Study on characterizing reservoir parameters dynamic variations by time-lapse seismic attributes is the theoretical basis for effectively distinguishing reservoir parameters variations and conducting time-lapse seismic interpretation,and it is also a key step for time-lapse seismic application in real oil fields. Based on the rock physical model of unconsolidated sandstone,the different effects of oil saturation and effective pressure variations on seismic P-wave and S-wave velocities are calculated and analyzed. Using numerical simulation on decoupled wave equations,the responses of seismic amplitude with different offsets to reservoir oil saturation variations are analyzed,pre-stack time-lapse seismic attributes differences for oil saturation and effective pressure variations of P-P wave and P-S converted wave are calculated,and time-lapse seismic AVO (Amplitude Versus Offset) response rules of P-P wave and P-S converted wave to effective pressure and oil saturation variations are compared. The theoretical modeling study shows that it is feasible to distinguish different reservoir parameters dynamic variations by pre-stack time-lapse seismic information,including pre-stack time-lapse seismic attributes and AVO information,which has great potential in improving time-lapse seismic interpreta-tion precision. It also shows that the time-lapse seismic response mechanism study on objective oil fields is especially important in establishing effective time-lapse seismic data process and interpreta-tion scheme.
CHEN XiaoHong1,3,LI JingYe2,3 & ZHAO Wei4 1 Key Lab of Geophysical Exploration of CNPC,China Petroleum University,Beijing 102249,China
Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.
