Application of Neural Networks and Cokriging for Predicting Reservoir Porosity-Thickness (Σøh)

Abstract

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.

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

“…Literature study reveals that machine learning algorithms exhibit superior performance in terms of high accuracy and low error value compared to conventional statistical methods (Nikravesh and Aminzadeh, 2001). Some of the important articles with special emphasis on RC using 3D seismic data include Boadu, (1997) ;Nikravesh, (1998a,b); Nikravesh et al, (1998); Chawathe et al, (1997); Yoshioka et al, (1996); Schuelke et al, (1997); Monson and Pita, (1997); Aminzadeh and Chatterjee, (1985); Finol and Jing (2002); Hou et al, (2008); Kadkhodaie-Ilkhchi et al, (2009); Al-Dousari and Garrouch (2013); Na 'imi, et al, (2014); Iturrarán-Viveros and Parra (2014).…”