Machine learning for predicting stochastic fluid and mineral volumes in complex unconventional reservoirs: A machine learning workflow can quickly, and accurately, predict mineralogy, porosity and saturation in multiple wells to better understand productive layers in unconventional oil reservoirs

The article focuses on determination of mineralogy is a critical step in the petrophysical analysis of many types of reservoirs and changes in volumes of minerals indicate changes in geological deposition, diagenesis, reservoir quality and brittleness. Topics include the potentially productive layers in a shale play are defined as layers that are sufficiently brittle to respond to hydraulic fracture treatments, the layer's brittleness is determined predominately by its relative mineral ratios.