Neural network identification of hydrocarbon potential of shaly sand reservoirs

Hydrocarbon potential evaluation of shaly sand layers requires the adoption of certain shaly water model and also the selection of suitable conventional logging suite. The main target is how to get the accurate porosity and how to convert the apparent water saturation to true water saturation for given shaly sand layer. In this paper neural network approach is presented to replace the conventional interpretation of well logging data to better determine fonnation porosity and water saturation and to identity hydrocarbon potential of clean and shaly sand layers. Two neural networks were constructed, one for prediction of porosity using six well logging data inputs: GR, LLD, RHOB, NPHI, PEF and t); and water saturation using 5 well logging data inputs: GR, LLD, RHOB, NPHI and PEF. Shale volume was defined from GR data. Four wells were used in this study. The core data from two wells are used to train and validate the neural networks for prediction of the fonnation porosity and water saturation. Network outputs have shown good matching with core data and the reference calculated petrophysical parameters. A hydrocarbon potential index is then defined based on cutoff values of porosity, water saturation, and shale volume. The trained neural networks together with the hydrocarbon potential index were used to predict possible pay zones in the four wells. The developed network approach has successfully deduced porosity, water saturation and defined pay zones in the four wells.