Gradient boosting to boost the efficiency of hydraulic fracturing

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...
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Autor*in:	Makhotin, Ivan [verfasserIn] Koroteev, Dmitry Burnaev, Evgeny

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Hydraulic fracturing Machine learning Decision trees Gradient boosting

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Journal of petroleum exploration and production technology - Berlin : Springer, 2011, 9(2019), 3 vom: 04. März, Seite 1919-1925
Übergeordnetes Werk:	volume:9 ; year:2019 ; number:3 ; day:04 ; month:03 ; pages:1919-1925

Links:	Volltext

DOI / URN:	10.1007/s13202-019-0636-7

Katalog-ID:	SPR031515932

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spelling	10.1007/s13202-019-0636-7 doi (DE-627)SPR031515932 (SPR)s13202-019-0636-7-e DE-627 ger DE-627 rakwb eng Makhotin, Ivan verfasserin aut Gradient boosting to boost the efficiency of hydraulic fracturing 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Hydraulic fracturing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Decision trees (dpeaa)DE-He213 Gradient boosting (dpeaa)DE-He213 Koroteev, Dmitry aut Burnaev, Evgeny aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 9(2019), 3 vom: 04. März, Seite 1919-1925 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:9 year:2019 number:3 day:04 month:03 pages:1919-1925 https://dx.doi.org/10.1007/s13202-019-0636-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 3 04 03 1919-1925
allfields_unstemmed	10.1007/s13202-019-0636-7 doi (DE-627)SPR031515932 (SPR)s13202-019-0636-7-e DE-627 ger DE-627 rakwb eng Makhotin, Ivan verfasserin aut Gradient boosting to boost the efficiency of hydraulic fracturing 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Hydraulic fracturing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Decision trees (dpeaa)DE-He213 Gradient boosting (dpeaa)DE-He213 Koroteev, Dmitry aut Burnaev, Evgeny aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 9(2019), 3 vom: 04. März, Seite 1919-1925 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:9 year:2019 number:3 day:04 month:03 pages:1919-1925 https://dx.doi.org/10.1007/s13202-019-0636-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 3 04 03 1919-1925
allfieldsGer	10.1007/s13202-019-0636-7 doi (DE-627)SPR031515932 (SPR)s13202-019-0636-7-e DE-627 ger DE-627 rakwb eng Makhotin, Ivan verfasserin aut Gradient boosting to boost the efficiency of hydraulic fracturing 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Hydraulic fracturing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Decision trees (dpeaa)DE-He213 Gradient boosting (dpeaa)DE-He213 Koroteev, Dmitry aut Burnaev, Evgeny aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 9(2019), 3 vom: 04. März, Seite 1919-1925 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:9 year:2019 number:3 day:04 month:03 pages:1919-1925 https://dx.doi.org/10.1007/s13202-019-0636-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 3 04 03 1919-1925
allfieldsSound	10.1007/s13202-019-0636-7 doi (DE-627)SPR031515932 (SPR)s13202-019-0636-7-e DE-627 ger DE-627 rakwb eng Makhotin, Ivan verfasserin aut Gradient boosting to boost the efficiency of hydraulic fracturing 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Hydraulic fracturing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Decision trees (dpeaa)DE-He213 Gradient boosting (dpeaa)DE-He213 Koroteev, Dmitry aut Burnaev, Evgeny aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 9(2019), 3 vom: 04. März, Seite 1919-1925 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:9 year:2019 number:3 day:04 month:03 pages:1919-1925 https://dx.doi.org/10.1007/s13202-019-0636-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 3 04 03 1919-1925
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author	Makhotin, Ivan
spellingShingle	Makhotin, Ivan misc Hydraulic fracturing misc Machine learning misc Decision trees misc Gradient boosting Gradient boosting to boost the efficiency of hydraulic fracturing
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topic_title	Gradient boosting to boost the efficiency of hydraulic fracturing Hydraulic fracturing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Decision trees (dpeaa)DE-He213 Gradient boosting (dpeaa)DE-He213
topic	misc Hydraulic fracturing misc Machine learning misc Decision trees misc Gradient boosting
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title	Gradient boosting to boost the efficiency of hydraulic fracturing
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title_full	Gradient boosting to boost the efficiency of hydraulic fracturing
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author_browse	Makhotin, Ivan Koroteev, Dmitry Burnaev, Evgeny
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doi_str_mv	10.1007/s13202-019-0636-7
title_sort	gradient boosting to boost the efficiency of hydraulic fracturing
title_auth	Gradient boosting to boost the efficiency of hydraulic fracturing
abstract	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. © The Author(s) 2019
abstractGer	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. © The Author(s) 2019
abstract_unstemmed	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. © The Author(s) 2019
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title_short	Gradient boosting to boost the efficiency of hydraulic fracturing
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