Gradient boosting to boost the efficiency of hydraulic fracturing
Open accessAbstract
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.
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Pioneering efforts on predicting the efficiency of hydraulic fracturing jobs and ML-based analysis of injectivity issues have already been made.
“Pioneering efforts on predicting the efficiency of hydraulic fracturing jobs [40] and ML-based analysis of injectivity issues [41] have already been made.”
From Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future (2021) · cites this paper as [40]
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Research works printed but not linked: 4
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Cited by
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Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future
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Self-citation
- Authors
- Makhotin, I., Koroteev, D., & Burnaev, E.
- Affiliations
- Skolkovo Institute of Science and Technology (RU)
- Corresponding author
- Makhotin, Ivan
- Koroteev, Dmitry
- Burnaev, Evgeny
- Venue
- Journal of Petroleum Exploration and Production Technology
- Published
- 2019-03-04
- Volume
- 9
- Issue
- 3
- Pages
- 1919-1925
- ISSN
- 2190-0558 (ISSN-L)
- 2190-0566
- OpenAlex
- W2911390555
- Cited by
- 40
- References
- 15
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2022: 11 · 2021: 8 · 2020: 9 · 2019: 2
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Petroleum Exploration and Production Technology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Hydraulic Fracturing and Reservoir Analysis
- Physical Sciences › Engineering › Ocean Engineering › Reservoir Engineering and Simulation Methods
- Physical Sciences › Engineering › Ocean Engineering › Oil and Gas Production Techniques
- Keywords
- hydraulic fracturing, production forecasting, gradient boosting, machine learning, data-driven modeling, oil field production
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