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

AAPG Asia Pacific Region, The 4th AAPG/EAGE/MGS Myanmar Oil and Gas Conference:
Myanmar: A Global Oil and Gas Hotspot: Unleashing the Petroleum Systems Potential

Datapages, Inc.Print this page

Artificial Neural Network (ANN) Prediction of porosity and Water Saturation of Shaly Sandstone Reservoirs

Abstract

This paper presents a successful application of neural networks (ANN) in predicting porosity, water saturation 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 determining 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 water saturation using shaly water saturation models (total shale, Simandoux and medium effective) and effective porosity in shalysand reservoirs. Two neural networks were presented to determine porosity and water saturation using GR, resistivity and density logging data and the cut off values for porosity and water saturation. Water saturation 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, water saturation and defined pay zones in a new well that projects its application for new wells. Neural network approached have trained for porosity and water saturation using the available well logging data. The predicted porosity and water saturation values have shown excellent matching with core data in the two wells comparing to the porosity and water saturation of the conventional techniques. Consequently, the developed network (ANN) can successfully deduce porosity, water saturation and defined pay zones of for new wells in shalysand.