This sand, when gas-saturated, can be categorized as a class 2 AVO reservoir or, in other words, a gas reservoir with a higher velocity and lower density than surrounding shales. A thin blocky sand encased by shales was selected as the reservoir. Rather it is a method to determine the best AVO input (if any) to accompany other geophysical and geologic inputs to the modeling.Ī data model with variable reservoir thicknesses was constructed using well log data from the middle Miocene section on the northern continental shelf of the Gulf of Mexico. Please note that looking for the best AVO attribute for reservoir characterization does not mean that this attribute will be the sole seismological contribution to the reservoir parameterization for reservoir simulations. This paper examines methodology differences between the various AVO attributes and, more importantly, compares the final reservoir description predicted by these attributes.
Derivative AVO attribute volumes that are calibrated to well data are also compared. In particular, the effectiveness of interceptgradient, ?, and elastic impedance AVO attributes, and their ability to accurately predict reservoir extent are presented. This paper focuses on the accuracy of AVO attributes commonly used in reservoir characterization. It is therefore essential to understand which seismic attributes will best contribute to the characterization of the reservoir. Accurate geoscience and engineering reservoir characterization (parameterization) improve prediction of hydrocarbon reserves and reservoir production. While exploration groups tend to use AVO attributes for detection and risk quantification, exploitation and production groups use AVO attributes for reservoir characterization and even fluid-front monitoring.
Amplitude variation with offset (AVO) techniques are used by exploration, development and production teams to assist hydrocarbon identification in clastic depositional settings.