PREDICTION OF POROSITY OF RESERVOIR SANDS USING SEISMIC ATTRIBUTES IN “ARIKE” FIELD NIGER DELTA, NIGERIA

Abstract

The study aimed at predicting the porosity of reservoir sands in ‘Arike field’ Niger Delta, Nigeria by converting seismic trace of the interval of interest in the seismic survey into a porosity log to generate a porosity volume. Optimal number of relevant attributes were selected using multi-attribute analysis. The study discovered that three attributes (energy, velocity fan, and Q factor) were efficient. These attributes were then utilized to train a supervised neural network to establish the relationship between seismic response and porosity. The Opendtect software used, extracted all specified input attributes and target values over the specified range along the well tracks and randomly divided the data into a training and test set attribute. The study established the integration and correlation of energy attribute, velocity fan attribute, and Q factor as relevant seismic attributes for porosity estimation when little or no well log is available, hence giving a means of spatially extending well data.