Integration of AVO Analysis and Seismic Inversion for Reservoir Characterization of an Oil Field, Niger Delta, Nigeria
Published: 2023-04-30
Page: 37-57
Issue: 2023 - Volume 6 [Issue 1]
V. O. Oguka
Department of Physics, University of Port Harcourt, Rivers, Nigeria.
G. O. Emujakporue
Department of Physics, University of Port Harcourt, Rivers, Nigeria.
A. O. Balogun *
Department of Physics, University of Port Harcourt, Rivers, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This study evaluates the characteristics of the G-field reservoirs using Amplitude-Versus-Offset (AVO) analysis and seismic inversion. Well log data were obtained from four deviated wells G-14, G-15, G-16, and G-17, and reservoirs were delineated using a combination of gamma and resistivity logs. The suitable well logs were used for the modeling of AVO and its analysis. A Post-stack 3D seismic data volume that covers an area of 266.68 km2 within which the four available wells are situated, was also utilized. Well correlation and seismic-to-well tie was done to facilitate horizon mapping on the seismic data and seismic volume was then inverted to give an impedance volume using model-based algorithm and other interesting parameters, using a neural network inversion algorithm. AVO analysis results using G–14 show a small negative intercept with subsequently higher amplitudes as the offset increases demonstrating a Class II AVO anomaly. The data slices of the extracted acoustic impedance, Mu–Rho, and Lambda–Rho volumes for the model-based inversion shows low values for hydrocarbon reservoir sand, especially around the producing wells, and high values for non-reservoir sand for both acoustic impedance and Lambda–Rho which is consistent with fluid determination while it was medium to high values around well locations indicating hydrocarbon bearing sand for both Mu–Rho and shear impedance. The data slices of , water saturation, and porosity shows that the four wells are located in areas with low and water saturation with very high porosity values indicating the presence of hydrocarbon.
Keywords: AVO, inversion, model-based, neural network
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