Neural Network Identification of Hydrocarbon Potential of Shaly Sand Reservoirs

Abstract

Abstract 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 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 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 formation 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 Introduction Identification of hydrocarbon potential of a reservoir rock is the main question facing the well logging analyst. Not all hydrocarbon sections can be considered a potential section unless it satisfies the cut off values limitations. There are many assigned hydrocarbon layers, however, when put on test it did not show hydrocarbons. It is essential to identify the potentiality of pay formation from well logging. Conventionally, smart use of good well logging data combination may answer this question of hydrocarbon potential.