1887
Volume 42, Issue 9
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

Advanced agent-based modeling (ABM) has been employed to simulate hydrocarbon migration and accumulation within diverse geological environments, providing a detailed representation of these subsurface processes. By treating hydrocarbon molecules as autonomous agents, the model captures the intricate interactions within digital subsurface environments, including sandstone, silt, and shale facies. The approach uncovers how geological traps, such as anticline formations and faulted structures, influence hydrocarbon accumulation, revealing patterns previously challenging to model with conventional methods. Additionally, the research addresses scaling challenges in ABM, offering insights into solutions like parallel computing. The findings not only advance petroleum geoscience but also suggest broader applications in environmental studies and resource management.

Loading

Article metrics loading...

/content/journals/10.3997/1365-2397.fb2024073
2024-09-01
2024-09-12
Loading full text...

Full text loading...

References

  1. Beaumont, E.A. and Foster, N.H. [1999]. Exploring for Oil and Gas Traps:American Association of Petroleum Geologists (AAPG) Treatise Handbook, 3. https://doi.org/10.1306/TrHbk624.
    [Google Scholar]
  2. Chattoe-Brown, E. and Gabbriellini, S. [2021]. How to Improve Network Science: the Potential of (Empirically Calibrated and Validated) Agent-Based Modelling. https://doi:10.31235/osf.io/bym74.
    [Google Scholar]
  3. Darcy, H. [1856]. Les Fontaines Publiques de la Ville de Dijon, Paris, France.
    [Google Scholar]
  4. Foschi, M. and Cartwright, J.A. [2016]. South Malvinas/Falkland basin: Hydrocarbon migration and petroleum system.Marine and Petroleum Geology, 77, 124–140. https://doi: 10.1016/j.marpetgeo.2016.06.002.
    https://doi.org/10.1016/j.marpetgeo.2016.06.002 [Google Scholar]
  5. Glen, C.M., Kemp, M.L. and Voit, E.O. [2019]. Agent-based modeling of morphogenetic systems: Advantages and challenges.PLoS computational biology, 15(3), e1006577. https://doi: 10.1371/journal.pcbi.1006577.
    https://doi.org/10.1371/journal.pcbi.1006577 [Google Scholar]
  6. Gorodnichev, M.G. [2022]. Simulation model of traffic flow based on agent-based modeling.Heritage and Sustainable Development, 4, 195–190. https://doi: 10.37868/hsd.v4i2.149.
    https://doi.org/10.37868/hsd.v4i2.149 [Google Scholar]
  7. Hamzeh, M. and Karimipour, F. [2020]. An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level.Open Geosciences, 12(1), 580–597. https://doi:10.1515/geo‑2020‑0159.
    https://doi.org/10.1515/geo-2020-0159 [Google Scholar]
  8. Hindle, A.D. [1997], Petroleum migration pathways and charge concentration: a three- dimensional model:AAPG Bulletin, 81, 1451–1481. https://doi.org/10.1306/3B05BB1E-172A-11D7-8645000102C1865D.
    [Google Scholar]
  9. Hunt, J.M. [1995]. Petroleum geochemistry and geology (textbook).Petroleum Geochemistry and Geology (Textbook) (2nd Ed.), WH Freeman Company.
    [Google Scholar]
  10. Klügl, F. and Bazzan, A.L. [2012]. Agent-based modeling and simulation.Ai Magazine, 33(3), 29–29. https://doi: 10.1609/aimag.v33i3.2425
    https://doi.org/10.1609/aimag.v33i3.2425 [Google Scholar]
  11. Magoon, L.B. and Dow, W.G. [1994]. The petroleum system—from source to trap, AAPG Memoir60. https://doi.org/10.1306/M60585.
    [Google Scholar]
  12. McDonald, G.W. and Osgood, N.D. [2023]. Agent-Based Modeling and its Tradeoffs: An Introduction & Examples.arXiv preprint arXiv:2304.08497. https://doi.org/10.48550/arXiv.2304.08497.
    [Google Scholar]
  13. Miller, K.D. [2015]. Agent-based modeling and organization studies: A critical realist perspective. Organization Studies, 36(2), 175–196. https://doi:10.1177/0170840614556921.
    [Google Scholar]
  14. O’Brien, G., Boreham, C., Thomas, H. and Tingate, P. [2009]. Understanding the critical success factors determining prospectivity — Otway Basin, Victoria.The APPEA Journal, 49(1), 129–170. https://doi:10.1071/aj08009.
    [Google Scholar]
  15. Schelling, T.C. [1969]. Models of segregation.The American Economic Review, 59(2), 488–493.
    [Google Scholar]
  16. Steffens, B., Corlay, Q., Suurmeyer, N., Noglows, J., Arnold, D. and Demyanov, V. [2022]. Can agents model hydrocarbon migration for petroleum system analysis? A fast screening tool to de-risk hydro- carbon prospects.Energies, 15(3), 902. https://doi.org/10.3390/en15030902.
    [Google Scholar]
  17. Tesfatsion, L. [2002]. Agent-based computational economics: Growing economies from the bottom up.Artificial life, 8(1), 55–82. https://doi:10.1162/106454602753694765.
    [Google Scholar]
  18. Wilensky, U. and Rand, W. [2015]. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press.
    [Google Scholar]
  19. Zhou, X., Jiao, W., Han, J., Zhang, J., Yu, H. and Wu, L. [2010]. Tracing hydrocarbons migration pathway in carbonate rock in Lunnan-Tahe oilfield.Energy Exploration & Exploitation, 28(4), 259–277. https://doi:10.1260/0144-5987.28.4.
    [Google Scholar]
/content/journals/10.3997/1365-2397.fb2024073
Loading
/content/journals/10.3997/1365-2397.fb2024073
Loading

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error