Summary

This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Our procedure is straight forward but does require careful quality control to insure reliable predictions from the seismic data. Network training, test and validation data sets provide calibration of seismic attributes with well log data, optimize the network parameters, and estimate the performance of the system to predict hidden representative data. Comprehensive statistical methods and interpretational/subjective measures insure that only attributes providing true relationships and a physical basis are used in the prediction of porosity from seismic attributes. The result, a 3-D volume of seismic derived porosity estimates for the Devonian Reservoir interval of the Pegasus Field, provide our reservoir development team with a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimum placement of horizontal wells and improved field development.

AAPG Search and Discovery Article #90937©1998 AAPG Annual Convention and Exhibition, Salt Lake City, Utah