Publications

The development of carbonate reservoir characterization has evolved rapidly. One new method that is currently in use is Common Contour Binning (CCB). CCB is new technology who introduced by using the concept of amplitude stacking of seismic 3D post stack data. CCB is used to detect subtle hydrocarbon-related seismic anomalies and to pinpoint gas-water, gas-oil and oil-water contacts. The method enhances an amplitude anomaly to detect the oil water contact and compares it with a back propagation neural network to determine the density distribution. The density distribution from the back propagation neural network is used to support the results of common contour binning. In this paper, we assume that the oil water contact is located at the interface of two contrasting density layers. In this case, the hydrocarbon trapping mechanism is a fault trap. Initially, the fault was identified using a fault enhancement filter, other seismic attributes were used to estimate the probability of an oil water contact in the reservoir. Common contour binning was then applied. The input data for the back propagation neural network process are seismic attributes that have a good linear correlation with the petrophysical data from the target, in this case density data from five wells. The procedure indicates a combination of the two methods yields good oil water contact predictions from 3D post stack seismic data.