Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing
This work addresses efficiency improvements in hydraulic fracturing for the oil and gas industry, but it appears incremental as it applies existing ML methods to a specific domain without major breakthroughs.
The paper tackles the problem of forecasting production increase after hydraulic fracturing in oilfields by developing a data-driven model using machine learning, specifically gradient boosting, and compares it to expert-based predictions, though no concrete numerical results are provided.
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.