Predicting water saturation using artificial neural networks (ANNS)

The application of artificial neural networks (ANNs) in the petroleum industry is widely increasing after major developments in ANN design. In this study, ANNs were used to develop a model for predicting water saturation in shaly formations using wireline well logs and core data. A general workflow (methodology) was constructed in order to cover different design issues in the ANN modelling. The workflow presents how the neural network results can be interpreted in terms of investigating the contribution of input variables and comparing the results with other regression models. In addition, the workflow focuses on the relevance of the statistics of the data and the importance of determining the uncertainties in the original data before using it in the model. The data for this study was taken from a sandstone formation in Oman. The input variables to the model were density, neutron, resistivity and photo-electric wireline logs. A three layered feed-forward neural network model with five hidden neurons and Resilient Back-propagation (PROP) algorithm was found to be the best design. The neural network model was able to predict the water saturation directly from wireline logs with a correlation factor of 0.91 and a root mean square error (RMSE) of 2.5%.