Browsing by Subject "regression"
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Item Methodology for Predicting Drilling Performance from Environmental Conditions(2012-02-14) De Almeida, Jose AlejandroThe use of statistics has been common practice within the petroleum industry for over a decade. With such a mature subject that includes specialized software and numerous articles, the challenge of this project was to introduce a duplicable method to perform deterministic regression while confirming the mathematical and actual validation of the resulting model. A five-step procedure was introduced using Statistical Analysis Software (SAS) for necessary computations to obtain a model that describes an event by analyzing the environmental variables. Since SAS may not be readily available, the code to perform the five-step methodology in R has been provided. The deterministic five-step procedure methodology may be applied to new fields with a limited amount of data. As an example case, 17 wells drilled in north central Texas were used to illustrate how to apply the methodology to obtain a deterministic model. The objective was to predict the number of days required to drill a well using environmental conditions and technical variables. Ideally, the predicted number of days would be within +/- 10% of the observed time of the drilled wells. The database created contained 58 observations from 17 wells with the descriptive variables, technical limit (referred to as estimated days), depth, bottomhole temperature (BHT), inclination (inc), mud weight (MW), fracture pressure (FP), pore pressure (PP), and the average, maximum, and minimum difference between fracture pressure minus mud weight and mud weight minus pore pressure. Step 1 created a database. Step 2 performed initial statistical regression on the original dataset. Step 3 ensured that the models were valid by performing univariate analysis. Step 4 history matched the models-response to actual observed data. Step 5 repeated the procedure until the best model had been found. Four main regression techniques were used: stepwise regression, forward selection, backward elimination, and least squares regression. Using these four regression techniques and best engineering judgment, a model was found that improved time prediction accuracy, but did not constantly result in values that were +/- 10% of the observed times. The five-step methodology to determine a model using deterministic statistics has applications in many different areas within the petroleum field. Unlike examples found in literature, emphasis has been given to the validation of the model by analysis of the model error. By focusing on the five-step procedure, the methodology may be applied within different software programs, allowing for greater usage. These two key parameters allow companies to obtain their time prediction models without the need to outsource the work and test the certainty of any chosen model.Item New Approaches in Testing Common Assumptions for Regressions with Missing Data(2014-07-30) Chown, Justin AndrewWe consider both nonparametric regression and heteroskedastic nonparametric regression models with multivariate covariates and with responses missing at random. The regression function is estimated using a local polynomial smoother, and, when necessary, the scale function is estimated using a combination of local polynomial smoothers. It is shown, for both regression models, that suitable residual-based empirical distribution functions using only the complete cases, i.e. residuals that can actually be constructed from the data, are efficient in the sense of H?jek and Le Cam. In our proofs we derive, more generally, the efficient influence function for estimating an arbitrary linear functional of the error distribution; this covers the distribution function as a special case. Our estimators are shown to admit functional central limit theorems. We do this by applying the transfer principle for complete case statistics, which makes it possible to adapt known results for fully observed data to the case of missing data. Then, we use these residual-based empirical distribution functions to test for normal errors using a martingale transform approach. Small simulation studies are conducted to investigate the performance of these tests. Our results, for the homoskedastic model, show the proposed approach to be comparable to one based on imputation, and, for the heteroskedastic model, the results are sensitive to the estimate of the scale function. Finally, we construct a test for heteroskedasticity using residuals from a nonparametric regression. The approach uses a weighted empirical process and only the completely observed data, and is shown to perform well in certain scenarios. All of the tests considered here are asymptotically distribution free, which means inference based on them does not depend on unknown parameters.Item Statewise Correlates of Civil Nuclear Energy(2014-08-01) Kafle, NischalQuantitative empirical analysis has been used in several works, over the past decade or so, to identify correlates of states motivation for pursuing military nuclear technology. Nelson and Sprecher used such methodology to identify various national attributes that correlate to states peaceful use of nuclear power for electricity generation, which was termed as \Nuclear Reliance." The major initial objective for the present work was to replace a dichotomous subjective independent variable used by Nelson and Sprecher to represent engagement in international commerce in civil nuclear technology with more objectively defined variables carrying a similar representation. Ordinary least squares stepwise regression was applied to a dataset consisting of 27 independent variables that was created for this study. Data for 13 of 27 independent variables were added to the dataset from previous study, and 9 of 14 previous attributes data were updated. Supervised stepwise regression was used to create a linear regression model with five predictors having acceptable confidence level (p < 0:01) and coefficient of determination (R^(2) ? 0:51). Results from stepwise linear regression showed that states that trade knowledge and material for nuclear power technology are heavily involved in civil nuclear power that states that are not involved in international trade of such technology and material. Analyses of the individual steps at several different levels of aggregation showed that some predictors were included as a consequence of improvements to residuals only for a few states. Preliminary results show that an analysis based on change from some prior year (1980 was used, for illustrative purposes) has considerable promise.