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Improving Prediction of Wireline Data Using Artificial Neural Network

Aly Rafaat, Ahmed Mohamad Essam (2015) Improving Prediction of Wireline Data Using Artificial Neural Network. IRC, Universiti Teknologi PETRONAS. (Submitted)

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This paper is dedicated to investigate the capabilities of artificial neural network (ANN) to improve prediction of petrophysical properties. Furthermore, this project is intended to test capability of network to predict the logging tools readings based on other tools readings. For petrophysical data prediction, it will be limited to predicting values of porosity by comparing predicted values from different models and values obtained from core data. Data obtained for core is considered to be the most accurate representation of petrophysical data. Hence, it is used as a reference data for testing capabilities of the model and training ANN networks.

Item Type: Final Year Project
Academic Subject : Academic Department - Petroleum Geosciences - Petrophysics - Petrophysical data acquisition
Subject: T Technology > T Technology (General)
Divisions: Geoscience and Petroleum Engineering
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 28 Jul 2016 10:24
Last Modified: 25 Jan 2017 09:35
URI: http://utpedia.utp.edu.my/id/eprint/16682

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