Porosity Evaluation Using Artificial Neural Network, Optimized with GA and PSO

The precise evaluation of porosity is fundamental to reservoir characterization and volumetric assessment.While direct measurements from core analysis provide accurate results, they are economically and operationally prohibitive for continuous formation evaluation. This study presents a robust machine learning framework that employs Artificial Neural Networks (ANNs)—Multilayer Perceptron (MLP) and Radial Basis Function (RBF)—to predict porosity from conventional well logs. To maximize predictive accuracy and convergence efficiency, the models are optimized using two metaheuristic algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Applied to a dataset from a carbonate reservoir in South-West Iran, the hybrid models (MLP-GA, MLP-PSO, RBF-GA, RBF-PSO) demonstrate a superior performance compared to their non-optimized counterparts. The optimization led to a significant increase in the correlation coefficient (R) and a substantial reduction in the Mean Square Error (MSE) for both vertical and horizontal porosity estimates. This research conclusively establishes that the synergy of ANNs with evolutionary optimizers offers a reliable, cost-effective, and rapid solution for porosity prediction, with strong potential for broader application in petrophysical property estimation.