arXiv:2602.22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces. Conventional high-fidelity numerical models require a high resolution to obtain reliable results, resulting in huge computational expense. This restricts the applicability of these models for multi-query problems, such as uncertainty quantification and optimisation, which require running numerous scenarios. As a cheaper alternative to high-fidelity models
The increasing computational demands of complex scientific and engineering problems, coupled with advancements in AI/ML techniques, necessitate more efficient simulation methods.
This development offers a pathway to significantly reduce the computational cost of high-fidelity simulations, enabling new applications in critical sectors like energy, materials science, and environmental modeling.
The ability to perform multi-query analyses like uncertainty quantification and optimization for highly complex physical interactions becomes feasible without prohibitive computational expense.
- · AI/ML researchers
- · Energy sector (oil & gas, geothermal)
- · Environmental engineering
- · Materials science
- · Traditional high-fidelity numerical simulation software vendors (without AI inte
Faster and cheaper R&D cycles for complex physical systems, accelerating innovation in various industries.
Increased adoption of AI/ML in scientific computing, leading to new specialized hardware and software demands.
Enhanced predictive capabilities improve resource management and risk assessment in areas susceptible to rock-fluid interactions, such as carbon sequestration or groundwater management.
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