Decision making for modeling of oil and gas fields by using case based reasoning

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


Release:

2019, Vol. 5. №3

Title: 
Decision making for modeling of oil and gas fields by using case based reasoning


For citation: Glukhikh I. N., Nikiforov D. V. 2019. “Decision making for modeling of oil and gas fields by using case based reasoning”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 5, no 3, pp. 147-163. DOI: 10.21684/2411-7978-2019-5-3-147-163

About the authors:

Igor N. Glukhikh, Dr. Sci. (Tech.), Professor, Head of Information Technology Department, University of Tyumen; eLibrary AuthorID, ORCID, Scopus AuthorID, igluhih@utmn.ru

Dmitry V. Nikiforov, Postgraduate Student, Information Technology Department, University of Tyumen; ORCID, dimonnikiforov@hotmail.com

Abstract:

This article describes a case-based reasoning (CBR) method as a decision-making tool in modeling oil and gas fields, its existing and potential application in the industry. The main direction for the application of the CBR method is the search for analogous objects for the design of oil and gas field development. The current engineering practice involves a vaguely formalized method of analogies, which does not allow defining the object as much as possible authentically the analogue that does not cause errors. The analogue object serves not only as a source of ready-made near optimal design solutions, but also as additional information about the object of development and key decisions in modeling hydrocarbon fields.

This paper considers the CBR method as the main tool for finding analogue objects, the main methods of extracting precedents from the database, and gives an idea of the object of development as a precedent. Proceeding from the peculiarities of presenting the object of development as a precedent and the peculiarities of applying the methods of extracting precedents, the authors have developed the concept of searching for analogue objects. In its implementation, it will allow for a different degree of information content of precedents stored in the database and will accelerate the procedure of extracting precedents from the database. The principal novelty is that the presented conceptual scheme allows using the methods of extracting precedents in the conditions of insufficient input data, which is important for the design of oil and gas fields.

References:

  1. Alekseeva A. A., Taranic M. A. 2016 “Mathematical methods of intelligent data analysis and output on the case based reasoning”. Proceedings of the 3rd International Research Conference “Information technology in science, management, social sphere and medicine” in 2 vols. Vol. 1, pp. 636-639. Tomsk. [In Russian]
  2. Bashlikov A. A. 2016. “Application of case theory methods in decision making system for pipeline system management”. Automation, Telemechanization and Communication in Oil Industry, no 1, pp. 23-32. [In Russian]
  3. Varshavskiy P. R., Eremeev A. P. 2009. “Modelling of decision making based on case based reasoning in intelligence system of decision making maintenance”. Artificial Intelligence and Decision Making, no 2, pp. 45-57. [In Russian]
  4. Glukhikh I. N. 2006. “Knowledge application and reasoning in situational knowledge base”. Tyumen State University Herald, no 5, pp. 265-270. [In Russian]
  5. Strategy. “Recover must not leave”. Accessed 24 June 2019. http://strategyjournal.ru/articles/izvlech-nelzya-ostavit/ [In Russian]
  6. Klikov U. N. 1970. “Situational models big system management”. Isvestia Akademii nauk USSR. Tekhnicheskaya kybernetika, no 6, pp. 17-25. [In Russian]
  7. Kuzyakov O. N., Glukhikh I. N., Gapanovich I. V. 2019. “Intelligent tracking of oil pipeline status with case-based reasoning”. Automation, Telemechanization and Communication in Oil Industry, no 3, pp. 31-36. DOI: 10.33285/0132-2222-2019-3(548)-31-36 [In Russian]
  8. Pospelov D. A. 1981. Logical-Linguistic Model in Management Systems. Moscow: Energoatomizdat. [In Russian]
  9. Pospelov D. A. 1986. Situational Management. Theory and Practice] Moscow: Nauka. [In Russian]
  10. Dursun S., Temizel C. 2013. “Efficient use of methods, attributes, and case-based reasoning algorithms in reservoir analogue techniques in field development”. SPE Digital Energy Conference (5-7 March, Woodlands, Texas, USA). SPE-163700-MS. DOI: 10.2118/163700-MS
  11. Glukhikh I. N., Piankov V. N., Zabolotnov A. R. 2002. “Situational models in corporative knowledge base on geological-technical measures’ know-how”. Neftyanoe khozyaystvo — Oil Industry, vol. 6, p. 45.
  12. Hodgin J. E., Harrell D. R. 2006. “The selection, application, and misapplication of reservoir analogs for the estimation of petroleum reserves”. SPE Annual Technical Conference and Exhibition (24-27 September, San Antonio, Texas, USA). SPE-102505-MS. DOI: 10.2118/102505-MS
  13. Irrgang R., Damski C., Kravis S., Maidla E., Millheim K. 1999. “A case-based system to cut drilling costs “. SPE Annual Technical Conference and Exhibition (3-6 October, Houston, Texas). SPE-56504-MS. DOI: 10.2118/56504-MS
  14. Kolodner J. L. 1992. “An Introduction to case-based reasoning”. Artificial Intelligence Review, no 6, pp. 3-34. DOI: 10.1007/BF00155578
  15. Kravis S. I. 2005. “A case based system for oil and gas well design with risk assessment”. Applied Intelligence, vol. 23, no 1, pp. 39-53. DOI: 10.1007/s10489-005-2371-7
  16. Kuzyakov O. N., Glukhikh I. N., Andreeva M. A. 2018. “Case-based reasoning approach for automating control of gas-compressor unit within gas-compressor station”. IOP Conference Series: Journal of Physics, vol. 1059, art. 012023. DOI: 10.1088/1742-6596/1059/1/012023
  17. Mendes J. R. P., Guilherme I. R., Morooka C. K. 2001. “Case-based system: indexing and retrieval with fuzzy hypercube”. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference. Vancouver. DOI: 10.1109/NAFIPS.2001.944709
  18. Perry P. B., Curry D. A., Kerridge J. D., Lawton J., Bowden D., Flett A. N. 2004. “A case based knowledge repository for drilling optimization”. IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition (13-15 September, Kuala Lumpur, Malaysia). SPE-87994-MS. DOI: 10.2118/87994-MS
  19. Sidle R., Lee W. J. 2010. “An update on the use of reservoir analogs for the estimation of oil and gas reserves”. SPE Economics & Management, vol. 2, no 2, pp. 80-85. DOI: 10.2118/129688-PA
  20. Skalle P., Aamodt A., Sveen J. 1998. “Case-based reasoning, a method for gaining experience and giving advise on how to avoid and how to free stuck drill strings”. IADC Middle East Drilling Conference. Dubai.