Integration Between Well Logs and CT Information to Estimate Petrophysical Properties Through a Neural Network Model

Reservoir petrophysical characterization is traditionally performed through the interpretation of well logs validated with routine core analysis (RCAL), often excluding the integration of other tools such as computed tomography (CT), which provides interpretation of higher resolution. In this study, artificial neural network (ANN) models were applied to estimate porosity and permeability by integrating conventional logs with CT-derived data (RHOB and PEF), thereby validating the petrophysical model of Ciénaga de Oro Formation. Neural networks were trained in MATLAB^® using a feed-forward regression network based on a multilayer perceptron (MLP) architecture, with RCAL measurements serving as a reference. Model performance was assessed by comparing predictions with laboratory data from two wells, yielding high accuracy (R² = 0.98 for permeability and R² = 0.90 for porosity) with mean absolute errors below 5%. Additional validation was performed using well logs and CT data from complete 3 ft sections, with the trained models successfully reproducing core heterogeneities at millimetric resolution. These results confirm the potential of integrating well logs and CT data with ANN to enhance petrophysical characterization and extend property estimation to wells lacking core or laboratory measurements. Furthermore, an interactive MATLAB^® tool was developed, enabling users to load well logs and CT files as flat inputs, generate high-resolution predictions, validate results, and export the estimated values.