Seismic Inverted Acoustic Impedance for Lithofacies Discrimination and Reservoir Fluids Prediction A Case of ‘Ovi’ Field, Niger Delta

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J. O. Amigun


Assessing and prediction of reservoir quality beyond areas covered by wells has always been a desirable goal for geoscientists. One technique that seeks to provide the prediction of reservoir properties from seismic data and solve this problem is seismic inversion. In this study, 3D seismic data, checkshot data and suite of wireline data which consist of sonic, density, gamma ray, resistivity and porosity logs were used. Following the initial seismic data interpretation and petrophysical analysis, acoustic impedance crossplots and seismic inversion were carried out using a model based approach on Hampson RussellTM software. Three reservoir sands (A, B and C) were mapped within the Agbada Formation. From their petrophysical analysis results, Net/Gross ranges from 0.56 - 0.75, porosity, F (0.31 – 0.34), permeability, K (2879 - 3294mD) and hydrocarbon saturation, Sh (0.60 – 0.61). These results imply that the reservoirs have good qualities. The crossplot of acoustic impedance and gamma ray values shows three lithologies which were inferred base on the cluster i.e. impedance range 24500 - 27500(ft/s)*(g/cc) for shale, 22000 - 24500(ft/s)*(g/cc) for water bearing sands and 17500 - 21500(ft/s)*(g/cc) for hydrocarbon bearing sands. The crossplot porosity shows an inverse relationship with acoustic impedance while that of acoustic impedance shows a linear relationship with water saturation. The inverted seismic section on tested inline shows that the three horizons of interest (reservoir sand tops) fall on impedance range associated with sand from the crossplot analysis i.e. 17500-24500(ft/s)*(g/cc). Average acoustic impedance maps were generated for the three horizons with a time window of 5ms. And from the maps, zones of low impedance were predicted as high porosity and low water saturation based on the crossplot analysis. These zones are characterized as hydrocarbon bearing zones, thus making additional six new prospects (i.e. A, B, X, Y, Z, R and Q) to be identified. This study has been able to accomplish lithofacies discrimination needed for accurate well placement to enhance hydrocarbon productivity and reservoir fluids prediction away from well control spot.

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