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Comparison of popular AVO attributes, AVO inversion, and calibrated AVO predictions

2002; Society of Exploration Geophysicists; Volume: 21; Issue: 3 Linguagem: Inglês

10.1190/1.1463776

1938-3789

Christopher P. Ross,

Hydraulic Fracturing and Reservoir Analysis

Amplitude variation with offset techniques are used by exploration, development, and production teams to assist hydrocarbon identification in clastic depositional settings. 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. Accurate geoscience and engineering reservoir charcterization (parametrization) improve prediction of hydrocarbon reserves and reservoir production. It is therefore essential to understand which seismic attributes will best contribute to the characterization of the reservoir. This paper focuses on the accuracy of AVO attributes commonly used in reservoir characterization. In particular, the effectiveness of intercept-gradient, lambda-mu-rho λ-μ-ρ), and elastic impedance AVO attributes, and their ability to accurately predict reservoir extent are presented. Derivative AVO attribute volumes that are caliabrated to well data are also compared. It can be argued that these techniques are essentially the same, because each attribute set comes from the measurement of AVO across a velocity-corrected CMP gather. But some geoscientists prefer one attribute type to another or only have access to a certain attribute. This paper examines methodology differences between the various AVO attributes and, more importantly, compares the final reservoir description predicted by these attributes. 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. Rather it is a method to determine the best AVO input (if any) to accompany other geophysical and geologic inputs to the modeling. A 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. A thin blocky sand encased by shales was selected as the reservoir. This sand, when gas-saturated, can be categorized as a class …