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 formation porosity and water saturation and to well identify 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 five well logging data inputs: GR, LLD, RHOB, NPHI and PEF. Shale volume was defined from GR data. Each neural network is trained using available logging data and validated using the core data before applying it to the entire well log. Neural network predicted formation porosity and water saturation for tested sections of one well. Also using the cut off values of porosity, water saturation and shale volume, the neural network defined the possible pay zones in the well. Network outputs have shown good matching with core data and the reference calculated petrophysical parameters. The developed network approach has successfully deduced porosity, water saturation and defined pay zones in a new well that projects its application for new wells.