At present, quantitative AVO plays a significant role in hydrocarbon prediction in many basins. Hydrocarbon prediction from seismic amplitude and AVO is a daunting task. Many AVO attributes are presented for this purpose. Based on the Mudrock equation, Smith and Gidlow (1987) defined an attribute named fluid factor as: ΔF=ΔVp/Vp-1.16ΔVs/Vs/γ (1) Where γ is the background Vp/Vs ratio, the constant 1.16 can be local value of Vp-Vs relation. Fatti et al. (1994) redefined the fluid factor as: ΔF=Rp-1.16Rs/γ (2) Where Rp is P-reflectivity and Rs is S-reflectivity. However, there exist several pitfalls in the AVO technique. One of the pitfalls is that a high porosity and good quality brine sand can give a rising AVO response. In this paper, we propose a new attribute called “J” for hydrocarbon prediction as follow: J=Jp sinα-Js cosα (3) Numerical and Marmousi II model are used to test the new method in this paper. In the numerical simulation, brine responses of J attribute are relatively stable with varying of porosity whereas hydrocarbon responses decrease under effect of porosity. In these two fluid factor cases, water response with high porosity can equal to hydrocarbon response with lower porosity which cause an ambiguity in interpretation. A part of Marmousi II model is used to compare performances of different attributes. The results show that all three attribute can detect hydrocarbon sands. However, Smith and Gidlow's and Fatti's fluid factor also show anomalies for water-bearing layer which can be misleading whereas J attribute is more sensitive to hydrocarbon. J attribute is less ambiguous in hydrocarbon detection. In summary, this study presents a new AVO attribute J and we compare it with Smith and Gidlow's and Fatti's fluid factor. This method is simple, fast, and an effective exploration tool. Through Marmousi II model study, we demonstrated that J attribute can detect hydrocarbons and has fewer anomalies from a non-pay zone. In this case, J attribute has better performance than both Smith and Gidlow's and Fatti's fluid factor in hydrocarbon prediction. It can predict presence of possible hydrocarbon sand effectively and reduce the ambiguity caused by lithology. Further, AVO attribute has to be interpreted as a guideline in exploration. Predicting hydrocarbon from amplitude has to be geologically/rock physics considered with structure and petroleum system playing a major role in reducing exploration risk. Introduction AVO interpretation for hydrocarbon prediction is a daunting task because pore-fill fluid and porosity affect amplitude simultaneously. A useful AVO attribute called “fluid factor” is presented for hydrocarbon prediction by Smith and Gidlow (1987) and Fatti et al. (1994) respectively. This study defines a new attribute “J” to detect hydrocarbons. It is supposed to be more sensitive to the existence of hydrocarbons and has less ambiguity caused by porosity. Numerical simulation and Marmousi II dataset are used to test the new attribute “J” and the results illustrate the effectiveness and less ambiguity of the J attribute. J attribute is a useful tool for hydrocarbon prediction and risk reduction in exploration. At present, AVO attribute analysis plays a significant role in hydrocarbon prediction in many basins where rocks are generally soft, unconsolidated and are sensitive to fluid replacement and response as per Gassmann (Ghosh et al., 2014). However, there exist several pitfalls in amplitude interpretation. One of the pitfalls is that good quality brine sands can give rising AVO responses in line with Gassmann fluid replacement algorithm as shown in Figure 1 (Ghosh et al., 2010). One of the widely-used AVO attributes for hydrocarbon prediction is fluid factor. Smith and Gidlow 1987 defined an attribute named fluid factor based on the Mudrock line (Castagna et al., 1985). The fluid factor is written as: ΔF ∆ 1.16 ∆ / (1) where, γ is the background Vp/Vs ratio and the constant 1.16 can be a local value of Mudrock line. Fatti et al., 1994 redefined the fluid factor as