Open Access


Download data is not yet available.


This paper focuses on the analysis of seismics attributes and the application of training algorithms for artificial neural networks to build lithofacies model based on which porosity distribution across a reservoir is modeled. Firstly, seismic attribute and facies log, porosity log are modeled using the standard procedure of Petrel software (Schlumberger). After that, the resulting models are extracted and used as an input data for SOM-supervised algorithm. The result of this step is a map showing the relationship beetwen seismic attributes and facies. In the next step, the map is used to build 3D facies model. With the same procedure, the 3D porosity model is build by Fitnet algorithm. In this work, ANN training, facies modeling and porosity modeling were implemented with MATLAB and the resulting models were compared to the ones that resulted from Petrel software. The good agreement in the porosity distribution patterns between the two models shows that the computational background used in this research is similar to that of Petrel software. The paper contributes to new insights into the fundamentals of computational algorithms used in Petrel which has not been thoroughly studied in Vietnam, and thus helps improve the software usage in reservoir properties modeling.

Author's Affiliation
Article Details

 Copyright Info

Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Mai, L., & Trần, H. (2021). Resevoir property modelling with seismic attributes and artificial neural network. Science & Technology Development Journal - Engineering and Technology, 4(SI3), SI61-SI69.

 Cited by

Article level Metrics by Paperbuzz/Impactstory
Article level Metrics by Altmetrics

 Article Statistics
HTML = 0 times
PDF   = 1 times
Total   = 1 times