Dewanto, Ordas and Mulyatno, Bagus Sapto and Rizki Ordas, Perdana and Wibowo, Rahmat Catur (2021) Reservoir properties prediction using seismic inversion and geostatistical integration. IOP Publishing Ltd., United Kingdom.
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37. Prosiding Internasional Scopus, Dewanto_2021_IOP_Conf._Ser.__Mater._Sci._Eng._1173_012008.pdf Download (1MB) | Preview |
Abstract
Exploration and production in the sand reservoir still have their challenges due to the low porosity and permeability characteristics. This study was conducted to analyse the characteristics of a tight sand reservoir based on a log property distribution map, using acoustic impedance inversion and multi-attribute analysis. Stepwise regression multi-attribute analysis is a method that uses the best attributes to predict the target log by going through a trial and error process. Choosing the right seismic attributes can provide a better depiction of the target zone. This research was conducted to obtain a map of subsurface geological structures, acoustic impedance volumes. Then, we performed a multi-attribute analysis to obtain a prediction of volume log properties such as pseudo gamma-ray, density and porosity using the stepwise regression method. The results of acoustic impedance seismic inversion and stepwise regression multi-attribute analysis shows that the reservoir is a gas with tight sand lithology, which has a range of acoustic impedance values of 15,000 ((ft/s)*(g/cc)) up to 30,000 ((ft/s)*(g/cc)), porosity of 12% to 24%, and the distribution of Sw of 8-13%. The density and porosity maps obtained from the multi-attributes analysis can help in the long-term exploration and production stages. Its aims are to improve primary recovery and tertiary recovery, understanding the stratigraphic traps, and continuity of reservoir layers.
| Item Type: | Other |
|---|---|
| Subjects: | Q Science > Q Science (General) Q Science > QE Geology |
| Divisions: | Fakultas Teknik (FT) > Prodi Teknik Geofisika |
| Depositing User: | ORDAS DEWA |
| Date Deposited: | 18 Jul 2022 01:11 |
| Last Modified: | 18 Jul 2022 01:11 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/43333 |
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