| Summary: | 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.
|
|---|