--> Artificial Neural Network (ANN) Prediction of porosity and Water Saturation of Shaly Sandstone Reservoirs
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Artificial Neural Network (ANN) Prediction of porosity and Previous HitWaterNext Hit Previous HitSaturationNext Hit of Shaly Sandstone Reservoirs

Abstract

This paper presents a successful application of neural networks (ANN) in predicting porosity, Previous HitwaterNext Hit Previous HitsaturationNext Hit and identifying lithofacies of shalysand reservoir using well logging data. ANN technique utilizes the prevailing unknown nonlinear relationship in data between well logs and the reservoir rock petrophysical properties. In heterogeneous reservoirs classical methods face problems in Previous HitdeterminingNext Hit accurately the relevant petrophysical parameters due to assumptions and uncertainties of input parameters. Applications of artificial intelligence have recently made this challenge a possible practice and in this study neural network has been proposed to supplement or replace the existing conventional techniques to determine Previous HitwaterNext Hit Previous HitsaturationNext Hit using shaly Previous HitwaterNext Hit Previous HitsaturationNext Hit models (total shale, Simandoux and medium effective) and effective porosity in shalysand reservoirs. Two neural networks were presented to determine porosity and Previous HitwaterNext Hit Previous HitsaturationNext Hit using GR, resistivity and density logging data and the cut off values for porosity and Previous HitwaterNext Hit Previous HitsaturationNext Hit. Previous HitWaterNext Hit Previous HitsaturationNext Hit and porosity have been determined using conventional techniques and neural network approach for two wells drilled in shalysand reservoir. ANN outputs have shown good matching with core data and the reference calculated petrophysical parameters; porosity, Previous HitwaterNext Hit Previous HitsaturationNext Hit and defined pay zones in a new well that projects its application for new wells. Neural network approached have trained for porosity and Previous HitwaterNext Hit Previous HitsaturationNext Hit using the available well logging data. The predicted porosity and Previous HitwaterNext Hit Previous HitsaturationNext Hit values have shown excellent matching with core data in the two wells comparing to the porosity and Previous HitwaterNext Hit Previous HitsaturationNext Hit of the conventional techniques. Consequently, the developed network (ANN) can successfully deduce porosity, Previous HitwaterNext Hit Previous HitsaturationTop and defined pay zones of for new wells in shalysand.