| Issue |
E3S Web Conf.
Volume 125, 2019
The 4th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2019)
|
|
|---|---|---|
| Article Number | 15006 | |
| Number of page(s) | 5 | |
| Section | Non-renewable Energy/Fossil Energy | |
| DOI | https://doi.org/10.1051/e3sconf/201912515006 | |
| Published online | 28 October 2019 | |
Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes
1 Department of Physics, Faculty of Mathematics and Natural Science, University of Indonesia, Depok – Indonesia
2 PT. Pertamina NSB, Jakarta – Indonesia
* Corresponding author: syamsu.rosid@ui.ac.id
It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.
Key words: Porosity / Probabilistic Neural Network / Multi-attribute / Seismic Attributes
© The Authors, published by EDP Sciences, 2019
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