The Jackson Purchase region of western Kentucky consists of Coastal Plain sediments near the northern margin of the Mississippi Embayment. Within this region is the Paducah Gaseous Diffusion Plant (PGDP), a uranium enrichment facility operated by the US Department of Energy. At PGDP, a Superfund site, soil and groundwater studies have provided subsurface lithologic data from hundreds of monitoring wells and borings. Despite preliminary efforts by various contractors, these data have not been utilized to develop detailed stratigraphic correlations of sedimentary units across the study area. In addition, sedimentary exposures along streams in the vicinity of PGDP have not been systematically described beyond the relatively simple geologic quadrangle maps published by the US Geological Survey in 196667. This study integrates lithologic logs, other previous site-investigation data, and outcrop mapping to provide a compilation of near-surface lithologic and stratigraphic data for the PGDP area. A database of borehole data compiled during this study has been provided to PGDP for future research and archival. Developments in understanding near-surface geology include the adoption of nomenclature used by the Illinois State Geological Survey (ISGS), which separates the Continental Deposits into two distinct units, the Mounds Gravel and Metropolis Formation, based on their unique depositional histories. Additionally, faulting presented on the preliminary Joppa (IL) 7.5-minute quadrangle map, but not mapped on the Joppa (KY) 7.5-minute quadrangle map, appears to have impacted deposition of post-Eocene sediments at the site. These faults are co-linear to zones of irregularity noted in the Cretaceous McNairy Formation structure elevation map created during this study, thick zones of the Mounds Gravel noted in an isopach map from this study, and contaminant plume maps created previously by contractors.

Little Cedar Creek field is a mature oil field located in southeastern Conecuh County, Alabama, in the northeastern Gulf of Mexico. As of May 2012, 12.5 MMBLS of oil and 14.8 MMCF of natural gas have been produced from the field area. The main reservoirs are microbial carbonate facies and associated nearshore high energy shoal facies of the Upper Jurassic Smackover Formation that overlie conglomerate and sandstone facies of the Norphlet Formation and underlie the argillaceous, anhydritic-carbonaceous facies of the Haynesville Formation. These carbonate reservoirs are composed of vuggy boundstone and moldic grainstone, and the petroleum trap is stratigraphic being controlled primarily by changes in depositional facies. To maximize recovery and investment in the field, an integrated geoscientific-engineering reservoir-wide development plan is needed, including reservoir characterization, modeling, and simulation. This research presents a workflow for geological characterization, formation evaluation, and 3D geologic modeling for fields producing from microbial carbonates and associated reservoirs. The workflow is used to develop a 3D geologic model for the carbonate reservoirs. Step I involves core description and thin section analysis to divide and characterize the different Smackover facies in the field area into 7 units. The main reservoir facies are the microbial boundstone characterized by vuggy porosity and nearshore/shoal grainstone characterized by moldic porosity. Step II is well log correlation and formation evaluation of 113 wells. We use wireline logs and conventional core data analysis data to calculate average porosity values, permeability and water saturations. Neural networks are utilized at this stage to derive permeability where core measurements are absent or partially present across the reservoirs. Step III is building the 3D structural and stratigraphic framework that is populated with the petrophysical parameters calculated in the previous step. Overall, the integration of reservoir characterization, formation evaluation, and 3D geologic modeling provides a sound framework in the establishment of a field/reservoir-wide development plan for optimal primary and enhanced recovery for these Upper Jurassic microbial carbonate and associated reservoirs. Such a reservoir-wide development plan has broad application to other fields producing from microbial carbonate reservoirs.

Sand ramps are widespread but understudied landforms which have the potential to provide detailed palaeoenvironmental information in dryland regions. This thesis investigates the utility of sand ramps as late Quaternary palaeoenvironmental archives by addressing two research questions: (1) What are the main controls on sand ramp formation in southern Africa? (2) What does the sand ramp record tell us about late Quaternary palaeoenvironments in southern Africa? The distribution of sand ramps in southern Africa was surveyed using Google Earth™ and 75 features were identified in southern Namibia. Ten of these sand ramps, and an additional feature from South Africa, were studied in the field. Sediments and morphology were mapped and a total of 64 OSL dates, 96 sediment samples, 10 heavy mineral assemblages and OSL sensitivity data from 8 samples were examined. The distribution of sand ramps suggests formation is dependent on (1) sediment supply, (2) accommodation space, (3) persistent unidirectional wind and (4) a variable semi-arid to arid climate. Chronologies and sediment analyses indicate individual sand ramp accumulation is locally controlled by sediment supply modulated by the availability, and nature, of the accommodation space. Comparison between the Namibian and South African sand ramps suggests considerable regional variation in the factors controlling sediment supply. The Namibian sand ramps show an affinity to local ephemeral river channels. Periods of dated sand ramp activity in multiple features are interpreted as periods of increased regional fluvial activity. Significant activity occurred at ~21-12 ka (with peaks at 21-18.5 ka and 14.5-12 ka), ~8.5-7.5 ka and ~2 ka. Some activity is also indicated at 85-65 ka and 45-35 ka. These results correspond well to regional records. Overall, this study demonstrates that sand ramps can provide palaeoclimatic information on both the local and regional scale but only if a number of caveats are taken into consideration.

Outcrop analogue studies of fluvial sedimentary systems are often undertaken to identify spatial and temporal characteristics (e.g. stacking patterns, lateral continuity, lithofacies proportions). However, the lateral extent typically exceeds that of the exposure, and/or the true width and thickness are not apparent. Accurate characterisation of fluvial sand bodies is integral for accurate identification and subsequent modelling of aquifer and hydrocarbon reservoir architecture. The studies presented in this thesis utilise techniques that integrate lidar, highresolution photography and differential geospatial measurements, to create accurate three-dimensional (3D) digital outcrop models (DOMs) of continuous 3D and laterally extensive 2D outcrop exposures. The sedimentary architecture of outcrops in the medial portion of a large Distributive Fluvial System (DFS) (Huesca fluvial fan) in the Ebro Basin, north-east Spain, and in the fluvio-deltaic succession of the Breathitt Group in the eastern Appalachian Basin, USA, are evaluated using traditional sedimentological and digital outcrop analytical techniques. The major sand bodies in the study areas are quantitatively analysed to accurately characterise spatial and temporal changes in sand body architecture, from two different outcrop exposure types and scales. Several stochastic reservoir simulations were created to approximate fluvial sand body lithological component and connectivity within the medial portion of the Huesca DFS. Results demonstrate a workflow and current methodology adaptation of digital outcrop techniques required for each study to approximate true geobody widths, thickness and characterise architectural patterns (internal and external) of major fluvial sand bodies interpreted as products of DFSs in the Huesca fluvial fan, and both palaeovalleys and progradational DFSs in the Pikeville and Hyden Formations in the Breathitt Group. The results suggest key geostatistical metrics, which are translatable across any fluvial system that can be used to analyse 3D digital outcrop data, and identify spatial attributes of sand bodies to identify their genetic origin and lithological component within fluvial reservoir systems, and the rock record. 3D quantitative analysis of major sand bodies have allowed more accurate width vs. thickness relationships within the La Serreta area, showing a vertical increase in width and channel-fill facies, and demonstrates a 22% increase of in-channel facies from previous interpretations. Additionally, identification of deposits that are products of a nodal avulsion event have been characterised and are interpreted to be the cause for the increase in width and channel-fill facies. Furthermore, analysis of the Pikeville and Hyden Fms contain sand bodies of stacked distributaries and palaeovalleys, as previously interpreted, and demonstrates that a 3D spatial approach to determine basin-wide architectural trends is integral to identifying the genetic origin, and preservation potential of sand bodies of both palaeovalleys and distributive fluvial systems. The resultant geostatistics assimilated in the thesis demonstrates the efficacy of integrated lidar studies of outcrop analogues, and provide empirical relationships which can be applied to subsurface analogues for reservoir model development and the distribution of both DFS and palaeovalley depositional systems in the rock record.

The thesis focuses on summarizing geologic information concerning Český Brod and its surrounding. The sum of information is intended as a basis for a complex tour of the area for students of ninth grade of elementary schools in and around Český Brod. Students taking the proposed tour should gain complex knowledge of geologic past and present in the given area.

A distribuição espacial e o volume dos domínios litológicos são freqüentemente as maiores fontes de incerteza na modelagem geológica. Geralmente, a interpretação destas características é baseada em critérios subjetivos de observações, sem levar em consideração a incerteza inerente a este processo. Existem métodos de simulação geoestatísticos capazes de quantificar esta incerteza tipológica das unidades geológicas. A maioria desses métodos utiliza como medida de continuidade geológica os modelos de covariância. Entretanto, estas ferramentas de estatística de dois-pontos, raramente, conseguem capturar os padrões de geometrias complexas. Uma alternativa para esta limitação é utilizar métodos de estatística de múltiplos pontos para reproduzir os padrões espaciais de heterogeneidade que são informados por uma imagem de treinamento. Nessa dissertação, será aplicada a geoestatística de múltiplos pontos (SNESIM) para simular os litotipos de um depósito de minério de ferro. A imagem de treinamento foi baseada em seções interpretadas. Os furos de sondagem são utilizados como amostras primárias. As informações geológicas são acessadas por mapas de probabilidade utilizados como informações secundárias. A metodologia é testada na simulação de um depósito de ferro brasileiro com três diferentes litotipos. Os resultados das simulações são comparados contra um modelo de referência e novos furos de sondagens. As geometrias e distribuição espacial das tipologias foram reproduzidas de forma consistente. A incerteza das distribuições e dos volumes dos domínios tipológicos foi quantificada. O algoritmo de múltiplos pontos e a metodologia proposta mostraram grande potencial de aplicação na simulação de depósitos minerais. / The spatial distribution and volumes of lithological domains are often the biggest sources of uncertainty in geological modeling. Usually, the interpretation of these characteristics is based on subjective criteria of observations, without taking into account the uncertainty inherent in this process. There geostatistical simulation methods capable of quantifying this uncertainty typological geological units. Most of these methods uses as a measure of continuity in geological models covariance. However, these two-point statistical is rarely sufficient to capture the patterns of complex geometries. An alternative to this limitation is to use statistical methods of multiple points to reproduce the spatial patterns of heterogeneity that are informed by a training image. In this dissertation, will be applied to multi-point geostatistics (SNESIM) to simulate lithotypes a deposit of iron ore. The training image was based on sections interpreted. The drillholes are used as primary samples. Geologic information is accessed by probability maps used as secondary information. The methodology is tested in the simulation of a deposit of Brazilian iron with three different rock types. The simulation results are compared against a reference model and new drillholes. The geometries and spatial typologies were reproduced consistently. The uncertainty distributions and volumes of typological domains were quantified. The algorithm of multiple points and the proposed methodology showed great potential for application in the simulation of mineral deposits.

Antes de se fazer uma estimativa ou uma simulação geoestatística, os domínios geológicos devem ser modelados de forma que cada domínio utilize apenas dados que pertencem a ele. Na industria mineral a incerteza oriunda dos teores geralmente é levada em consideração, entretanto a incerteza gerada pelo modelo nem sempre é analisada. Sabendo que a maior fonte de incerteza está na transição de uma litologia para outra, essa dissertação visa avaliar a incerteza do modelo geológico através de simulações de funções distâncias assinaladas em zonas de maior incerteza, gerando vários modelos com diferentes proporções de cada litologias. Um estudo de caso em um banco de dados real com alta complexidade geológica é utilizado para avaliar o uso da metodologia. O método se mostrou eficaz para avaliar o impacto da diferença de volume que cada litologia pode alcançar, demonstrando a importância de medir a incerteza na construção de modelos geológicos. / Before making an estimation or a geostatistical simulation, geological domains must be modeled so that each domain uses only data that belongs to it. In the mineral industry the uncertainty derived from the grades is generally taken into account, however the uncertainty generated by the model is not always analyzed. Knowing that the greatest source of uncertainty is in the transition from one lithology to another, this dissertation aims to evaluate the uncertainty of the geological model through signed distances function simulation in uncertainty zones, generating several models with different proportions of each lithology. A case study in a real dataset with high geological complexity is used to evaluate the use of the methodology. The method proved effective in assessing the impact of the volume difference that each lithology can reach, demonstrating the importance of measuring uncertainty in the construction of geological models.

Ao modelar-se depósitos minerais é comum enfrentarmos o problema de estimar múltiplos atributos possivelmente correlacionados, onde algumas variáveis são amostradas menos densamente do que outras. A falta de dados impõe um problema que requer atenção antes de qualquer modelagem subsequente. Precisamos, ao final, de modelos que sejam estatisticamente representativos. A maioria dos conjuntos de dados de problemas práticos são amostrados de maneira heterotópica e, para obter resultados coerentes, é preciso entender os motivos pelos quais alguns dados faltam e quais são os mecanismos que influenciaram a ausência de informações. A teoria de dados faltantes relaciona as amostras ausentes com aquelas medidas através de três mecanismos distintos: Faltante Completamente Aleatório (Missing Completely At Random - MCAR), Faltante Aleatório (Missing At Random - MAR) e Faltante Não Aleatório (Missing Not At Random - MNAR). O último mecanismo é extremamente complexo e a literatura recomenda ser tratado inicialmente como um mecanismo MAR. E após uma transformação fixa deve ser aplicada aos valores complementados para que estes se transformem em valores MNAR Embora existam métodos estatísticos clássicos para lidar com dados faltantes, tais abordagens ignoram a correlação espacial, uma característica que ocorre naturalmente em dados geológicos. A metodologia adequada para tratar com a falta de dados geológicos é a atualização bayesiana, em que se inserem valores sob mecanismo MAR considerando a correlação espacial. No presente estudo, a atualização bayesiana foi combinada com transformações fixas para tratar o mecanismo de falta de dados MNAR em dados geológicos. A transformação fixa aqui empregada é baseada no erro de inserção gerado em um cenário MAR no conjunto de dados. Assim, com o conjunto completo resultante foi utilizado em uma simulação sequencial gaussiana dos teores de uma base de dados multivariada, apresentando resultados satisfatórios, superiores aos obtidos por meio da cossimulação sequencial gaussiana, não inserindo qualquer viés no modelo final. / When modeling mineral deposits, it is common to face the problem of estimating multiple attributes possibly correlated where some variables are more densely sampled then others. Missing data imposes a problem that requires attention prior to any subsequent modeling. The later requires estimation models statistically representative. Most practical data sets are often heterotopically sampled, and to obtain coherent results one must understand the reasons why there are missing data and what are the mechanisms that cause the absence of information. The theory of missing data relates the missing samples to those measured through three different mechanisms: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR). The last mechanism is quite complex to deal with, and the literature recommends being treated as a MAR mechanism and after a fixed transform should be applied to the imputed values so that these turn into MNAR imputed values. Even though there are classical statistical methods to deal with missing data, such approaches ignore spatial correlation, a feature that occurs naturally in geological data. The adequate methodology to deal with missing geologic data is Bayesian Updating, which approaches the MAR mechanism and accounts for spatial correlation. In the present study, bayesian updating was used combined with fixed transforms to treat MNAR missing data mechanism in geologic data. The fixed transform herein used is based on the error of MAR imputation on the data set. The resulting complete set was then used on a sequential gaussian simulation of the grades on a multivariate data set, presenting satisfactory results, superior to those obtained through sequential gaussian cossimulation, not inserting any biases on the final model.

A distribuição espacial e o volume dos domínios litológicos são freqüentemente as maiores fontes de incerteza na modelagem geológica. Geralmente, a interpretação destas características é baseada em critérios subjetivos de observações, sem levar em consideração a incerteza inerente a este processo. Existem métodos de simulação geoestatísticos capazes de quantificar esta incerteza tipológica das unidades geológicas. A maioria desses métodos utiliza como medida de continuidade geológica os modelos de covariância. Entretanto, estas ferramentas de estatística de dois-pontos, raramente, conseguem capturar os padrões de geometrias complexas. Uma alternativa para esta limitação é utilizar métodos de estatística de múltiplos pontos para reproduzir os padrões espaciais de heterogeneidade que são informados por uma imagem de treinamento. Nessa dissertação, será aplicada a geoestatística de múltiplos pontos (SNESIM) para simular os litotipos de um depósito de minério de ferro. A imagem de treinamento foi baseada em seções interpretadas. Os furos de sondagem são utilizados como amostras primárias. As informações geológicas são acessadas por mapas de probabilidade utilizados como informações secundárias. A metodologia é testada na simulação de um depósito de ferro brasileiro com três diferentes litotipos. Os resultados das simulações são comparados contra um modelo de referência e novos furos de sondagens. As geometrias e distribuição espacial das tipologias foram reproduzidas de forma consistente. A incerteza das distribuições e dos volumes dos domínios tipológicos foi quantificada. O algoritmo de múltiplos pontos e a metodologia proposta mostraram grande potencial de aplicação na simulação de depósitos minerais. / The spatial distribution and volumes of lithological domains are often the biggest sources of uncertainty in geological modeling. Usually, the interpretation of these characteristics is based on subjective criteria of observations, without taking into account the uncertainty inherent in this process. There geostatistical simulation methods capable of quantifying this uncertainty typological geological units. Most of these methods uses as a measure of continuity in geological models covariance. However, these two-point statistical is rarely sufficient to capture the patterns of complex geometries. An alternative to this limitation is to use statistical methods of multiple points to reproduce the spatial patterns of heterogeneity that are informed by a training image. In this dissertation, will be applied to multi-point geostatistics (SNESIM) to simulate lithotypes a deposit of iron ore. The training image was based on sections interpreted. The drillholes are used as primary samples. Geologic information is accessed by probability maps used as secondary information. The methodology is tested in the simulation of a deposit of Brazilian iron with three different rock types. The simulation results are compared against a reference model and new drillholes. The geometries and spatial typologies were reproduced consistently. The uncertainty distributions and volumes of typological domains were quantified. The algorithm of multiple points and the proposed methodology showed great potential for application in the simulation of mineral deposits.

Antes de se fazer uma estimativa ou uma simulação geoestatística, os domínios geológicos devem ser modelados de forma que cada domínio utilize apenas dados que pertencem a ele. Na industria mineral a incerteza oriunda dos teores geralmente é levada em consideração, entretanto a incerteza gerada pelo modelo nem sempre é analisada. Sabendo que a maior fonte de incerteza está na transição de uma litologia para outra, essa dissertação visa avaliar a incerteza do modelo geológico através de simulações de funções distâncias assinaladas em zonas de maior incerteza, gerando vários modelos com diferentes proporções de cada litologias. Um estudo de caso em um banco de dados real com alta complexidade geológica é utilizado para avaliar o uso da metodologia. O método se mostrou eficaz para avaliar o impacto da diferença de volume que cada litologia pode alcançar, demonstrando a importância de medir a incerteza na construção de modelos geológicos. / Before making an estimation or a geostatistical simulation, geological domains must be modeled so that each domain uses only data that belongs to it. In the mineral industry the uncertainty derived from the grades is generally taken into account, however the uncertainty generated by the model is not always analyzed. Knowing that the greatest source of uncertainty is in the transition from one lithology to another, this dissertation aims to evaluate the uncertainty of the geological model through signed distances function simulation in uncertainty zones, generating several models with different proportions of each lithology. A case study in a real dataset with high geological complexity is used to evaluate the use of the methodology. The method proved effective in assessing the impact of the volume difference that each lithology can reach, demonstrating the importance of measuring uncertainty in the construction of geological models.