It is true to say the oil & gas industry is transforming – in practice, in operations, and economics. But the physics have not changed - the .. Read more elemental challenge flowing hydrocarbons from a rock matrix to the surface remains the same. Despite advanced numerical techniques including now various forms of advanced Machine Learning and Artificial Intelligence we struggle to accurately simulate and understand physical processes in the reservoir and surrounding subsurface: fluid flow, transient and static fluid pressures, hydraulic fracturing, wellbore integrity, reservoir compaction just to name a few. Conventionally, we have relied on computationally intensive physics-based models. More recently, particularly driven by the large publicly available data sets in US unconventionals, there is an increasing trend to use purely data-driven models and extract the necessary intelligence. In this webinar we explore the challenges, disparities and limitations of physics-driven and data-driven models; and the opportunities of combining both approaches, as well as borrowing from other engineering and science disciplines that share the same or analogous physical laws.
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