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

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.