  In this paper, we present a data-driven model for forecasting the production
increase after hydraulic fracturing (HF). We use data from fracturing jobs
performed at one of the Siberian oilfields. The data includes features,
characterizing the jobs, and geological information. To predict an oil rate
after the fracturing machine learning (ML) technique was applied. We compared
the ML-based prediction to a prediction based on the experience of reservoir
and production engineers responsible for the HF-job planning. We discuss the
potential for further development of ML techniques for predicting changes in
oil rate after HF.
