A New AVO Attribute for Hydrocarbon Prediction and Application to the Marmousi II Dataset

Abstract

At present, quantitative AVO plays a significant role in HC prediction in many basins. Hydrocarbon prediction from seismic amplitude and AVO is a daunting task. Many AVO attributes were presented for this purpose. Based on Mudrock equation, 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 (1994) redefined 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 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 the 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, Gidlow's and Fatti's fluid factor also show anomalies for water-bear 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 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 non-pay zone. In this case, J attribute has better performance than both Gidlow's and Fatti's fluid factor in hydrocarbon prediction. It can predict presence of possible H.C. 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.

AAPG Datapages/Search and Discovery Article #90217 © 2015 International Conference & Exhibition, Melbourne, Australia, September 13-16, 2015