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Software outlines for decisions making support in oil and gas engineering

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The aim of this study is to develop an architecture of enterprise solutions that allow real-time (or simulated) extraction, storage and analysis of parameterized data from high- resolution sensors to more accurately predict the potential course of technological processes in the industry and solving of related logistics tasks. Design/methodology/approach: The development of an integration architecture based on appropriate Web tools for viewing and collaborating on corporate information of the oil and gas industry will allow full operational decision-making on this basis, guided by the values of relevant controlled parameters and imposed on them and the process as a whole relevant constraints in general are the methodological grounds of the research from the theoretical and subject domain scope. The functionality of the artificial intelligence system should be reduced to sending signals to the controller in order to modify the controlled parameters through the appropriate instructions. At the theoretical level, measurement, interpretation and control will take place either on the surface, or on the bore, or in both places at the same time. Findings: There were explored software outlines for making possible the creation of the desired findings for new and better business processes and technological innovations in the domestic gas and oil industries based on intelligent information solutions. As proposed in this study, optimal flexibility and forward performance will only be achieved through the use of the cloud as a platform for tomorrow's technological challenges in the oil and gas industry. Originality/value: The newly developed focus on novel class of increasing domestic business efficiency will generally encourage oil and gas companies to develop their information architecture in the direction of knowledge-based systems and solutions, especially when controlling the drilling of oil and gas wells in terms of incomplete, inaccurate and poorly structured information from sensors.
Rocznik
Tom
Strony
81--99
Opis fizyczny
Bibliogr. 13 poz.
Twórcy
  • Ivano-Frankivsk National University of Oil and Gas
  • Ivano-Frankivsk National University of Oil and Gas
  • Ivano-Frankivsk National University of Oil and Gas
  • Ivano-Frankivsk National University of Oil and Gas
Bibliografia
  • 1. Andrew, S, Henderson, M, Irani, B, Parker, B, Sternesky, M. (2008). Digital Oil Field brought from concept to development in a year. World Oil, pp. 107-110.
  • 2. Baaziz, A., Quoniam, L. (2013). How to use big data technologies to optimize operations in upstream petroleum industry. International Journal of Innovation, September.
  • 3. Baaziz, A., Quoniam, L. (2013). The information for the operational risk management in uncertain environments: Case of Early Kick Detection while drilling of the oil or gas wells. International Journal of Innovation and Applied Studies (IJIAS), Vol. 4, No. 1, Sep.
  • 4. Baaziz, A., Quoniam, L., Vasilak, V. (2013). Enhancements Case of Early Kick Detection while drilling of the oil or gas wells. International Journal of Innovation and Applied Studies (IJIAS), Vol. 4, No. 1, Sep.
  • 5. Chesanovskyy, M., Sheketa, V., Yurchyshyn, V. et al. (2016). The Formal Structuring of Subject Domain for oil and Gas Industry IT Applications. Modern problems of radio engineering, telecommunications, and computer science (TCSET): the XIII International Theoretical and Practical Conference, (Lviv-Slavske, 23-26 february). Lviv, pp. 503-505.
  • 6. Ferguson, M. (2012). Architecting a big data platform for analytics. Intelligent Business Strategies, October.
  • 7. Guanghui, Xu, Feng, Xu, Hongxu, Ma (2012). Deploying and researching Hadoop in virtual machines. Automation and Lotgistics (ICAL) - Zhengzhou, China.
  • 8. Hems, A., Soofi, A. Prez, E. (2013). Drilling for New Business Value How innovative oil and gas companies are using big data to outmaneuver the competition. Microsoft, May.
  • 9. Hollingsworth, J. (2013). Big Data For Oil & Gas. Oracle Oil & Gas Industry Business Unit, March.
  • 10. Nicholson, R. (2012). Big Data in the Oil & Gas Industry. IDC Energy Insights, September.
  • 11. Rawat, N. (2014). Big Data Analytics in Oil & Gas Industry. International Journal of Scientific & Engineering Research, Vol. 5, Iss. 5, May.
  • 12. Romanyshyn, Y., Sheketa, V., Poteriailo, L., Pikh, V., Pasieka, N., Kalambet, Y. (2019). Social-Communication Web Technologies In The Higher Education As Means Of Knowledge Transfer. Proceedings of the IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). Vol. 3. 17-20 September. Lviv, Ukraine, pp. 35-39.
  • 13. Singh, S., Pandey, S., Shankar, R., Dumka, A. (2015) Application of Big Data Analytics to Optimizethe Operations in the Upstream Petroleum Industry. 2nd International Conference on Computing for Sustainable Global Development (indiacom) - New Delhi, India
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-090087a2-5fa5-4305-a318-75b5d103aab0
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