Software & hardware platform for Digital Oilfield system organization

UDK: 681.518:622.276
Key words: digital oilfield, data mining, artificial neural networks, distributed calculations, multiagent system, software-hardware platform
Authors: I.S. Korovin, M.G. Tkachenko (Scientific Research Institute of Multiprocessor Computer Systems at Southern Federal University, RF, Taganrog)

Approach of building a software-hardware platform applied for solution of production tasks in the Digital Oilfield framework is considered in the paper. Nowadays a problem of development and implentation into industrial processes a united digital oilfield system assumes proceeding a sufficient amounts of work, linked with the integration of novel software & hardware tools into the existing infrastructure of an oil company. The suggested way of solving the given problem a priori demands huge financial investments and leads to unevitable stops in the work of the oil well fund, that is inadmissible in real industrial conditions.

In the paper we suggested an approach of building an intelligent oilfield system on the basis of autonomous software-hardware modules, phase-by-phase implemented into the existing corporate control systems. Also, we offered to apply data mining techniques, in particular, artificial neural networks, and also distributed calculations technology on the multiagent interaction for real time data handling procedures optimization. A prototype of a hardware-software module is presented.

References

1. Digital oilfield outlook report. Opportunities and challenges for Digital Oilfield

transformation, URL: https://www.accenture.com/t20151210T215032__w__/usen/_

acnmedia/PDF-2/Accenture-Digital-Oilfield-Outlook-JWN-October-

2015.pdf

2. The digital oilfield. Real time field management, URL [www.petex.com/includes/

download.php?id=43

3. Korovin Ya.S., Khisamutdinov M.V., Tkachenko M.G., Forecasting of oilfield

equipment work conditions with the application of evolutionary algorithms

and artificial neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry,

2013, no. 12, pp. 128-132.

4. Korovin Ya.S., Tkachenko M.G., Khisamutdinov M.V., Kalyaev A.I., Artificial intelligence

hybrid methods application in the task of the heavy oilfields profitability

increase (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 1,

pp. 106-109.

5. Korovin Ya.S., Decision support system for electrical submersible pumps

control on the neural network basis (In Russ.), Neftyanoe khozyaystvo = Oil Industry,

2007, no. 1, pp. 80-83.

6. Korovin Ya.S., Kononov S.V., Tkachenko M.G., Oilfield equipment's state diagnostics

on the basis of data mining technologies (In Russ.), Neftyanoe

khozyaystvo = Oil Industry, 2012, no. 9, pp. 116-118.

Approach of building a software-hardware platform applied for solution of production tasks in the Digital Oilfield framework is considered in the paper. Nowadays a problem of development and implentation into industrial processes a united digital oilfield system assumes proceeding a sufficient amounts of work, linked with the integration of novel software & hardware tools into the existing infrastructure of an oil company. The suggested way of solving the given problem a priori demands huge financial investments and leads to unevitable stops in the work of the oil well fund, that is inadmissible in real industrial conditions.

In the paper we suggested an approach of building an intelligent oilfield system on the basis of autonomous software-hardware modules, phase-by-phase implemented into the existing corporate control systems. Also, we offered to apply data mining techniques, in particular, artificial neural networks, and also distributed calculations technology on the multiagent interaction for real time data handling procedures optimization. A prototype of a hardware-software module is presented.

References

1. Digital oilfield outlook report. Opportunities and challenges for Digital Oilfield

transformation, URL: https://www.accenture.com/t20151210T215032__w__/usen/_

acnmedia/PDF-2/Accenture-Digital-Oilfield-Outlook-JWN-October-

2015.pdf

2. The digital oilfield. Real time field management, URL [www.petex.com/includes/

download.php?id=43

3. Korovin Ya.S., Khisamutdinov M.V., Tkachenko M.G., Forecasting of oilfield

equipment work conditions with the application of evolutionary algorithms

and artificial neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry,

2013, no. 12, pp. 128-132.

4. Korovin Ya.S., Tkachenko M.G., Khisamutdinov M.V., Kalyaev A.I., Artificial intelligence

hybrid methods application in the task of the heavy oilfields profitability

increase (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 1,

pp. 106-109.

5. Korovin Ya.S., Decision support system for electrical submersible pumps

control on the neural network basis (In Russ.), Neftyanoe khozyaystvo = Oil Industry,

2007, no. 1, pp. 80-83.

6. Korovin Ya.S., Kononov S.V., Tkachenko M.G., Oilfield equipment's state diagnostics

on the basis of data mining technologies (In Russ.), Neftyanoe

khozyaystvo = Oil Industry, 2012, no. 9, pp. 116-118.



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