Variable Input Neural Network Ensembles in Generating Synthetic Well Logs

This paper discusses a hybrid method for construction of neural network ensembles (NNE) in generating synthetic well logs that is often driven by the needs of simulating unobtainable actual logs, reducing the operational cost, reconstruction of missing and/or bad log data, and minimizing the hazards associated with using radioactive sources. In this method, several computer-driven routines are developed to rank the candidate neural network inputs as a function of data partition, network complexity and initialization. Then a network pool is automatically formed having the selected candidate networks characterized with multi-set inputs and different hidden nodes. The ensemble optimization is performed using a multi-objective genetic algorithm by aggregating the ensemble validation error, complexity, and negative correlation into a single quantity of merit. The simulations applied to actual field examples demonstrate that using multi-set-input NNE is more robust than using single-set-input NNE with significantly reduced uncertainty and improved prediction accuracy on the new data for some applications.