Physics-integrated neural networks for improved mineral volumes and porosity estimation from geophysical well logs

Abstract

Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations, particularly in hydrocarbon exploration, CO₂ sequestration, and geothermal energy development. Current techniques, such as multimineral petrophysical analysis, offer details into mineralogical distribution. However, it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation. Furthermore, traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles. To address this, we present a new approach using Physics-Integrated Neural Networks (PINNs), that combines data-driven learning with domain-specific physical constraints, embedding petrophysical relationships directly into the neural network architecture. This approach enforces that predictions adhere to physical laws. The methodology is applied to the Broom Creek Deep Saline aquifer, a CO₂ sequestration site in the Williston Basin, to predict the volumes of key mineral constituents—quartz, dolomite, feldspar, anhydrite, illite—along with porosity. Compared to traditional artificial neural networks (ANN), the PINN approach demonstrates higher accuracy and better generalizability, significantly enhancing predictive performance on unseen well datasets. The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN, highlighting the superior accuracy of the PINN approach. This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions, providing a more robust tool for decision-making in various subsurface geoscience applications.