Abstract
Increased difficulty of engineering problems require new approaches, which would combine automation and intellectual support in decision-making. Oil and gas engineering is a complex combination of interconnected areas from producing oil to supplying it to consumers. A key milestone in developing an oil and gas asset is conceptual engineering stage, which includes selecting equipment that has optimal characteristics to meet requirements and operating conditions, which ultimately defines capital investments in the overall cost structure. The article presents an algorithm for automatic configuration of oil and gas equipment, which uses a case-based approach and an ontology model that can support decision-making in field development conceptual design. Using the Protégé ontology editor, the authors created an ontology of standard infrastructure solutions based on oil and gas assets’ data, which includes hierarchies of classes and instances. The list of pad facilities formed using the SPARQL Query language has proved efficient for automated search of necessary information when solving engineering problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Muller, G., Falk K.: What can (Systems of) systems engineering contribute to oil and gas? An illustration with case studies from Subsea. In: Proceedings of the 13th IEEE Annual Conference on System of Systems Engineering (SoSE), pp. 629–635. IEEE, New York (2018)
Engen, S., Falk, K., Muller, G.: The need for systems awareness to support early-phase decision-making—A study from the norwegian energy industry. Systems 9(3) (2021)
Gonzalez, J.: Cost-cutting as an innovation driver among suppliers during an industry downturn. In: Thune, T., Engen, O., Wicken, O. (eds.) Petroleum Industry Transformations, pp. 70–83. Routledge, New York (2018)
Yakovlev, V., Khasanov, M., Sitnikov, A., Simonov, M., Perets, D.: Napravleniya razvitiya kognitivny`kh tekhnologij v perimetre Bloka razvedki i doby`chi kompanii «Gazprom neft`». Neftyanoe Khozyaystvo 12, 6–9 (2017). (in Russian)
Shushakov, A., Bilinchuk, A., Pavlechko, N., et al.: «ERA:Dobycha» – integrirovannaya platforma dlya povysheniya effektivnosti ekspluatacii mekhanizirovannogo fonda skvazhin. Neftyanoe Khozyaystvo 12, 60–63 (2017). (in Russian)
Khasanov, M., Prokofiev, D., Ushmaev, O., Gilmanov, R., Margarit, A.: Perspektivnye tekhnologii Big Data v neftyanom inzhiniringe: opyt kompanii «Gazprom neft’». Neftyanoe Khozyaystvo 12, 76–79 (2016). (in Russian)
Hagedorn, T., Bone, M., Kruse, B., Grosse, I., Blackburn, M.: Knowledge representation with ontologies and semantic web technologies to promote augmented and artificial intelligence in systems engineering. Insight 23(1), 15–20 (2020)
Altamiranda, E., Colina, E.: A system of systems engineering approach for intelligent control and supervision of subsea production systems. In: OCEANS 2019-Marseille, pp. 1–9. IEEE (2019)
Koroteev, D., Tekic, Z. Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI 3 (2021)
Solanki, P., Baldaniya, D., Jogani, D., Chaudhary, B., Shah, M., Kshirsagar, A.: Artificial intelligence: new age of transformation in petroleum upstream. Pet. Res. 7(1), 106–114 (2021)
Baclawski, K., Bennett, M., Berg-Cross, G.: Ontology summit 2020 communiqué: knowledge graphs. Appl. Ontol. 16(2), 229–247 (2021)
Schäffer, E., Shafiee, S., Mayr, A., Franke, J.: A strategic approach to improve the development of use-oriented knowledge-based engineering configurators (KBEC). Procedia CIRP 96, 219–224 (2021)
Single, J., Schmidt, J., Denecke, J.: Ontology-based computer aid for the automation of HAZOP studies. J. Loss Prevent. Process Ind. 68 (2020)
Skalle, P., Aamodt, A.: Petrol 18 946: downhole failures revealed through ontology engineering. J. Pet. Sci. Eng. 191 (2020)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
López De Mántaras, R., Mcsherry, D., Bridge, D., Leake, D., Smyth, B., et al.: Retrieval, reuse, revision, and retention in case-based reasoning. Knowl. Eng. 20(3), 215–240 (2005)
Wei, L., Du, H., Mahesar, Q., Ammari, K., Magee, D., et al.: A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning. Expert Syst. Appl. 158 (2020)
Bouhana, A., Amir, Z., Fekih, A., Chabchoub, H., Abed, M.: An ontology-based CBR approach for personalized itinerary search systems for sustainable urban freight transport. Expert Syst. Appl. 42(7), 3724–3741 (2015)
Avdeenko, T., Makarova, E.: Knowledge representation model based on case-based reasoning and the domain ontology: application to the IT consultation. IFAC-PapersOnLine 51(11), 1218–1223 (2018)
Mabkhot, M, Al-Samhan, A, Hidri, L.: An ontology-enabled case-based reasoning decision support system for manufacturing process selection. Adv. Mater. Sci. Eng. 1, 1–18 (2019)
Glukhikh, I., Glukhikh, D.: Case based reasoning for managing urban infrastructure complex technological objects. In: Proceedings of the CEUR Workshop 2021. CEUR Workshop Proceedings, Germany (2021)
Dohyun, K., Jeong, D., Seo,Y.: Intelligent design for simulation models of weapon systems using a mathematical structure and case-based reasoning. Appl. Sci. 10(21) (2020)
Gaub, H.: Customization of mass-produced parts by combining injection molding and additive manufacturing with Industry 4.0 technologies. Reinf. Plast. 60, 401–404 (2016)
William, B. AI as systems engineering augmented intelligence for systems engineers. Incose Insight—March 2020: AI & Systems Engineering 23(1), 52–54 (2020)
Azad, M.: Exploiting augmented intelligence in systems engineering and engineered systems. Incose Insight—March 2020: AI & Systems Engineering 23(1), 31–36 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Glukhikh, I., Pisarev, M., Liss, D., Shestakova, A., Nonieva, K. (2023). Case-Based Reasoning and Ontology-Based Approach to Selecting Equipment Solutions in Oilfield Engineering. In: Dolinina, O., et al. Artificial Intelligence in Models, Methods and Applications. AIES 2022. Studies in Systems, Decision and Control, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-22938-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-22938-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22937-4
Online ISBN: 978-3-031-22938-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)