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Promising Big Data technologies in petroleum engineering: the experience of the Gazprom Neft PJSC

UDK: 658.012.011.56:002
Key words: Big Data, cognitive technologies, predictive analysis, predictive analytics, machine learning
Authors: M.M. Khasanov, D.O/ Prokofiev, O.S. Ushmaev, B.V. Belozerov, R.R. Gilmanov, A.S. Margarit (Gazpromneft NTC LLC, RF, Saint-Petersburg)

According to conclusions of international consulting enterprise Bain & Company new analytical capabilities in big data will allow oil and gas industry to improve efficiency by 6-8%. Industry has already been dealing with huge amounts of information for decisions making for a long time. However, modern computational power and new algorithms for handling these data bring this process to a new level. When modern computers lack power to calculate through many iterations for solution of the petroleum engineering problems Big Data technologies come up to assist. Big Data applications already change the landmark of the industry.

In the age of information technology, followed by prompt growth of amount and diversity of processed data, a possibility arises for qualitative transition from quantity to quality of data. As the result vast amount of solutions and tools for structured and unstructured data were developed – Big Data technologies. The area has become one of key IT drivers and widespread in Western countries today. Progress in Big Data gave an impetus to introduction of modern gauge sensors collecting huge amount of production data.

Considerable attention is being paid to methods of processing and data mining in Gazprom Neft PJSC. The company has experience of small-scale solutions realization using Big Data technologies and initiated a set of projects aimed at overcoming petroleum engineering technological challenges by cognitive methods and tools.

References

1. Jacobs T., Automated drilling technologies showing promise, JPT, 2015,

V. 67, no. 6 (June), URL: http://www.spe.org/jpt/article/9010-automateddrilling-

technologies-showing-promise.

2. Rawi Z., Machinery predictive analytics, SPE 128559, 2010.

3. Stone P., Introducing predictive analytics: opportunities, SPE 106865, 2007.

4. How advanced analytics can drive productivity, URL: http://www.mckinsey.

com/business-functions/mckinsey-analytics/our-insights/how-advanced-

analytics-can-drive-productivity.

5. Big success with big data, URL: https://www.accenture.com/ieen/~/

media/Accenture/Conversion-Assets/DotCom/Documents/Global/

PDF/Digital_1/Accenture-Big-Data-POV.pdf.

According to conclusions of international consulting enterprise Bain & Company new analytical capabilities in big data will allow oil and gas industry to improve efficiency by 6-8%. Industry has already been dealing with huge amounts of information for decisions making for a long time. However, modern computational power and new algorithms for handling these data bring this process to a new level. When modern computers lack power to calculate through many iterations for solution of the petroleum engineering problems Big Data technologies come up to assist. Big Data applications already change the landmark of the industry.

In the age of information technology, followed by prompt growth of amount and diversity of processed data, a possibility arises for qualitative transition from quantity to quality of data. As the result vast amount of solutions and tools for structured and unstructured data were developed – Big Data technologies. The area has become one of key IT drivers and widespread in Western countries today. Progress in Big Data gave an impetus to introduction of modern gauge sensors collecting huge amount of production data.

Considerable attention is being paid to methods of processing and data mining in Gazprom Neft PJSC. The company has experience of small-scale solutions realization using Big Data technologies and initiated a set of projects aimed at overcoming petroleum engineering technological challenges by cognitive methods and tools.

References

1. Jacobs T., Automated drilling technologies showing promise, JPT, 2015,

V. 67, no. 6 (June), URL: http://www.spe.org/jpt/article/9010-automateddrilling-

technologies-showing-promise.

2. Rawi Z., Machinery predictive analytics, SPE 128559, 2010.

3. Stone P., Introducing predictive analytics: opportunities, SPE 106865, 2007.

4. How advanced analytics can drive productivity, URL: http://www.mckinsey.

com/business-functions/mckinsey-analytics/our-insights/how-advanced-

analytics-can-drive-productivity.

5. Big success with big data, URL: https://www.accenture.com/ieen/~/

media/Accenture/Conversion-Assets/DotCom/Documents/Global/

PDF/Digital_1/Accenture-Big-Data-POV.pdf.



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