Application of Neural Networks in Determining Petrophysical Properties from Seismic Survey
The history of reservoir characterization has shown that considerable efforts have been devoted to establishing knowledge over inter-well correlation of reservoir rock petrophysical properties. Recent developments in seismic technology have attracted our attention back to the possibility of using seismic attributes for determining reservoir rock properties in general. This is to deviate from the traditional use of seismic data merely as support in describing reservoir structures. Recent research and investigations have produced some encouraging progresses.
Recently, a technique for modeling correlation between potentially usable seismic attributes such as P-wave velocity, acoustic impedance, and Poisson ratio, on one side, and petrophysical properties such as porosity and water saturation, on the other side, using data from well-logs, core, and well production tests has been proposed. The plan to apply the technique on real seismic data from a limestone reservoir in Java had revealed potential error that may result in the estimation of the petrophysical properties. The principal problem encountered was in the form of data absence, both from well-log and seismic, which could end up the application of the model in failure. A consideration over the matter had prompted the attention toward soft computing as an approach to minimize the potential error.
Soft computing, particularly neural networks, as a pattern-recognition approach suits well with the task of minimizing the potential error mentioned above. The approach has proved itself useful in various stages of the work especially in providing data otherwise difficult to obtain reliably. The support ranges from assistance in estimating missing log data to generation of Poisson ratio maps required as a support to the acoustic impedance map provided by seismic processing. Use of the estimated data was justified by validation at wells. By applying the technique previously mentioned, with additional support by data from geological and engineering analyses, maps of porosity and water saturation have been produced for the reservoir. Application of neural networks has proved that problems in seismic-based reservoir characterization can be reduced significantly.
KeywordsWater Saturation Poisson Ratio Acoustic Impedance Reservoir Rock Seismic Survey
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