Evaluation of the Effect of Well Parameters on Oil Production
A. Giussani
Texas Tech University
E. A. Nasir
Baker Hughes a GE Company
M. C. Watson
Texas Tech University
P. H. Westfall
Texas Tech University
M. E. Wigwe
Texas Tech University
Operators in the oil and gas industry are faced with different economic decisions relating to unconventional oil wells. With the popularity of data science and big data analytics tools, a petroleum engineer applies statistical techniques to analyze oil and gas data. We use regression analysis and decision tree in R to evaluate the effect of various well parameters on oil production. Our dataset has over 5700 horizontal oil wells located in the six most productive counties in North Dakota. Two formations present are Bakken and Three Forks. Initial EDA shows that, on average, operators are applying the same drilling and completion techniques across both formations as indicated in a comparative boxplot and two-sample t-test. Linear and "loess" bivariate fit indicates that higher completion parameters lead to higher production. Recursive partitioning trees also support this finding. However, we see reduction in oil production with these parameters if we model production per the different variables. The average well costs in the Bakken increased from $6 - $6.5 million in 2008 to over $9-$10 million in 2011. More stages or proppant does not necessarily equate to more oil, but more cost.