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Spatial modelling of unconventional wells in the Niobrara Shale play : a descriptive, and a predictive approach.

Hokstad, Vegard; Tiganj, Dzenana
Master thesis
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URI
https://hdl.handle.net/11250/2679380
Date
2020
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  • Master Thesis [3258]
Abstract
This research investigates oil production in the modestly studied Niobrara shale play,

using data containing information about well-design and production volumes from wells

drilled in the period 2011 - 2018. Firstly, machine learning techniques were employed to

conduct a descriptive analysis, with the motive of identifying drivers of well-productivity.

Models of increasing spatial resolution were applied to isolate the effect of high-grading

of geological conditions from well-design choices. The statistical models employed were

different Random Forest (RF) configurations, Geographical Random Forest (GRF), and

the well-established technique of Regression Kriging (RK). It was found that spatial effects

were attributed slightly short of 40% of the relative importance in explaining variations in

the first-year production volumes of oil. Further, it was found that models attributed too

much importance to well-design variables if spatial effects were not adequately accounted

for. It was also found that the more data-driven and less restrictive RF and GRF

performed slightly better than the widely recognized RK. Secondly, a predictive analysis

was conducted in an attempt at identifying undrilled locations with favorable geology for

future drilling. For this part of the research, data containing information about geological

conditions were utilized, alongside the production data. It was found that applying RF

and GRF yielded quite disappointing results when the task was formulated as a regression

problem. However, lowering the precision by rephrasing the task as a classification problem

resulted in a RF classifier that outperformed random guessing to some extent. A visual

assessment of the classifier’s generated heatmap of predictions suggested that the model

was highly capable of identifying geological settings associated with the most extreme

wells, in terms of productivity.

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