Reservoir properties prediction using seismic inversion and geostatistical integration

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.