Multi-Stakeholder Agent-Based Decision Framework for Sustainable Horizontal Well Placement: Field Validation of Ultra-Deep Azimuthal Resistivity Integration Across Three Well Examples
DOI:
https://doi.org/10.29017/scog.v49i3.2164Keywords:
sustainable oil recovery, energy policy, agent-based, drilling risk, UDAR technology, 3D far-field petrophysicsAbstract
The oil industry is under mounting pressure to reconcile economic viability with environmental sustainability while managing complex stakeholder dynamics. Traditional modelling approaches struggle to capture emergent behaviours from multi-agent decision-making in sustainable oil recovery (Bonabeau, 2002; Macal & North, 2010). Three field wells are examined through an economic review within an agent-based framework that integrates Ultra-Deep Azimuthal Resistivity (UDAR) technology with 3D far-field petrophysics to optimize sustainable oil recovery by improving stakeholder collaboration (Manuaba et al., 2025a). We developed a multi-agent system comprising nine stakeholder types of petrophysicists, drilling engineers, geologists, reservoir engineers, oil operators, service companies, government regulators, oil market analysts, and geopolitical forces that incorporate UDAR’s 3D far-field characterization capabilities. The model examines four critical points: economic feasibility, environmental impact, technical risk, and political stability. An agent-based framework illustrates how UDAR technology serves as a central information hub, facilitating informed decision-making across technical, business, and regulatory domains (Manuaba et al., 2024; Pewekar et al., 2025). The model captures complex feedback mechanisms, including market signals, regulatory alignment, technology integration loops and human interactions. Sustainability metrics, such as well lifespan, energy efficiency, social impact, economic viability, and resource conservation, are embedded in agents' decision-making processes (Erhueh et al., 2023). Results show that integrating UDAR technology with agent-based provides a robust framework for optimizing sustainable oil recovery operations. This approach enables coordinated stakeholder action while balancing competing objectives and adapting to dynamic market and regulatory conditions, offering significant potential for improving both economic and environmental outcomes.
References
Agle, B. R., Mitchell, R. K., & Sonnenfeld, J. A. (1999). Who matters to CEOs? An investigation of stakeholder attributes and salience, corporate performance, and CEO values. Academy of Management Journal, 42(5), 507–525. https://doi.org/10.5465/256973
Albaqami, N., Al-Ansari, Y., Alrowaili, S., et al. (2025). Increasing the resolution of the inversion by combining deep resistivity with ultra-deep resistivity. SPE Middle East Oil & Gas Show and Conference. https://doi.org/10.2118/226907-MS
Ameneiro, R., Clegg, N., Walmsley, A., et al. (2023). 3-dimensional ultradeep azimuthal resistivity: A tool for identification of bypassed pay in mature fields. SPWLA Annual Logging Symposium. https://doi.org/10.30632/spwla-2023-0071
Basu, S. (2017). Mobile app enabling instant, real-time integration of geology, petrophysics, reservoir engineering, production technology, petroleum engineering, production engineering, and process engineering disciplines (U.S. Patent Application).
Bazilian, M., Rogner, H., Howells, M., Hermann, S., Arent, D., Gielen, D., & Yumkella, K. K. (2011). Considering the energy, water and food nexus: Towards an integrated modelling approach. Energy Policy, 39(12), 7896–7906. https://doi.org/10.1016/j.enpol.2011.09.039
Bonabeau, E. (2002). Agent-based modelling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(3), 7280–7287. https://doi.org/10.1073/pnas.082080899
Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., & Mac Dowell, N. (2018). Carbon capture and storage (CCS): The way forward. Energy & Environmental Science, 11(5), 1062–1176. https://doi.org/10.1039/C7EE02342A
Clarkson, M. E. (1995). A stakeholder framework for analyzing and evaluating corporate social performance. Academy of Management Review, 20(1), 92–117. https://doi.org/10.5465/amr.1995.9503271994
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone Publishing.
Erhueh, O. V., Esiri, A. E., & Wodu, E. K. (2023). Advancing petrophysical techniques for sustainable hydrocarbon extraction: Enhancing resource efficiency and minimizing waste. World Journal of Advanced Research and Reviews, 17(2), 243–258. https://doi.org/10.30574/wjarr.2023.17.2.0243
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Cambridge University Press.
Frooman, J. (1999). Stakeholder influence strategies. Academy of Management Review, 24(2), 191–205. https://doi.org/10.5465/amr.1999.1893939
Ganguli, S. S., & Dimri, V. P. (2023). Reservoir characterization: State-of-the-art, key challenges and ways forward. In Developments in Structural Geology and Tectonics (Vol. 8, pp. 389–427). Elsevier. https://doi.org/10.1016/B978-0-323-99593-1.00015-X
Höök, M., & Tang, X. (2013). Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy, 52, 797–809. https://doi.org/10.1016/j.enpol.2012.10.046
Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. https://doi.org/10.1057/jos.2010.3
Manuaba, I. B. G. H., Aljishi, M. K., Santoso, G. I., & Dolan, J. T. (2023). Fracture network characterization beyond wellbores: A new approach to identify water corridors through advanced resistivity analysis (SPE-214995-MS). Society of Petroleum Engineers. https://doi.org/10.2118/214995-MS
Manuaba, I. B. G. H., Ghanim, R. A., Bikchandaev, E., et al. (2024). Far-field petrophysics utilizing deep-resistivity high-definition and ultra-deep-resistivity 3D inversion in thin, laminated reservoirs. International Petroleum Technology Conference. https://doi.org/10.2523/IPTC-23716-EA
Manuaba, I. B. G. H., Ghanim, R. A., Bikchandaev, E., et al. (2025a). Far field petrophysics for saturation mapping beyond the wellbore. SPE Journal. https://doi.org/10.2118/231160-PA
Manuaba, I. B. G. H., Abdrabalnby, A. A., Wang, H., & Santoso, G. I. (2025b). Mapping water paths using ultra-high resolution borehole imaging and ultra-deep directional resistivity logging while drilling in carbonate reservoirs (SPE-226313-MS). https://doi.org/10.2118/226313-MS
Pewekar, A., Abbas, Y., Ghosh, K., et al. (2025). Smart reservoir management and proactive model enhancement with ultra-deep azimuthal resistivity: From real-time mapping to post-drilling applications. SPE Middle East Oil & Gas Show and Conference. https://doi.org/10.2118/226909-MS
Phillips, R., Freeman, R. E., & Wicks, A. C. (2003). What stakeholder theory is not. Business Ethics Quarterly, 13(4), 479–502. https://doi.org/10.5840/beq200313434
Ringler, P., Keles, D., & Fichtner, W. (2016). Agent-based modelling and simulation of smart electricity grids and markets: A literature review. Renewable and Sustainable Energy Reviews, 57, 205–215. https://doi.org/10.1016/j.rser.2015.12.169.
Wu, H.-H., Chatterjee, A., Clegg, N., et al. (2023). A new focused UDAR inversion to highlight finer geological features in transitional-resistivity formations. SPWLA Annual Logging Symposium. https://doi.org/10.30632/SPWLA-2023-0069.
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