Print this pageAbstract: Neural Network Interpretation of Well 
Log
Data
LIN ZHANG and MARY POULTON, University of Arizona, Dept. of Mining and Geological of Engineering, Tucson, AZ
Neural networks are computer models loosely based on the function
of biological neurons. As such, they find application in areas of pattern
recognition where humans perform very well and traditional computer models
perform poorly. One of the strengths of the neural network approach is
that it can perform non-parametric modeling very rapidly and with a high
degree of accuracy. In geophysics, neural networks have been used to invert
data, classify patterns, and combine data from multiple sensors. Our application
involves the use of neural networks to pick lithologic layers from wireline
log data, to invert shallow EM log data to produce a model of resistivity
and thickness, and to learn synthetic two-dimensional forward models of
electric logs.
Our first step in processing log data is to break the log into sections
based on lithologic layers. We have developed an adaptive layer picking
code using neural networks that can accurately detect layers in log data.
Next, we use a modular neural network architecture to invert data from
a shallow EM induction tool (Geonics EM39) commonly used for groundwater
studies. Conductivity information from the tool is converted to logl0 resistivity
and used as input to a neural network, which in turn produces a model of
resistivity and thickness for each section of the log. In another application,
we use the neural network to learn the forward model response of various
electrical logging tools. The trained networks can then be used in conjunction
with an inversion routine to greatly reduce the amount of time it takes
to perform a two-dimensional interpretation of log
data.
AAPG Search and Discovery Article #90931©1998 AAPG Foundation Grants-in-Aid