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dc.contributor.advisorBeck, Geneen_US
dc.creatorDe Almeida, Jose Alejandroen_US
dc.date.accessioned2012-02-14T22:18:33Zen_US
dc.date.accessioned2012-02-16T16:20:36Z
dc.date.available2012-02-14T22:18:33Zen_US
dc.date.available2012-02-16T16:20:36Z
dc.date.created2010-12en_US
dc.date.issued2012-02-14en_US
dc.date.submittedDecember 2010en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8808en_US
dc.description.abstractThe 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectPredictingen_US
dc.subjectpredicten_US
dc.subjectestimatingen_US
dc.subjectestimateen_US
dc.subjectdrillingen_US
dc.subjectdaysen_US
dc.subjectregressionen_US
dc.subjectdeterministicen_US
dc.subjectprobablisticen_US
dc.subjectlinearen_US
dc.titleMethodology for Predicting Drilling Performance from Environmental Conditionsen_US
dc.typeThesisen
thesis.degree.departmentPetroleum Engineeringen_US
thesis.degree.disciplinePetroleum Engineeringen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelMastersen_US
dc.contributor.committeeMemberSchubert, Jeromeen_US
dc.contributor.committeeMemberSherman, Michaelen_US
dc.type.genrethesisen_US
dc.type.materialtexten_US


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