In hydraulic fracturing
operations, small rounded particles called
proppants are mixed and injected with fracture fluids into the targeted
formation. The proppant particles hold the fracture open against formation
closure stresses, providing a conduit for the reservoir fluid flow.
The fracture’s capacity to transport fluids is called fracture
conductivity and is the product of proppant permeability and fracture
width. Prediction of the propped fracture conductivity is essential
for fracture design optimization. Several theoretical and few empirical
models have been developed in the literature to estimate fracture
conductivity, but these models either suffer from complexity, making
them impractical, or accuracy due to data limitations. In this research,
and for the first time, a machine learning approach was used to generate
simple and accurate propped fracture conductivity correlations in
unconventional gas shale formations. Around 350 consistent data points
were collected from experiments on several important shale formations,
namely, Marcellus, Barnett, Fayetteville, and Eagle Ford. Several
machine learning models were utilized in this research, such as artificial
neural network (ANN), fuzzy logic, and functional network. The ANN
model provided the highest accuracy in fracture conductivity estimation
with R
2 of 0.89 and 0.93 for training
and testing data sets, respectively. We observed that a higher accuracy
could be achieved by creating a correlation specific for each shale
formation individually. Easily obtained input parameters were used
to predict the fracture conductivity, namely, fracture orientation,
closure stress, proppant mesh size, proppant load, static Young’s
modulus, static Poisson’s ratio, and brittleness index. Exploratory
data analysis showed that the features above are important where the
closure stress is the most significant.