From Liability to Strategic Asset: A Data-Driven, Regulatory-Compliant Framework for Inactive Well Portfolio Recovery and Its Implications for U.S. National Energy Security
Keywords:
inactive well portfolio management; multi-criteria decision framework; petroleum data engineering; regulatory compliance automation; domestic energy security; decline curve analytics; ETL pipelineAbstract
Inactive oil and gas wells represent a largely underutilized category of domestic energy asset. In the United States, tens of thousands of inactive wells sit within mature producing basins, constrained by regulatory deadline pressure and the absence of systematic evaluation methodology. This paper presents a four-part data-driven framework for inactive well portfolio recovery, developed and validated through direct field implementation: a purpose-built ETL data pipeline integrating heterogeneous petroleum data sources; a weighted multi-criteria composite scoring algorithm producing a four-tier well classification; a nine-domain regulatory compliance automation system aligned with Texas Railroad Commission requirements; and a national scaling analysis with actionable policy recommendations. The framework was applied to a portfolio of over two hundred inactive wells across multiple Texas producing fields, demonstrating substantial improvements in evaluation efficiency, capital allocation precision, regulatory compliance performance, and economic recovery relative to conventional well-by-well approaches. The quantified outcomes of this field deployment establish that systematic data engineering and algorithmic prioritization can convert inactive well inventories from regulatory liability into recoverable domestic energy supply at scale. Applied nationally, the methodology offers a domestic energy security pathway characterized by rapid deployment timelines, capital efficiency superior to new exploration, and environmental co-benefits aligned with modern ESG frameworks.
Downloads
References
D. Kristanto, D. Rukmana, A. Amperianto, and W. Paradhita, "Evaluation and reactivation strategy of shut-in wells due to high water cut to improve oil production in Bayu Field: Case study of Bayu-N3 well," Int. J. Oil Gas Coal Eng., vol. 10, no. 1, pp. 31–41, Feb. 2022. doi: 10.11648/j.ogce.20221001.13
S. R. Rachapudi Venkata et al., "Idle wells reactivation: The Kenyon International story," in Proc. SPE Nigeria Annual Int. Conf. Exhibition (NAIC), Lagos, Nigeria, Aug. 2022, paper SPE-211955-MS. doi: 10.2118/211955-MS
B. Higgins et al., "An integrated analysis of shut-in well reactivation for oil production optimization in the DLN-11 well," J. Pet. Sci. Technol., 2024. [Online]. Available: https://www.researchgate.net/publication/403411021
A. Meenakshisundaram, O. S. Tomomewo, L. Aimen, and S. O. Bade, "A comprehensive analysis of repurposing abandoned oil wells for different energy uses: Exploration, applications, and repurposing challenges," Clean. Eng. Technol., vol. 22, p. 100797, Aug. 2024. doi: 10.1016/j.clet.2024.100797
Z. Wei, S. Zhu, X. Dai, X. Wang, L. M. Yapanto, and I. R. Raupov, "Multi-criteria decision making approaches to select appropriate enhanced oil recovery techniques in petroleum industries," Energy Rep., vol. 7, pp. 2751–2758, Nov. 2021. doi: 10.1016/j.egyr.2021.05.002
Y.-F. Huang, "Multi-criteria decision making (MCDM) model for supplier evaluation and selection for oil production projects in Vietnam," Processes, vol. 8, no. 2, p. 134, Feb. 2020. doi: 10.3390/pr8020134
A. Kolios, V. Mytilinou, E. Lozano-Minguez, and K. Salonitis, "A comparative study of multiple-criteria decision-making methods under stochastic inputs," Energies, vol. 9, no. 7, p. 566, Jul. 2016. doi: 10.3390/en9070566
A. Tadjer, A. Hong, and R. B. Bratvold, "Machine learning based decline curve analysis for short-term oil production forecast," Energy Explor. Exploit., vol. 39, no. 5, pp. 1747–1769, Sep. 2021. doi: 10.1177/01445987211011784
D.-R. Jacota, C. R. Popa, M. Tanase, and C. Veres, "Machine-learning algorithm and decline-curve analysis comparison in forecasting gas production," Processes, vol. 14, no. 5, p. 826, 2026. doi: 10.3390/pr14050826
A. Loye, M. Olalekan, M. Stephen, and A. R. Nyemenim, "Development of universal decline curve analysis technique for forecasting the performance of oil wells," Int. J. Eng. Trends Technol., vol. 31, Jan. 2016. [Online]. Available: https://www.researchgate.net/publication/299552336
F. D. Wicaksono, Y. B. Arshad, and H. Sihombing, "Monte Carlo net present value for techno-economic analysis of oil and gas production sharing contract," Int. J. Technol., vol. 10, no. 4, pp. 829–840, Jul. 2019. doi: 10.14716/ijtech.v10i4.2051
R. Qiu, Z. Li, Q. Zhang, X. Yao, S. Xie, Q. Liao, and B. Wang, "A realistic and integrated model for evaluating offshore oil development," J. Mar. Sci. Eng., vol. 10, no. 8, p. 1155, 2022. doi: 10.3390/jmse10081155
M. A. Cremon, M. A. Christie, and M. G. Gerritsen, "Monte Carlo simulation for uncertainty quantification in reservoir simulation: A convergence study," J. Pet. Sci. Eng., vol. 190, 2020. doi: 10.1016/j.petrol.2020.107015
A. Goyal, "Upstream oil & gas data management trends: Challenges and potential solutions," in Proc. Int. Field Exploration and Development Conf. 2024 (IFEDC 2024), Springer Series in Geomechanics and Geoengineering, Singapore: Springer, 2025, pp. 1723–1729. doi: 10.1007/978-981-96-2363-1_104
J. Su, S. Yao, and H. Liu, "Data governance facilitate digital transformation of oil and gas industry," Front. Earth Sci., vol. 10, p. 861091, Mar. 2022. doi: 10.3389/feart.2022.861091
R. Joshi, V. Desai, A. Waghela, and P. Tawde, "Predictive analysis of oil and gas using well log data," in Lect. Notes Electr. Eng., vol. 1185, Singapore: Springer, 2024, pp. 311–319. doi: 10.1007/978-981-97-1682-1_32
H. H. Nguyen, P. van Nguyen, and V. M. Ngo, "Energy security and the shift to renewable resources: The case of Russia-Ukraine war," Extr. Ind. Soc., vol. 17, p. 101442, Feb. 2024. doi: 10.1016/j.exis.2024.101442
N. H. A. Razek, V. Galvani, S. Rajan, and B. McQuinn, "Can U.S. strategic petroleum reserves calm a tight market exacerbated by the Russia–Ukraine conflict?" Resour. Policy, vol. 86, p. 104062, Oct. 2023. doi: 10.1016/j.resourpol.2023.104062
Railroad Commission of Texas, "Inactive Well Aging Report (IWAR)," Texas RRC, Austin, TX, Sep. 2023. [Online]. Available: https://www.rrc.texas.gov/oil-and-gas/compliance-enforcement/hb-2259-hb-3134-inactive-well-requirements/inactive-well-aging-report-iwar/
Texas Legislature, Senate Bill 1150: Relating to Requirements for Plugging Oil and Gas Wells, 89th Legislature, Regular Session, Austin, TX, 2025. [Online]. Available: https://capitol.texas.gov/tlodocs/89R/analysis/html/SB01150F.htm
Nikhil Patel; Sandeep Trivedi; Vijay Bhalani; Nuruzzaman Faruqui "Curriculum vitae sorting: A novel framework for personality-based automatic CV sorting using deep learning," in Proc. Int. Conf. Adv. Inf. Technol. (CAIT), 2024, doi: 10.1109/CAIT64506.2024.10963094.
V. Bhalani , "Exploring the frontier of generative AI models: Impact and future directions," J. Inf. Comput. Sci., vol. 14, no. 12, 2024, doi: 10.12733/JICS.2024.V14I12.535569.110003.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.


