APPLICATION OF SEISMIC MULTI-ATTRIBUTE PROBABILISTIC NEURAL NETWORK TRANSFORM IN ‘BIMBO’ FIELD, NIGER DELTA, NIGERIA

Abstract

A Probabilistic Neural Network (PNN) multiattribute transform was applied to a three – dimensional (3D) seismic data and a suite of borehole log data obtained within ‘Bimbo’ field, offshore Niger Delta. This was done to characterize the delineated reservoirs in terms of the predicted porosity, density and acoustic impedance variations across the reservoirs. Tops of hydrocarbon-bearing reservoirs were identified on the well logs which were tied to the seismic data with the aid of a synthetic seismogram A post stack seismic inversion was carried out to create an impedance volume which served as an external attribute for the Multi-linear attribute regression analysis. The multi-attribute linear regression analysis determined the optimum group of seismic attributes to be used for the Probabilistic Neural Network which was trained to generate a nonlinear relationship between the specified well log petrophysical parameters (density and porosity) and the seismic attributes. The established relationship was used to predict the petrophysical parameters of interest across the study area through which hydrocarbon prospect zones were identified. Three (3) hydrocarbon-bearing sands, - Res_A, Res_B and Res_C, - were delineated from the well log data and the petrophysical parameters were calculated. Application of the Probabilistic Neural Network in predicting porosity provided a correlation coefficient of 85% while its validation result provided a correlation coefficient of 75%. For Density prediction, the Probabilistic Neural Network provided a correlation coefficient of 90% while its validation result provided a correlation coefficient of 87%. Integration of the PNN-predicted petrophysical maps (Porosity, Density and Acoustic impedance) for the tops of Res_B and Res_C revealed two prospect areas ‘X’ and ‘Y’ characterized by low acoustic impedance values, high porosity and low density. The study has established a reliable relationship between log-derived petrophysical parameters of the reservoirs and seismic attributes.