The distribution of the porosity-thickness (Σøh) of the Arab C4 Zone in the offshore El Bunduq field was estimated using neural network and geostatistical techniques. The Arab C4 Zone is approximately 10 to 15 meters thick which corresponds to a time window of less than 20 milliseconds. The reservoir is faulted and the reflection has a poor signal-to-noise ratio. The study utilizes 21 seismic attributes which were derived from a 3-D seismic survey calibrated from 42 wells, 27 of which are deviated. The attributes include different types of amplitudes, complex trace statistics, sequence statistics and frequencies. Three methods are compared: (I) simple kriging using only well data, (II) the neural network technique using 3-D seismic and well data, and (III) cokriging using the output of the neural network technique. Cross validation tests indicate that Method III is not consistently more precise than Method I. Also Method II, in cross-validation tests, demonstrated relatively large dispersion between data and estimates. It appears that although neural networks achieve good correlation between seismic attributes and reservoir properties, the physical relationship remains ambiguous.
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