A Data Management Framework in the Upstream Sector of the Oil and Gas Industry

Document Type : Review Article

Authors

1 PhD Student of Information Technology Management, Faculty of Technology and Industrial Management, Tehran University, Tehran, Iran

2 Associate Professor of Information Technology Management, Faculty of Technology and Industrial Management, Tehran University, Tehran, Iran

3 Associate Professor of Information Technology Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

4 Professor of Information Technology Management, Faculty of Technology and Industrial Management, Tehran University, Tehran, Iran

Abstract

In recent years, major oil companies around the world have accelerated their digital transformation journey by embracing data as a strategic asset and developing digital oil fields. The emergence of big data, alongside the siloed operations of various disciplines within the upstream oil and gas value chain, has led to numerous challenges in data management for data-driven decision-making and value creation. Consequently, the need for a robust data management framework at the industry level is evident. This article aims to address the gap caused by the lack of a comprehensive perspective on data management to guide professionals and researchers in the exploration and production Industry. Using the meta-synthesis method, along with a systematic review of previous studies and the integration of existing literature in this industry, data management is examined from various aspects, distinguishing it from data governance. After identifying 10 key components and 47 sub-components, a four-layer conceptual framework for data management is proposed, including: Data Infrastructure Layer, Data Architecture and Integration Layer, Data Analytics and Services Layer and Data Management Objectives Layer. The elements of this framework can assist the petroleum industry in assessing data-driven maturity, defining data management projects, and implementing AI-based governance.

Keywords

References

  1. Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424–438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008
  2. Akoum, M., & Hazzaa, H. B. (2019, October 21). A data governance framework - The foundation for data management excellence. Society of Petroleum Engineers - SPE Gas and Oil Technology Showcase and Conference 2019, GOTS 2019. https://doi.org/10.2118/198593-ms
  3. Allied Market Research. (2018). Role of Data Management In Oil and Gas Sector. https://blog.alliedmarketresearch.com/role-of-data-management-in-oil-and-gas-sector-360
  4. Almadani, B. (2015). Drilling data management in petroleum industry based on RTPS. Procedia Computer Science, 56(1), 325–332. https://doi.org/10.1016/j.procs.2015.07.215
  5. Almadani, B. (2016). QoS-aware real-time pub/sub middleware for drilling data management in petroleum industry. Journal of Ambient Intelligence and Humanized Computing, 7(2), 287–299. https://doi.org/10.1007/s12652-015-0332-5
  6. AlSuwaidan, L. (2021). The role of data management in the Industrial Internet of Things. Concurrency and Computation: Practice and Experience, 33(23). https://doi.org/10.1002/cpe.6031
  7. Alzahrani, M. A., Contreras Otalvora, W. B., Alotaibi, B. M., & Aman, B. (2022). The Challenges with Drilling Real-Time Data and Proposed Data Management Framework. ADIPEC.
  8. Blosser, D., & Haines, P. (2013). Data governance at Chevron GOM: a case study. PNEC 17th International Conference on Petroleum Data Integration, Data and Information Management, May.
  9. Carvajal, G., Maucec, M., Cullick, S., & Carvajal, G. (2017). Intelligent digital oil and gas fields: Concepts, collaboration, and right-time decisions. In Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-Time Decisions. https://www.elsevier.com/books/intelligent-digital-oil-and-gas-fields/carvajal/978-0-12-804642-5
  10. Collia, D. V, & Moreau, R. L. (2020). SafeOCS Industry Safety Data Program: An Industrywide Safety Data Management Framework. Journal of Petroleum Technology, 72(12), 34–37.
  11. DAMA-DMBOK. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition). Technics Publications, LLC.
  12. David, R. M., Saputelli, L., Hafez, H., Narayanan, R., Colombani, P., & Al Naqbi, T. (2017). Upstream data architecture and data governance framework for efficient integrated upstream workflows and operations. Society of Petroleum Engineers - SPE Abu Dhabi International Petroleum Exhibition and Conference 2017, 2017-Janua. https://doi.org/10.2118/188962-ms
  13. Geekiyanage, S. C. H., Sui, D., & Aadnoy, B. S. (2018). Drilling data quality management: Case study with a laboratory scale drilling rig. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 8. https://doi.org/10.1115/OMAE2018-77510
  14. Geekiyanage, S. C. H., Tunkiel, A., & Sui, D. (2020). Drilling data quality improvement and information extraction with case studies. Journal of Petroleum Exploration and Production Technology. https://doi.org/10.1007/s13202-020-01024-x
  15. Gharieb, A., Gabry, M. A., Elsawy, M., Edries, T., Mahmoud, W., Algarhy, A., & Darraj, N. (2024). In-House Integrated Big Data Management Platform for Exploration and Production Operations Digitalization: From Data Gathering to Generative AI through Machine Learning Implementation Using Cost-Effective Open-Source Technologies - Experienced Mature Work. In Society of Petroleum Engineers - SPE Conference at Oman Petroleum and Energy Show, OPES 2024 (p. D011S011R004). https://doi.org/10.2118/218560-MS
  16. Guizhi, M., Zhanmin, Z., Xuefeng, J., Yu, W., Xizhi, Y., Peng, G., Lihong, D., Zanmei, W., Xiaoxia, N., Fujun, T., Zhaojing, Z., & Houbing, W. (2017). Application of big data analysis in oil production engineering. 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017, 447–451. https://doi.org/10.1109/ICBDA.2017.8078859
  17. Gupta, U. G., & Cannon, S. (2020). A Review of Data Governance Definitions and Emerging Perspectives. International Journal of Data Analytics, 1(2), 30–47. https://doi.org/10.4018/ijda.2020070103
  18. Hawtin, S. (2010). Applying DAMA to Oil Industry Data. Schlumberger Information Solutions, 14th Petroleum Data Integration, Information & Data Management Conference.
  19. Holdaway Keith R. (2014). Harness Oil and Gas Big Data with Analytics. In Harness Oil and Gas Big Data with Analytics. https://doi.org/10.1002/9781118910948
  20. Huff, E., & Lee, J. (2020). Data as a strategic asset: Improving results through a systematic data governance framework. SPE Latin American and Caribbean Petroleum Engineering Conference Proceedings, 2020-July. https://doi.org/10.2118/198950-ms
  21. Isbell, M., Neal, J., Copeland, H., Foster, N., & Patrick, S. (2022). Maximizing the Value of Downhole Drilling Data: A Novel Approach to Digital Drilling Data Management and Analytics. In SPE - International Association of Drilling Contractors Drilling Conference Proceedings (Vols. 2022-March, p. D021S017R002). https://doi.org/10.2118/208710-MS
  22. Kang, J., Al Masry, Z., Varnier, C., Mosallam, A., & Zerhouni, N. (2023). Data Management Framework for Risk Estimate of Electronic Boards in Drilling and Measurement Tools. IFAC-PapersOnLine, 56(2), 11936–11941. https://doi.org/10.1016/j.ifacol.2023.10.609
  23. Li, Y., Wei, B., & Wang, X. (2017). A web-based visual and analytical geographical information system for oil and gas data. ISPRS International Journal of Geo-Information, 6(3). https://doi.org/10.3390/ijgi6030076
  24. Nguyen, T., Gosine, R. G., & Warrian, P. (2020). A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0. In IEEE Access (Vol. 8, pp. 61183–61201). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.2979678
  25. Nimmagadda, S. L., Mani, N., Reiners, T., & Wood, L. C. (2021). Big Data Guided Unconventional Digital Reservoir Energy Ecosystem and its Knowledge Management. Pacific Asia Journal of the Association for Information Systems, 13(1), 1–35. https://doi.org/10.17705/1pais.13101
  26. Parkinson, J. (2016). Corporate Governance and Data Governance. Are you really In Control? https://www.linkedin.com/pulse/corporate-governance-data-you-really-control-john-parkinson
  27. Permana, A., Sulaksono, A., Hikmah, N., Dwiyono, I., Pratama, R., Gustian, Y., Suseno, P., & Parsaulian, S. (2022). Pioneering Subsurface Data Management Studio and Asset Retirement Obligation Retrenchment in Upstream South Sumatra Region, Indonesia. 4, 2404–2408. https://doi.org/10.2118/210045-MS
  28. Purohit, P., Al Nuaimi, F., & Nakkolakkal, S. (2024). Data Governance, Privacy, Data Sharing Challenges. Society of Petroleum Engineers - GOTECH Conference 2024, Day 2 Wed, D021S031R001. https://doi.org/10.2118/219172-MS
  29. Sanasi, C., Dal Forno, L., Maccarini, G. R., Mutidieri, L., Tempone, P., Mezzapesa, D., Dalla Rosa, M., Bucci, A., Rinaldi, F., & Andreoletti, C. (2021). Company Data Governance Transformation to Support the Business Evolution. In Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 (p. D021S035R004). https://doi.org/10.2118/207525-MS
  30. Sandelowski, M., & Barroso, J. (2006). Handbook for Synthesizing Qualitative Research. Publishing Company.
  31. Smith, G. (2020). Thoughts on the Key Themes in Oil and Gas Data Management Dropping Down the Digitalization Hype Curve. December 2019.
  32. Su, J., Yao, S., & Liu, H. (2022a). Data Governance Facilitate Digital Transformation of Oil and Gas Industry. Frontiers in Earth Science, 10, 622. https://doi.org/10.3389/feart.2022.861091
  33. Su, J., Yao, S., & Liu, H. (2022b). Data Governance Facilitate Digital Transformation of Oil and Gas Industry. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.861091
  34. Wang, H., Wu, H., & Wang, X. (2021). Research on the application of big data in the petroleum industry. 12. https://doi.org/10.1117/12.2622722
  35. Xiong, H. P., Liu, W. W., & Zhao, C. Y. (2014). A metadata management model for massive data engineering in oilfield. Applied Mechanics and Materials, 513–517, 4372–4377. https://doi.org/10.4028/www.scientific.net/AMM.513-517.4372
  36. Yuanting, W., Zhanmin, Z., Lihong, D., Weiyi, X., Guizhi, M., Dengwen, Z., Yun, L., Bo, Y., Jiaheng, W., & Limin, Y. (2019). Research and Application of Big Data Analysis Platform for Oil Production Engineering in Huabei Oilfield. 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019, 148–151. https://doi.org/10.1109/ICBDA.2019.8713238
Iranian Journal of Public Policy
Volume 11, Issue 1
March 2025
Pages 217-237

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