Application of Machine Learning to Select the Most Favorable Hydraulic Fracturing Intervals

What is it about?

The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs.

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Photo by Goran Ivos on Unsplash

Photo by Goran Ivos on Unsplash

Why is it important?

This paper presents the practical use of machine learning to evaluate brittleness in a cost and time-effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for the initial screening of fracturing candidates and the selection of fracturing intervals in other clay-rich and heterogeneous shale formations.