- / -

No description available.

Finding and developing oil and gas resources requires accurate geological information with which to formulate strategies for exploration and exploitation ventures. When data are scarce, statistical procedures are sometimes substituted to compensate for the lack of information about reservoir properties. The most modern methods incorporate geostatistics. Even the best geostatistical methods yield results with varying degrees of uncertainty in their solutions. Geological information is, by its nature, spatially limited and the geoscientist is handicapped in determining appropriate values for various geological parameters that affect the final reservoir model (Massonnat, 1999). This study focuses on reservoir models that depend on geostatistical methods. This is accomplished by quantifying the uncertainty in outcome of reservoir simulations as six different geological variables are changed during a succession of reservoir simulations. In this study, variations in total fluid produced are examined by numerical modeling. Causes of uncertainty in outcomes of the model runs are examined by changing one of six geological parameters for each run. The six geological parameters tested for their impact on reservoir performances include the following: 1) variogram range used to krig thickness layers, 2) morphology around well 14, 3) shelf edge orientation, 4) bathymetry ranges attributed for each facies, 5) variogram range used to simulate facies distribution, 6) extension of the erosion at top of the reservoir. The parameters were assigned values that varied from a minimum to a maximum quantity, determined from petrophysical and core analysis. After simulation runs had been completed, a realistic, 3-dimensional reservoir model was developed that revealed a range of reservoir production data. The parameters that had the most impact on reservoir performance were: 1) the amount of rock eroded at the top of the reservoir zone and 2) the bathymetry assigned to the reservoir facies. This study demonstrates how interaction between geological parameters influence reservoir fluid production, how variations in those parameters influence uncertainties in reservoir simulations, and it highlights the interdependencies between geological variables. The analysis of variance method used to quantify uncertainty in this study was found to be rapid, accurate, and highly satisfactory for this type of study. It is recommended for future applications in the petroleum industry.

Finding and developing oil and gas resources requires accurate geological information with which to formulate strategies for exploration and exploitation ventures. When data are scarce, statistical procedures are sometimes substituted to compensate for the lack of information about reservoir properties. The most modern methods incorporate geostatistics. Even the best geostatistical methods yield results with varying degrees of uncertainty in their solutions. Geological information is, by its nature, spatially limited and the geoscientist is handicapped in determining appropriate values for various geological parameters that affect the final reservoir model (Massonnat, 1999). This study focuses on reservoir models that depend on geostatistical methods. This is accomplished by quantifying the uncertainty in outcome of reservoir simulations as six different geological variables are changed during a succession of reservoir simulations. In this study, variations in total fluid produced are examined by numerical modeling. Causes of uncertainty in outcomes of the model runs are examined by changing one of six geological parameters for each run. The six geological parameters tested for their impact on reservoir performances include the following: 1) variogram range used to krig thickness layers, 2) morphology around well 14, 3) shelf edge orientation, 4) bathymetry ranges attributed for each facies, 5) variogram range used to simulate facies distribution, 6) extension of the erosion at top of the reservoir. The parameters were assigned values that varied from a minimum to a maximum quantity, determined from petrophysical and core analysis. After simulation runs had been completed, a realistic, 3-dimensional reservoir model was developed that revealed a range of reservoir production data. The parameters that had the most impact on reservoir performance were: 1) the amount of rock eroded at the top of the reservoir zone and 2) the bathymetry assigned to the reservoir facies. This study demonstrates how interaction between geological parameters influence reservoir fluid production, how variations in those parameters influence uncertainties in reservoir simulations, and it highlights the interdependencies between geological variables. The analysis of variance method used to quantify uncertainty in this study was found to be rapid, accurate, and highly satisfactory for this type of study. It is recommended for future applications in the petroleum industry.

No description available.

No description available.

Parametric methods are effective and appropriate when data sets are obtained by well-defined random sampling procedures, the population distribution for responses is well-defined, the null sampling distributions of suitable test statistics do not depend on any unknown entity and well-defined likelihood models are provided for by nuisance parameters. Permutation testing methods, on the other hand, are appropriate and unavoidable when distribution models for responses are not well specified, nonparametric or depend on too many nuisance parameters; when ancillary statistics in well-specified distributional models have a strong influence on inferential results or are confounded with other nuisance entities; when the sample sizes are less than the number of parameters and when data sets are obtained by ill-specified selection-bias procedures. In addition, permutation tests are useful not only when parametric tests are not possible, but also when more importance needs to be given to the observed data set, than to the population model, as is typical for example in biostatistics. The different types of permutation methods for analysis of variance, multiple linear regression and principal component analysis are explored. More specifically, one-way, twoway and three-way ANOVA permutation strategies will be discussed. Approximate and exact permutation tests for the significance of one or more regression coefficients in a multiple linear regression model will be explained next, and lastly, the use of permutation tests used as a means to validate and confirm the results obtained from the exploratory PCA will be described. / Dissertation (MSc)--University of Pretoria, 2012. / Statistics / unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / Better comprehension of the Urban Heat Island study requires information on the natural as well as built characteristics of the environment at high spatial resolution. Sky View Factor (SVF) has been distinguished as a significant parameter for Local Climate Zone (LCZ) classification based on environmental characteristics that influence the urban climate at finer spatial scales. The purpose of this thesis was to evaluate currently available data sources and methods for deriving continuous SVF estimates. The specific objectives were to summarize the characteristics of currently available digital surface models (DSMs) of the study region and to compare the results of using these models to estimate SVF with three different raster-based algorithms: Horizon Search Algorithm in R-programming (Doninck, 2018), Relief Visualization Toolbox (RVT) (Žiga et al., 2016), and the Urban Multi-scale Environmental Predictor (UMEP) plugin in QGIS (Lindberg, et al., 2018).

Analysis of Variance (ANOVA) is the easiest and most widely used model nowadays in statistics. ANOVA however requires a set of assumptions for the model to be a valid choice and for the inferences to be accurate. Among many, ANOVA assumes the data in question is normally distributed and homogenous. However, data from most disciplines does not meet the assumption of normality and/or equal variance. Regrettably, researchers do not always check whether the assumptions are met, and if these assumptions are violated, inferences might well be wrong. We conducted a simulation study to compare the performance of standard ANOVA to Poisson and Negative Binomial models when applied to counts data. We considered different combination of sample sizes and underlying distributions. In this simulation study, we first assed Type I error for each model involved. We then compared power as well as the quality of the estimated parameters across the models.

Le contexte de ce projet de recherche est celui de la stabilité des barrages-poids et aborde le besoin d’évaluation de la résistance de l’interface béton-rocher. Puisqu’il est techniquement difficile d’évaluer si l’interface est liée ou non, la cohésion réelle et sa contribution à la résistance au cisaillement sont souvent négligées et ce sujet précis est peu abordé dans la littérature. Un lien direct peut être fait entre cette non-considération et des travaux de stabilisation réalisés sur des ouvrages hydrauliques. Cette étude a comme objectif la caractérisation de la cohésion réelle dans le but de déterminer s’il est sécuritaire d’incorporer sa contribution dans l’évaluation de stabilité des barrages-poids. Pour ce faire, il est nécessaire d’évaluer les comportements en traction et en cisaillement de l’interface et d’analyser comment ils sont affectés par des paramètres importants telle la rugosité de l’interface. Cette caractérisation est faite à l’aide d’un programme expérimental sur 66 répliques d’interfaces béton-rocher en mortier. La rugosité est évaluée à l’aide d’un profilomètre laser et du paramètre Z2. Les répliques ont fait l’objet d’essais de traction directe, de traction par pression de fluide et de cisaillement direct. L’influence de la rugosité d’interface et de la résistance à la compression uniaxiale (UCS) des matériaux sur les résistances à la traction et au cisaillement est évaluée grâce à l’analyse des variances (ANOVA). Des essais supplémentaires ont permis d’approfondir la compréhension du mécanisme de rupture en cisaillement. Les résultats indiquent une résistance à la traction moyenne de l’interface liée de 0,62 MPa et une cohésion (en cisaillement) moyenne de 3,1 MPa. L’ANOVA montre une augmentation significative de la résistance à la traction avec la rugosité et une augmentation significative de la résistance au cisaillement au pic avec la rugosité, l’UCS et la contrainte normale. Il a aussi été observé que le pas d’échantillonnage a un impact important sur la valeur de Z2. Les résultats suggèrent qu’une valeur minimale de cohésion de 100 à 200 kPa pourrait être utilisée dans la mesure où il peut être démontré que l’interface est liée. Cette condition pourrait d’ailleurs constituer un sujet de recherche s’inscrivant dans la continuité des travaux réalisés.