Generating a capillary saturation-height function to predict hydrocarbon saturation using artificial neural networks

A well-known method to determine the hydrocarbon saturation distribution in a reservoir model is by using a saturation-height function derived from capillary pressure measured on core samples. This approach fails, however, in complex formations and does not use information from wireline logs. In this paper we use an artificial neural network to develop a saturation-height function for the complex Gharif Formation in Oman to predict the hydrocarbon saturation.

Different neural network models were developed using different input variables. The optimal model was able to generate the saturation-height function with an error of 0.046 (fraction of pore volume, PV) using wireline logs, including the logarithm of resistivity, cation exchange capacity and porosity. This is a considerable improvement over conventional methods based on capillary pressure. The neural network model was then used to predict the saturation in the formation as a function of depth, and robust results were obtained.