The used metering technique in this study is based on the dual energy (Am-241 and
Cs-137) gamma ray attenuation. Two transmitted NaI detectors in the best orientation
were used and four features were extracted and applied to the model. This
paper highlights the application of Adaptive Neuro-fuzzy Inference System (ANFIS)
for identifying ow regimes and predicting volume fractions in gas-oil-water multiphase
systems. In fact, the aim of the current study is to recognize the ow regimes based on
dual energy broad-beam gamma-ray attenuation technique using ANFIS. In this study,
ANFIS is used to classify the ow regimes (annular, stratied, and homogenous) and
predict the value of volume fractions. To start modeling, sucient data are gathered.
Here, data are generated numerically using MCNPX code. In the next step, ANFIS
must be trained. According to the modeling results, the proposed ANFIS can correctly
recognize all the three dierent ow regimes, and other ANFIS networks can determine
volume fractions with MRE of less than 2% according to the recognized regime, which
shows that ANFIS can predict the results precisely.