Improving Prediction of Wireline Data Using Artificial Neural Network
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 da...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
IRC
2015
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| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/16682/1/eline_data_for_Dulang_field_with_Artificial_Neural_Network2%20%281%29.pdf http://utpedia.utp.edu.my/16682/ |
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| Summary: | 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. |
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