Organizational Over Technology: Why Digital Twin Adoption in Oil and Gas Stalls—A Review and the Case for Mechanism Design

A literature review of ten peer-reviewed articles published between 2018 and 2025 examines the adoption of digital twins in the oil and gas sector. According to Meza et al. [1], 68% of the implementations are digital shadows, which involve one-way data flow, 18% are digital models, and only 14% are actual digital twins with bidirectional feedback. Wanasinghe et al. [2] noted that 88% of the published work comes from the industry, with 95% related to the supply chain and 5% from operators, while 12% of the studies are from academic institutions. Furthermore, only 7% of the research involves collaboration between industry and academia (Meza et al. [1]). The lack of standardization and data silos are identified as the major obstacles in six studies each. Pandi [3] highlights a persistent "broken chain" in data transfer that has persisted for more than two decades, emphasizing that errors in 10% of engineering work can significantly impact 90% of costs. Only three papers present specific quantitative outcomes: Shen et al. [4] reported a 5.41% increase in efficiency across 35 oil wells, while LeBlanc [5] modeled a net present value of $211 million over 27 years.
These findings suggest that the main limitation is organizational rather than technological. Theoretical frameworks, especially those developed by Professor Jun Cai on incentive-compatible mechanisms for distributed systems, provide a foundational approach for tackling governance issues. Future studies should focus on applying these mechanism design concepts within the specific institutional environment of digital twins in the oil and gas industry.

Digital twin, Oil and gas, Mechanism design, Data governance

1. Meza, E. B. M., Souza, D. G. B. d., Copetti, A., Sobral, A. P. B., Silva, G. V., Tammela, I., & Cardoso, R. (2024). Tools, technologies and frameworks for digital twins in the oil and gas industry: An in-depth analysis. Sensors, 24(19), 6457. [Google Scholar] [Crossref]

2. Wanasinghe, T. R., Wroblewski, L., Petersen, B. K., Gosine, R. G., James, L. A., De Silva, O., Mann, G. K. I., & Warrian, P. J. (2020). Digital twin for the oil and gas industry: Overview, research trends, opportunities, and challenges. IEEE Access, 8, 104176-104198. [Google Scholar] [Crossref]

3. Pandi, S. (2023). A study on building blocks of digital twin for oil and gas industry. International Journal of Engineering Research & Technology (IJERT), 12(11). [Google Scholar] [Crossref]

4. Shen, F., Ren, S. S., Zhang, X. Y., Luo, H. W., & Feng, C. M. (2021). A digital twin-based approach for optimization and prediction of oil and gas production. Mathematical Problems in Engineering, 2021, 3062841. [Google Scholar] [Crossref]

5. LeBlanc, M. B. (2020). Digital twin technology for enhanced upstream capability in oil and gas (Master's thesis). Massachusetts Institute of Technology. [Google Scholar] [Crossref]

6. Cameron, D., Waaler, A., & Komulainen, T. (2018). Oil and gas digital twins after twenty years: How can they be made sustainable, maintainable and useful. Proceedings of the 59th Conference on Simulation and Modelling, No. 153, pp. 9-16. [Google Scholar] [Crossref]

7. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474 [Google Scholar] [Crossref]

8. Li, B., Gai, J., & Xue, X. (2020). The digital twin of oil and gas pipeline system. IFAC-PapersOnLine, 53(3), pp. 1-5. [Google Scholar] [Crossref]

9. Xue, X., Li, B., & Gai, J. (2020). Asset management of oil and gas pipeline system based on digital twin. IFAC-PapersOnLine, 53(3), pp. 1-5. [Google Scholar] [Crossref]

10. Correia, J. B., Rodrigues, F., Santos, N., Abel, M., & Becker, K. (2022). Data management in digital twins for the oil and gas industry: Beyond the OSDU data platform. Journal of Information and Data Management, 13(3), 409-422. [Google Scholar] [Crossref]

11. Dmitriev, V. M. et al. (2025). Digital twins in the oil and gas industry: A review of Russian and international scientific sources. Tomsk State University of Control Systems and Radioelectronics, pp. 1-12. [Google Scholar] [Crossref]

12. Cai, J. (2024-present). Faculty profile. Concordia University. [Google Scholar] [Crossref]

13. Yi, C., Chen, R., Chen, J., Li, X., & Cai, J. (2026). Self-evolving digital twin over wireless networks: Dynamic twin construction and service interaction. Springer. [Google Scholar] [Crossref]

14. Li, G., Cai, J., & Ni, S. (2022). Truthful deep mechanism design for revenue-maximization in edge computing with budget constraints. IEEE Transactions on Vehicular Technology, 71(1), 902-914. [Google Scholar] [Crossref]

15. Li, G., Cai, J., He, C., Zhang, X., & Chen, H. (2024). Online incentive mechanism designs for asynchronous federated learning in edge computing. IEEE Internet of Things Journal. [Google Scholar] [Crossref]

16. Li, G., Cai, J., Lu, J., & Chen, H. (2025). Incentive mechanism design for cross-device federated learning: A reinforcement auction approach. IEEE Transactions on Mobile Computing, 24(4), 3059-3075. [Google Scholar] [Crossref]

17. Okegbile, S. D., Cai, J., Chen, J., & Yi, C. (2024). A reputation-enhanced shard-based Byzantine fault-tolerant scheme for secure data sharing in zero trust human digital twin systems. IEEE Internet of Things Journal, 11(12), 22726-22741. [Google Scholar] [Crossref]

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