| dc.description.abstract | The 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 |
