Abstract
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
| Original language | English |
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| State | Published - Dec 28 2019 |