Curtis A. Link, Debra L. Hanneman, Charles J. Wideman, J. W. Halvorson
Artificial neural networks (ANNs) are becoming increasingly popular as a method for parameter classification and as a tool for recognizing complex relationships in a variety of data types. The power of ANNs lies in their ability to 'learn' from a set of training data and then being able to 'generalize' to new data sets. In addition, ANNs are able to incorporate data over a large range of scales and are robust in the presence of noise.
A back propagation artificial neural network has proved to be a useful tool for predicting sequence boundaries from well logs in a Cenozoic basin. The network was trained using the following log set: neutron porosity, bulk density, pef, and interpreted paleosol horizons from a well in the Deer Lodge Valley, southwestern Montana After successful training, this network was applied to the same set of well logs from a nearby well minus the interpreted paleosol horizons. The trained neural network was able to produce reasonable predictions for paleosol sequence boundaries in the test well based on the previous training.
In an ongoing oil reservoir characterization project, a back propagation neural network is being used to produce estimates of porosity and permeability for subsequent input into a reservoir simulator. A combination of core, well log, geological, and 3-D seismic data serves as input to a back propagation network which outputs estimates of the spatial distribution of porosity and permeability away from the well.
AAPG Search and Discovery Article #90959©1995 AAPG Rocky Mountain Section Meeting, Reno, Nevada