is widely used to increase oil well production and to reduce formation damage.
Reservoir studies and engineering analyses are carried out to select the wells
for this kind of operation. As the reservoir parameters have some diffuse
characteristics, Fuzzy Inference Systems (FIS) have been tested for these
selection processes in the last few years. This paper compares the performance
of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for
hydraulic fracturing, with knowledge acquired from an operational data base
to set the SIF membership functions. The training data and the validation
data used were the same for both systems. We concluded that, despite the
genetic fuzzy system being a newer process, it obtained better results than
the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy
system can work with constraints, the membership functions setting kept the
consistency of variable linguistic values.
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