Porosity and permeability prediction from petrographic point-counting data using machine learning: Applications to Rotliegendes and Buntsandstein reservoirs | Synapse
March 3, 2026Open Access
Porosity and permeability prediction from petrographic point-counting data using machine learning: Applications to Rotliegendes and Buntsandstein reservoirs
Key Points
Porosity and permeability prediction demonstrates significant correlation with petrographic data, enhancing reservoir analysis.
Key evidence shows the model achieves accuracy rates above 85% in predicting reservoir properties.
Analysis utilizes supervised machine learning techniques on petrographic point-counting data for effective modeling.
Implication suggests improved reservoir characterization methods could lead to better resource management and efficiency.