Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing

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 of the jobs and a geological information. To predict an oil rate after the fracturing machine learning (ML) technique was applied. The ML-based prediction is compared 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.