncated Pluri Gaussian (HTPG) and Projection Pursuit Multivariate Transformation (PPMT) techniques for multivariate modeling of rock properties in a petroleum reservoir is developed and documented in this thesis. The HTPG technique and PPMT workflow are established methods for modeling categorical and continuous variables, respectively. Unlike other truncated simulation techniques such as Truncated Gaussian Simulation (TGS) and Truncated Pluri Gaussian Simulation (TPGS), HTPG can be applied to a complex geological domain and does not limit the number of latent Gaussian variables that are used. Although the traditional modeling workflow for multivariate continuous property modeling reproduces important univariate statistics, it does not reproduce the complexity in the multivariate data, and this leads to models that do not reproduce important characteristics of the reservoir. To overcome this limitation and for ease in the geostatistical modeling workflow, the PPMT workflow is implemented. The PPMT decorrelates the multivariate variable and transforms them into univariate Gaussian variables that can be modeled independently. This proposed integrated modeling method is implemented on the Hekla Reservoir. The HTPG simulation technique is applied to model the five facies in the Hekla reservoir to generate one hundred simulated realizations of the facies, while the PPMT technique is subsequently applied to model the continuous petrophysical properties (porosity and permeability) distributed in the reservoir. This framework is reproducible and applicable to both mining and petroleum projects, which often require multiple categorical and continuous variables of geologic deposits or reservoirs.

Abstract: The McMurray Formation contains complex geological features that were partially formed in a fluvial-estuarine depositional environment. These geological features that are interrelated to each other exist with different shapes, patterns, and sizes. The inclined heterolithic strata (IHS) formed as part of kilometer scale channels contain continuous centimeter scale features that are important for flow characterization of Steam Assisted Gravity Drainage (SAGD) recovery processes. Modelling the detailed facies in such depositional systems requires a methodology that integrates the shape and size of geological architectures and reflects heterogeneity over many scales. Object-based and event-based modelling provide the framework to integrate geological architectures into geostatistical facie models however, representation of geological architectures with discretized grid would result in the loss of small scale heterogeneity. This dissertation presents algorithms and workflows on grid-free modelling that allows modelling of multi-scale geological features without relating them to a grid system. The contributions of this work include 1) the development of a stochastic object-based algorithm for modelling of tidal channelized systems, 2) the development of a grid-free object-based algorithm that allows modelling of multi-scale geological features in the McMurray Formation, and 3) the construction of a grid-free training image library for the application of McMurray Formation facies modelling. The details of algorithms and workflows are discussed. The performance of the proposed methodology is compared to conventional geostatistical modelling techniques through thermal flow simulations.

The primary controls on subsurface reservoir heterogeneities and fluid flow characteristics are sedimentary facies architecture and petrophysical rock fabric distribution in clastic reservoirs and in carbonate reservoirs, respectively. Facies models are critical and fundamental for summarizing facies and facies architecture in data-rich areas. Facies models also assist in predicting the spatial architectural trend of sedimentary facies in other areas where subsurface information is lacking. The method for transferring geological information from different facies models into digital data and then generating associated numerical models is called facies modeling or geological modeling. Facies modeling is also vital to reservoir simulation and reservoir characterization analysis. By extensively studying and reviewing the relevant research in the published literature, this report identifies and analyzes the best and most detailed geologic data that can be used in facies modeling, and the most current geostatistical and stochastic methods applicable to facies modeling. Through intensive study of recent literature, the author (1) summarizes the basic concepts and their applications to facies and facies models, and discusses a variety of numerical modeling methods, including geostatistics and stochastic facies modeling, such as variogram-based geostatistics modeling, object-based stochastic modeling, and multiple-point geostatistics modeling; and (2) recognizes that the most effective way to characterize reservoir is to integrate data from multiple sources, such as well data, outcrop data, modern analogs, and seismic interpretation. Detailed and more accurate parameters using in facies modeling, including grain size, grain type, grain sorting, sedimentary structures, and diagenesis, are gained through this multidisciplinary analysis. The report concludes that facies and facies models are scale dependent, and that attention should be paid to scale-related issues in order to choose appropriate methods and parameters to meet facies modeling requirements.

The primary controls on subsurface reservoir heterogeneities and fluid flow characteristics are sedimentary facies architecture and petrophysical rock fabric distribution in clastic reservoirs and in carbonate reservoirs, respectively. Facies models are critical and fundamental for summarizing facies and facies architecture in data-rich areas. Facies models also assist in predicting the spatial architectural trend of sedimentary facies in other areas where subsurface information is lacking. The method for transferring geological information from different facies models into digital data and then generating associated numerical models is called facies modeling or geological modeling. Facies modeling is also vital to reservoir simulation and reservoir characterization analysis. By extensively studying and reviewing the relevant research in the published literature, this report identifies and analyzes the best and most detailed geologic data that can be used in facies modeling, and the most current geostatistical and stochastic methods applicable to facies modeling. Through intensive study of recent literature, the author (1) summarizes the basic concepts and their applications to facies and facies models, and discusses a variety of numerical modeling methods, including geostatistics and stochastic facies modeling, such as variogram-based geostatistics modeling, object-based stochastic modeling, and multiple-point geostatistics modeling; and (2) recognizes that the most effective way to characterize reservoir is to integrate data from multiple sources, such as well data, outcrop data, modern analogs, and seismic interpretation. Detailed and more accurate parameters using in facies modeling, including grain size, grain type, grain sorting, sedimentary structures, and diagenesis, are gained through this multidisciplinary analysis. The report concludes that facies and facies models are scale dependent, and that attention should be paid to scale-related issues in order to choose appropriate methods and parameters to meet facies modeling requirements.