TY - JOUR
T1 - Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects
AU - Steffens, Bastian
AU - Corlay, Quentin
AU - Suurmeyer, Nathan
AU - Noglows, Jessica
AU - Arnold, Daniel
AU - Demyanov, Vasily
N1 - Funding Information:
Funding: This work was supported by the James Watt Scholarship (B.S. 100%, Q.C. 50%) and the NERC National Productivity Investment Fund (NPIF) grant, ref. no. NE/R01051X/1 (Q.C. 50%). The APC was funded by Studio X LLC (50%) and the James Watt Scholarship of B.S. (50%).
Funding Information:
Acknowledgments: We would like to thank the NERC National Productivity Investment Fund (NPIF) grant, ref. no. NE/R01051X/1, and Heriot Watt University, via its James Watt Scholarship scheme, for funding Quentin Corlay’s PhD studies in equal parts; additionally, we would like to thank Heriot Watt University for sponsoring 100% of Bastian Steffens’ PhD studies via its James Watt Scholarship scheme. We would also like to thank the Natural Environmental Research Coun‐ cil (NERC) Centre for Doctoral Training (CDT) in Oil and Gas of which both PhD studies are part. We would also like to acknowledge Shell and Studio X for setting up the Go with the Flow geo‐data science challenge and their support for publishing this work. Finally, we would like to thank Hel‐ en Lewis, Thomas Wagner, Dave McCarthy, and the anonymous reviewers for their comments and suggestions that improved this paper.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect.
AB - Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect.
KW - hydrocarbon migration
KW - conceptual modelling
KW - Agent-based modelling
KW - petroleum system modelling;
KW - decision making
KW - Uncertainty quantification
KW - Go with the Flow
UR - http://www.scopus.com/inward/record.url?scp=85123633383&partnerID=8YFLogxK
U2 - 10.3390/en15030902
DO - 10.3390/en15030902
M3 - Article
SN - 1996-1073
VL - 15
JO - Energies
JF - Energies
IS - 3
M1 - 902
ER -
