Pre-Stack Data Prediction for Fluvial Reservoirs
Yuelong Yue; Hongtao Chen; Yuhai Li; Tinghui Li; Bingling Li; Yuhua Bai; Huimin Shi
The study area is located at the southeast flank of North Dagang buried hill structural belt in the center of Huanghua Depression. As affected by faulted basin, there are multi sets of oil-bearing series vertically and diversified reservoirs horizontally, forming a large composite hydrocarbon accumulation zone. It is favorable for oil and gas to generate, migrate and accumulate. The reservoirs here are dominated by fluvial clastic rocks, it is a set of unconsolidated high-porosity high permeability sandstone reservoirs, the oil layer changes greatly. Therefore, how to predict the channel sands accurately is a key factor to delineate the lithologic reservoirs here.
The pre-stack simultaneous inversion prediction technique is used in this study. It mainly involves: 1) seismic amplitude-preserved processing, including dynamic/static correction of seismic data, conversion of CMP data to CRP data, and acquisition of different partial-stack angle data; 2) well data processing, including prediction of S-wave velocity and evaluation of K value, calculation and analysis of multi-angle EI curves for each well based on several well logs, filling of wells and well location calibration, and correlation between well curves and seismic data, etc.; 3) extraction of angular wavelets, including extraction of optimum wavelet corresponding to each angle gather with the seismic statistics method by combining wavelet data; 4) establishment of elastic impedance inversion model; application of several constraints to adjust the related parameters, providing the most reasonable model; 5) pre-stack parameter simultaneous inversion; simultaneous calculation of P/S-wave impedance and density by using different angle data, so as to obtain the elastic parameters, such as P-S velocity ratio, Poisson's ratio and bulk modulus. This study helps to improve the prediction accuracy of lithologic reservoir in the study area and reduce the risks in further development and deployment, providing a typical application of pre-stack data prediction technique.
AAPG Search and Discovery Article #90163©2013AAPG 2013 Annual Convention and Exhibition, Pittsburgh, Pennsylvania, May 19-22, 2013