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. Fred Jenson, Chiranjith Ranganathan, Shi Xiuping, Ted Holden, CGG
Determination of mineralogy is a critical step in the petrophysical analysis of many types of reservoirs. Changes in volumes of minerals indicate changes in geological deposition, diagenesis, reservoir quality and brittleness.
Particularly in shale plays, success depends on selecting leases, based on identification of hydrocarbons-in-place within potentially productive layers. Potentially productive layers in a shale play are defined as layers that are sufficiently brittle to respond to hydraulic fracture treatments. A layerâs brittleness is determined predominately by its relative mineral rat