Gradient boosting to boost the efficiency of hydraulic fracturing

Journal of Petroleum Exploration and Production Technology

Dmitry Koroteev¹  Evgeny Burnaev¹  Ivan Makhotin¹ 

[1] Skolkovo Institute of Science and Technology;

关键词: Hydraulic fracturing;    Machine learning;    Decision trees;    Gradient boosting;   

DOI  :  10.1007/s13202-019-0636-7

来源: DOAJ

【 摘 要 】

Abstract 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 have 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.

【 授权许可】

Unknown