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Porosity Prediction for Gullfaks Field with Adaptive Neuro Fuzzy Inference System

Min Sheu, Joel Lim (2015) Porosity Prediction for Gullfaks Field with Adaptive Neuro Fuzzy Inference System. IRC, Universiti Teknologi PETRONAS. (Submitted)

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Petrophysical properties such as porosity and permeability are critical in reservoir characterization. However obtaining these properties from core data is sometimes too costly for oil companies. Therefore prediction of petrophysical properties from well logs is sometimes more preferable method as it is cheaper. However, uncertainties will be present during predictions and may affect the predicted results. In this project, an artificial intelligence (AI) method known as Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in predicting one of the petrophysical properties - porosity. ANFIS is the combination of other two AI namely Artificial Neural Network and Fuzzy Inference System. The main objective is to prove that ANFIS is the best prediction technique as compared with the conventional model. The project uses well logs data from Gullfaks Field as the input data for ANFIS and data of core plugs from the same field as the output data. By comparing with the conventional method - Monte Carlo method, the ANFIS model yielded a higher correlation coefficient and lower root mean squared error, confirming that ANFIS predicting ability is more accurate than that of conventional method.

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: 19 Oct 2016 09:08
Last Modified: 25 Jan 2017 09:36
URI: http://utpedia.utp.edu.my/id/eprint/16865

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