  The photoelectric factor (PEF) is an important well logging tool to
distinguish between different types of reservoir rocks because PEF measurement
is sensitive to elements with high atomic number. Furthermore, the ratio of
rock minerals could be determined by combining PEF log with other well logs.
However, PEF log could be missing in some cases such as in old well logs and
wells drilled with barite-based mud. Therefore, developing models for
estimating missing PEF log is essential in those circumstances. In this work,
we developed various machine learning models to predict PEF values using the
following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI),
gamma ray (GR), compressional and shear velocity.
  The predictions of PEF values using adaptive-network-fuzzy inference system
(ANFIS) and artificial neural network (ANN) models have errors of about 16% and
14% average absolute percentage error (AAPE) in the testing dataset,
respectively. Thus, a different approach was proposed that is based on the
concept of automated machine learning. It works by automatically searching for
the optimal model type and optimizes its hyperparameters for the dataset under
investigation. This approach selected a Gaussian process regression (GPR) model
for accurate estimation of PEF values. The developed GPR model decreases the
AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This
error could be further decreased to about 2% by modeling the potential noise in
the measurements using the GPR model.
