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A novel classification method applied to well log data calibrated by ontology based core descriptionsGraciolli, Vinicius Medeiros January 2018 (has links)
Um método para a detecção automática de tipos litológicos e contato entre camadas foi desenvolvido através de uma combinação de análise estatística de um conjunto de perfis geofísicos de poços convencionais, calibrado por descrições sistemáticas de testemunhos. O objetivo deste projeto é permitir a integração de dados de rocha em modelos de reservatório. Os testemunhos são descritos com o suporte de um sistema de nomenclatura baseado em ontologias que formaliza extensamente uma grande gama de atributos de rocha. As descrições são armazenadas em um banco de dados relacional junto com dados de perfis de poço convencionais de cada poço analisado. Esta estrutura permite definir protótipos de valores de perfil combinados para cada litologia reconhecida através do cálculo de média e dos valores de variância e covariância dos valores medidos por cada ferramenta de perfilagem para cada litologia descrita nos testemunhos. O algoritmo estatístico é capaz de aprender com cada novo testemunho e valor de log adicionado ao banco de dados, refinando progressivamente a identificação litológica. A detecção de contatos litológicos é realizada através da suavização de cada um dos perfis através da aplicação de duas médias móveis de diferentes tamanhos em cada um dos perfis. Os resultados de cada par de perfis suavizados são comparados, e as posições onde as linhas se cruzam definem profundidades onde ocorrem mudanças bruscas no valor do perfil, indicando uma potencial mudança de litologia. Os resultados da aplicação desse método em cada um dos perfis são então unificados em uma única avaliação de limites litológicos Os valores de média e variância-covariância derivados da correlação entre testemunhos e perfis são então utilizados na construção de uma distribuição gaussiana n-dimensional para cada uma das litologias reconhecidas. Neste ponto, probabilidades a priori também são calculadas para cada litologia. Estas distribuições são comparadas contra cada um dos intervalos litológicos previamente detectados por meio de uma função densidade de probabilidade, avaliando o quão perto o intervalo está de cada litologia e permitindo a atribuição de um tipo litológico para cada intervalo. O método desenvolvido foi testado em um grupo de poços da bacia de Sergipe- Alagoas, e a precisão da predição atingida durante os testes mostra-se superior a algoritmos clássicos de reconhecimento de padrões como redes neurais e classificadores KNN. O método desenvolvido foi então combinado com estes métodos clássicos em um sistema multi-agentes. Os resultados mostram um potencial significante para aplicação operacional efetiva na construção de modelos geológicos para a exploração e desenvolvimento de áreas com grande volume de dados de perfil e intervalos testemunhados. / A method for the automatic detection of lithological types and layer contacts was developed through the combined statistical analysis of a suite of conventional wireline logs, calibrated by the systematic description of cores. The intent of this project is to allow the integration of rock data into reservoir models. The cores are described with support of an ontology-based nomenclature system that extensively formalizes a large set of attributes of the rocks, including lithology, texture, primary and diagenetic composition and depositional, diagenetic and deformational structures. The descriptions are stored in a relational database along with the records of conventional wireline logs (gamma ray, resistivity, density, neutrons, sonic) of each analyzed well. This structure allows defining prototypes of combined log values for each lithology recognized, by calculating the mean and the variance-covariance values measured by each log tool for each of the lithologies described in the cores. The statistical algorithm is able to learn with each addition of described and logged core interval, in order to progressively refine the automatic lithological identification. The detection of lithological contacts is performed through the smoothing of each of the logs by the application of two moving means with different window sizes. The results of each pair of smoothed logs are compared, and the places where the lines cross define the locations where there are abrupt shifts in the values of each log, therefore potentially indicating a change of lithology. The results from applying this method to each log are then unified in a single assessment of lithological boundaries The mean and variance-covariance data derived from the core samples is then used to build an n-dimensional gaussian distribution for each of the lithologies recognized. At this point, Bayesian priors are also calculated for each lithology. These distributions are checked against each of the previously detected lithological intervals by means of a probability density function, evaluating how close the interval is to each lithology prototype and allowing the assignment of a lithological type to each interval. The developed method was tested in a set of wells in the Sergipe-Alagoas basin and the prediction accuracy achieved during testing is superior to classic pattern recognition methods such as neural networks and KNN classifiers. The method was then combined with neural networks and KNN classifiers into a multi-agent system. The results show significant potential for effective operational application to the construction of geological models for the exploration and development of areas with large volume of conventional wireline log data and representative cored intervals.
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A novel classification method applied to well log data calibrated by ontology based core descriptionsGraciolli, Vinicius Medeiros January 2018 (has links)
Um método para a detecção automática de tipos litológicos e contato entre camadas foi desenvolvido através de uma combinação de análise estatística de um conjunto de perfis geofísicos de poços convencionais, calibrado por descrições sistemáticas de testemunhos. O objetivo deste projeto é permitir a integração de dados de rocha em modelos de reservatório. Os testemunhos são descritos com o suporte de um sistema de nomenclatura baseado em ontologias que formaliza extensamente uma grande gama de atributos de rocha. As descrições são armazenadas em um banco de dados relacional junto com dados de perfis de poço convencionais de cada poço analisado. Esta estrutura permite definir protótipos de valores de perfil combinados para cada litologia reconhecida através do cálculo de média e dos valores de variância e covariância dos valores medidos por cada ferramenta de perfilagem para cada litologia descrita nos testemunhos. O algoritmo estatístico é capaz de aprender com cada novo testemunho e valor de log adicionado ao banco de dados, refinando progressivamente a identificação litológica. A detecção de contatos litológicos é realizada através da suavização de cada um dos perfis através da aplicação de duas médias móveis de diferentes tamanhos em cada um dos perfis. Os resultados de cada par de perfis suavizados são comparados, e as posições onde as linhas se cruzam definem profundidades onde ocorrem mudanças bruscas no valor do perfil, indicando uma potencial mudança de litologia. Os resultados da aplicação desse método em cada um dos perfis são então unificados em uma única avaliação de limites litológicos Os valores de média e variância-covariância derivados da correlação entre testemunhos e perfis são então utilizados na construção de uma distribuição gaussiana n-dimensional para cada uma das litologias reconhecidas. Neste ponto, probabilidades a priori também são calculadas para cada litologia. Estas distribuições são comparadas contra cada um dos intervalos litológicos previamente detectados por meio de uma função densidade de probabilidade, avaliando o quão perto o intervalo está de cada litologia e permitindo a atribuição de um tipo litológico para cada intervalo. O método desenvolvido foi testado em um grupo de poços da bacia de Sergipe- Alagoas, e a precisão da predição atingida durante os testes mostra-se superior a algoritmos clássicos de reconhecimento de padrões como redes neurais e classificadores KNN. O método desenvolvido foi então combinado com estes métodos clássicos em um sistema multi-agentes. Os resultados mostram um potencial significante para aplicação operacional efetiva na construção de modelos geológicos para a exploração e desenvolvimento de áreas com grande volume de dados de perfil e intervalos testemunhados. / A method for the automatic detection of lithological types and layer contacts was developed through the combined statistical analysis of a suite of conventional wireline logs, calibrated by the systematic description of cores. The intent of this project is to allow the integration of rock data into reservoir models. The cores are described with support of an ontology-based nomenclature system that extensively formalizes a large set of attributes of the rocks, including lithology, texture, primary and diagenetic composition and depositional, diagenetic and deformational structures. The descriptions are stored in a relational database along with the records of conventional wireline logs (gamma ray, resistivity, density, neutrons, sonic) of each analyzed well. This structure allows defining prototypes of combined log values for each lithology recognized, by calculating the mean and the variance-covariance values measured by each log tool for each of the lithologies described in the cores. The statistical algorithm is able to learn with each addition of described and logged core interval, in order to progressively refine the automatic lithological identification. The detection of lithological contacts is performed through the smoothing of each of the logs by the application of two moving means with different window sizes. The results of each pair of smoothed logs are compared, and the places where the lines cross define the locations where there are abrupt shifts in the values of each log, therefore potentially indicating a change of lithology. The results from applying this method to each log are then unified in a single assessment of lithological boundaries The mean and variance-covariance data derived from the core samples is then used to build an n-dimensional gaussian distribution for each of the lithologies recognized. At this point, Bayesian priors are also calculated for each lithology. These distributions are checked against each of the previously detected lithological intervals by means of a probability density function, evaluating how close the interval is to each lithology prototype and allowing the assignment of a lithological type to each interval. The developed method was tested in a set of wells in the Sergipe-Alagoas basin and the prediction accuracy achieved during testing is superior to classic pattern recognition methods such as neural networks and KNN classifiers. The method was then combined with neural networks and KNN classifiers into a multi-agent system. The results show significant potential for effective operational application to the construction of geological models for the exploration and development of areas with large volume of conventional wireline log data and representative cored intervals.
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A novel classification method applied to well log data calibrated by ontology based core descriptionsGraciolli, Vinicius Medeiros January 2018 (has links)
Um método para a detecção automática de tipos litológicos e contato entre camadas foi desenvolvido através de uma combinação de análise estatística de um conjunto de perfis geofísicos de poços convencionais, calibrado por descrições sistemáticas de testemunhos. O objetivo deste projeto é permitir a integração de dados de rocha em modelos de reservatório. Os testemunhos são descritos com o suporte de um sistema de nomenclatura baseado em ontologias que formaliza extensamente uma grande gama de atributos de rocha. As descrições são armazenadas em um banco de dados relacional junto com dados de perfis de poço convencionais de cada poço analisado. Esta estrutura permite definir protótipos de valores de perfil combinados para cada litologia reconhecida através do cálculo de média e dos valores de variância e covariância dos valores medidos por cada ferramenta de perfilagem para cada litologia descrita nos testemunhos. O algoritmo estatístico é capaz de aprender com cada novo testemunho e valor de log adicionado ao banco de dados, refinando progressivamente a identificação litológica. A detecção de contatos litológicos é realizada através da suavização de cada um dos perfis através da aplicação de duas médias móveis de diferentes tamanhos em cada um dos perfis. Os resultados de cada par de perfis suavizados são comparados, e as posições onde as linhas se cruzam definem profundidades onde ocorrem mudanças bruscas no valor do perfil, indicando uma potencial mudança de litologia. Os resultados da aplicação desse método em cada um dos perfis são então unificados em uma única avaliação de limites litológicos Os valores de média e variância-covariância derivados da correlação entre testemunhos e perfis são então utilizados na construção de uma distribuição gaussiana n-dimensional para cada uma das litologias reconhecidas. Neste ponto, probabilidades a priori também são calculadas para cada litologia. Estas distribuições são comparadas contra cada um dos intervalos litológicos previamente detectados por meio de uma função densidade de probabilidade, avaliando o quão perto o intervalo está de cada litologia e permitindo a atribuição de um tipo litológico para cada intervalo. O método desenvolvido foi testado em um grupo de poços da bacia de Sergipe- Alagoas, e a precisão da predição atingida durante os testes mostra-se superior a algoritmos clássicos de reconhecimento de padrões como redes neurais e classificadores KNN. O método desenvolvido foi então combinado com estes métodos clássicos em um sistema multi-agentes. Os resultados mostram um potencial significante para aplicação operacional efetiva na construção de modelos geológicos para a exploração e desenvolvimento de áreas com grande volume de dados de perfil e intervalos testemunhados. / A method for the automatic detection of lithological types and layer contacts was developed through the combined statistical analysis of a suite of conventional wireline logs, calibrated by the systematic description of cores. The intent of this project is to allow the integration of rock data into reservoir models. The cores are described with support of an ontology-based nomenclature system that extensively formalizes a large set of attributes of the rocks, including lithology, texture, primary and diagenetic composition and depositional, diagenetic and deformational structures. The descriptions are stored in a relational database along with the records of conventional wireline logs (gamma ray, resistivity, density, neutrons, sonic) of each analyzed well. This structure allows defining prototypes of combined log values for each lithology recognized, by calculating the mean and the variance-covariance values measured by each log tool for each of the lithologies described in the cores. The statistical algorithm is able to learn with each addition of described and logged core interval, in order to progressively refine the automatic lithological identification. The detection of lithological contacts is performed through the smoothing of each of the logs by the application of two moving means with different window sizes. The results of each pair of smoothed logs are compared, and the places where the lines cross define the locations where there are abrupt shifts in the values of each log, therefore potentially indicating a change of lithology. The results from applying this method to each log are then unified in a single assessment of lithological boundaries The mean and variance-covariance data derived from the core samples is then used to build an n-dimensional gaussian distribution for each of the lithologies recognized. At this point, Bayesian priors are also calculated for each lithology. These distributions are checked against each of the previously detected lithological intervals by means of a probability density function, evaluating how close the interval is to each lithology prototype and allowing the assignment of a lithological type to each interval. The developed method was tested in a set of wells in the Sergipe-Alagoas basin and the prediction accuracy achieved during testing is superior to classic pattern recognition methods such as neural networks and KNN classifiers. The method was then combined with neural networks and KNN classifiers into a multi-agent system. The results show significant potential for effective operational application to the construction of geological models for the exploration and development of areas with large volume of conventional wireline log data and representative cored intervals.
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Building a Predictive Model for Stratigraphic Transitions and Lateral Facies Changes in the Cretaceous Almond Formation, WyomingPhillips, Joseph E. 07 December 2020 (has links)
The Cretaceous Almond Formation, located in the Greater Green River Basin, records deposition of coastal plain fluvial sandstones and shallow marginal-marine sandstones in a net-transgressive sequence along the western margin of the Cretaceous Interior Seaway (CIS) from the late Campanian to early Maastrichtian. The Almond Formation is an important hydrocarbon reservoir, with development mainly along the Wamsutter Arch and the northeast margins of the Washakie Basin. Previous studies have primarily focused on outcrops along the eastern flank of the Rock Springs Uplift and subsurface data targeting the Wamsutter Arch. Further development of the Almond petroleum system requires extending our understanding of lateral facies changes and sequence stratigraphic architecture away from areas that have been previously studied. The aim of this research is to build a predictive model of lateral and temporal facies transitions and associated reservoir character along the Cherokee Arch in southern Wyoming. This structural feature marks the southern margin of the Washakie Basin and is roughly perpendicular to the shoreline of the CIS. Outcrop examination at either end of the arch shows that lower Almond strata along the western margin of the Washakie Basin transition from coastal plain facies associations to time-equivalent shallow-marine strata to the east, while the upper Almond strata transition from shallow-marine sands to offshore and prodeltaic muds across the ~125 km separating the two outcrop localities. This reveals clear facies associations shifts at the basin scale, which are difficult to interpret using only well data. The preservation of shoreface strata and related near-shore, fluvio-deltaics across large distances in the dip direction shows the large magnitude of shoreline migration. This also suggests that the system gradient was likely very gentle, leading to wide facies belts, and that reservoir continuity could be complex over significant distances. Stacking patterns observed in outcrop, core, and log curves demonstrate an early progradational sequence across the basin from the west to east. This time equivalent strata suggests sediment supply outpaced accommodation during deposition of the lower Almond and equivalent basinward strata, leading to progradation and eventually to some aggradation before relative sea-level rose. This is significant as the Almond is thought primarily as an overall retrogradational system. Within the upper Almond and basinward equivalent strata, stacking patterns reveal a well preserved retrogradational sequence as accommodation outpaced sediment supply during the final transgression of the Mesaverde Group. Core and outcrop analysis to the east at this time show facies associations that potentially represent an inundated, estuarine deltaic environment of deposition transitioning to deltaic depofacies to the west. Clinoformal geometry and an additional sand found in the subsurface of a cluster of only southern wells corroborate a deltaic interpretation. This sand is interpreted as a lobate deposit flanked by shale to the north. Shorelines span a short distance in the east and a much broader distance to the west with a clear facies shift in between allowing for marine shale to directly overlay coastal plain facies. Outcrop, core, and subsurface datasets have led to a better understanding of sediment partitioning and preservation during this transgressive phase of the CIS in the western United States. A better understanding of these spatial and temporal patterns will help to remove risk associated with exploration along this trend, as well as serve as an analogue for other transgressive deposits. Additional data would increase knowledge of this system and lead to solidification of new ideas presented for the Almond Formation along the Cherokee Arch.
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Hydrological and Paleoclimate Analysis of a Pinyon-Juniper and Fen-Dominated Watershed on the Windy Ridge Mega-LandslideBarker, Joel Frederick 01 November 2019 (has links)
Water BudgetThis chapter documents the hydrologic analysis of a watershed within the Windy Ridge mega-landslide of Central Utah to (1) create a water budget and (2) place a quantitative limit on the magnitude of climatic changes documented by Shurtliff et al. (2017) and Hudson et al. (2019). (1) A water budget was calculated over the last four years using instrumentation and weather stations both within and surrounding the watershed, In terms of precipitation input, 85% is released by the evapotranspiration of the Pinyon-Juniper forest, 4% discharges as surface water from the base of the watershed, and 11 % infiltrates the groundwater system. This infiltration rate is slightly lower than the 15% suggested by Maxey-Eakin method (Maxey and Eakin, 1949), likely due to the less permeable, clay-rich sediment. (2) Previous studies performed on Garden Basin Cattail (GBC) Fen at the base of its watershed suggest swings from pond-like to wetland environments (Shurtliff et al, 2017; Hudson et al, 2019). This study estimated precipitation values necessary to create standing water (pond) environments. Changes in annual precipitation, as well as input from North American monsoon (NAM), may cause these environmental changes. Each of these cases were examined. Trends in piezometer measurements compared to mean annual precipitation indicated that ‰¥ 644 mm of annual precipitation are required to sustain a wet (perennial standing water) environment. The change from wetland to pond conditions may depend on seasonal trends in precipitation. This study suggests an increase of 150-300 mm of precipitation in late summer (NAM) may be connected to perennially wet conditions. The higher annual precipitation values, largely accomplished by NAM fluctuations, caused a transition from wetland to pond (Hudson et al., 2019; Shurtliff et al., 2017). Chapter 2: Core AnalysisChapter 2 further documents the watershed's historical environmental and climate record by analyzing sediment and topography surrounding GBC fen, adding to the works of Shurtliff et al. (2019) and Hudson et al. (2019). A core was extracted from GBC fen at the base of the watershed and the sediment analyzed in terms of color, texture, environmental scanning electron microscope (ESEM) imaging, RockEval pyrolysis, and 14C ages. These results were then compared to pre-existing pollen and diatom proxies completed on a previous core by Shurtliff et al. (2019). This study suggests climatic variation, along with basin fill processes, was the driver of environmental change in GBC fen (Garden Basin watershed). Climate proxies show the basic trend from a particularly wet period (12-9 ka BP) of more stagnant or deeper water, to a much dryer period of much shallower water levels (9-3 ka BP), followed by a rebound in moisture levels, especially in the past few hundred years. Although climate was the driver of transitions within GBC2 core, a pollen record of sustained shallow water plants and MASW (Park et al., 1999) survey may suggest beaver activity.
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