Abstract

For the purpose of developing, managing, and optimizing a reservoir's output, accurate reservoir characterization is a crucial step. Reservoir characterization must be dynamic in order to attain accuracy and guarantee that all information available at any given time is incorporated into the reservoir model. But in order to do so, one must first create a static model. Static model is a straightforward representation of the reservoir at a specific time in order to begin. With the availability of newly acquired petrophysical, seismic, and production data, the reservoir model is adjusted to account the changes in the reservoir. The improved model would serve as a more accurate gauge of the state of reservoir at the moment. In the present study, a reservoir characterization using sparse layer reflectivity (SLR) inversion has been introduced. The inversion is carried out by defining a set of functions to characterize reflectivity patterns and constructing the seismic trace as a superposition of these patterns. A small number of reflection responses are discovered through basis pursuit decomposition, and these responses are combined to produce the seismic trace. The method is first tested for the composite trace extracted near to well location. The qualitative and quantitative analysis of composite trace shows that the correlation between inverted and original impedance is 0.99 with 0.041 RMS error. Thereafter, the post-stack seismic data from the Blackfoot field, is inverted into acoustic impedance (AI) and interpretation has been done. The inverted section shows that AI varies between 6000-18000m/s*g/cc with very high-resolution subsurface information. A low impedance anomaly has been noticed near to 1050-1065ms time interval and interpreted as a sand channel. The SLR inversion approach is particularly effective at identifying thin layers that other seismic inversion techniques can