Research article

Fluid Substitution and AVO Modeling for Hydrocarbon and Lithology Discrimination in X-Field, Offshore Niger Delta

*¹Ugwu SA and ²Nwankwo CN

¹Department of Geology, University of Port Harcourt, Nigeria

²Department of Physics, University of Port Harcourt, Nigeria

*Corresponding Author E-mail:ugwusa@yahoo.com; cyrilnn@yahoo.com

Accepted 14 August 2014

Abstract

Seismic amplitude analysis has been carried out using well logs and seismic data in order to discriminate and characterize fluid and lithology from two reservoirs in XXX-field, Niger Delta. This is aimed at reducing the risks associated with exploitation of hydrocarbon in the area. A repeat 3D seismic survey was processed through amplitude preserving monitoring purposes. Velocity field corrections were made and Forward modeling processing achieved by using Kingdom and Jason Work bench software packages. Correlation of depth-based log data with time-based seismic data for purpose of identifying prospect level on seismic was achieved by the synthetic-to-seismic tie. The rock properties obtained from Sonic and Density logs were used to construct reflection coefficients of the various reflectors or geologic boundaries. Gassmann fluid substitution was applied in order to gain insight and understanding of the subsurface layers at the interface between the considered reservoirs A (R1) and B and the overlying shale. The results of Gassmann fluid substitution and AVO modeling suggest that hydrocarbons can be discriminated from brine, but discrimination of oil from gas is somewhat difficult. AVO analysis taking the average Vp, Vs and density across the overlying shale and reservoir reveals that both targets of interest exhibit a Class 3 AVO anomaly. The cross plot technique proves effective in fluid discrimination within the reservoirs and can be used as a quality control tool on the reliability of well log data. The Bayesian analysis shows that the posteriori probability of 50% is obtained at the overlapping zone of oil and gas in both reservoir models.

Keywords: AVO, fluid discrimination, seismic, modeling, reservoir