Neural Network Prediction of P-wave Log for Reservoir Characterization in the Niger Delta Basin

Abstract

This study present a new technique that integrates several logs for P-wave prediction to minimize some errors and uncertainties associated with most estimation methods. The adopted method involves application of an artificial neural network technique that integrates density, resistivity and gamma ray logs for data training and the prediction of P-wave log. The results obtained gave correlation coefficient of 0.77, 0.24 and 0.42 between the acquired P-wave log and the acquired density, resistivity and gamma ray logs respectively, to demonstrate the relationship between P-wave log and the selected logs for the prediction process. The correlation coefficient of the estimated P-wave from Gardner and Faust methods with the acquired P-wave log are 0.64 and 0.59 respectively, while that of  the  neural network derived P-wave gave a better correlation coefficient  of  0.81. Cross plot validation of P-wave derive Acoustic Impedance against density for both lithology and fluid discrimination revealed clusters for neural network derived P-wave parameter similar to the acquired P-wave derived parameters. Results of the presented neural network technique have been demonstrated to be more effective than results of the two conventional techniques.