Seismic interpretation pitfalls can occur even with advanced geophysical technologies applied to high quality seismic data. In this paper we present one such example. It is an AVO interpretation pitfall where the sign of the gradient of a Class III reservoir is different whether we use well log data or pre-stack seismic data. This pitfall is extremely damaging because the combined interpretation of gradient (G) and intercept (I) has been used extensively to derisk prospects. Worse, another interpretation pitfall was to believe that angle-dependent tuning within the reservoir was the primary source of this attribute distortion because the reservoir thickness was equal to or less than the tuning thickness (λ/4). Therefore, further exploration of the physics of wave propagation (seismology) is essential to accurately explain this dramatic variation in bright amplitudes at the reservoir top. Currently, AVO (amplitude variation with offset) technology can be considered as a highly effective tool for the detection and characterization of hydrocarbon reservoirs. The basis of this technology is the dependence of the seismic amplitudes on the petrophysical parameters of subsurface rocks. This dependence is manifested in the variation of amplitudes as a function of source-receiver distance in seismic surveys. This amplitude variation is better understood in the incident angle domain using the Zoeppritz equations, which approximate the solution of the elastic wave equation. The elastic wave equation is an integral part of AVO technology. The elastic wave and Zoeppritz equations make up what we call AVO modeling. In addition to all this, the introduction of AVO classes has benefited seismic interpreters by providing a clear, simple, and systematic approach to quantitative seismic interpretation (QI) in terms of petrophysical parameters. In this sense, rock physics is another relevant discipline in AVO technology. Rock physics studies the relationship between the seismic properties of rocks (P-wave and S-wave velocities and density) and the reservoir properties of interest. Successful results therefore require optimal integration of all these disciplines to understand the physical mechanisms behind the wave propagation phenomena. The purpose of this article is to highlight the importance of incorporating physical mechanisms into AVO workflows to minimize (resolve) interpretation pitfalls and maximize reliability. We start with rock physics to better understand the data and the geology of the study area. We then design AVO modeling experiments to evaluate different physical mechanisms to explain the observations. This evaluation helped us understand why and how the reservoir attributes were distorted. We now know that the thin (~λ/8) shale overlying the reservoir was responsible for the wave interference phenomenon capable of changing the sign of the AVO gradient. This finding restores the potential of seismic and AVO to contribute to the exploration and production process in the study area.