Data assimilation, a geoscience data technique that originates from meteorology and oceanography, is widely used in the oil and gas industry for assisted history matching. The method aims to find a model solution to a given dynamic problem that agrees with the observations given the uncertainties in both observations and models. In this webinar, I will give an introduction to the concept of data assimilation and introduce a number of well-known methods. I will illustrate the use of so-called iterative ensemble smoothers to effectively estimate parameters of a model that predicts the evolution of the COVID-19 pandemic in eight countries, including Norway, England, France, Brazil, and a number of states in the United States. The model used, a SEIR model with age-classes and compartments of sick, hospitalized, and dead, is conditioned on daily numbers of accumulated deaths, the number of hospitalized and, where possible, on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters leading to model predictions in close agreement with the observations. The updated ensemble of model simulations have predictive capabilities and include uncertainty estimates. In this webinar, I will discuss how we can estimate the effective reproductive number as a function of time, and how the evolution of the pandemic depends on local differences in response to intervention measures.
Femke is Associate Professor in Geoscience and Engineering at Delft University of Technology, Netherlands. In her research, she assimilates data into numerical models of subsurface flow and mechanics to estimate the effects of subsurface activities and associated risks. With a PhD in Aerospace Engineering from Delft University of Technology, she spent almost 10 years in climate research before joining Shell International Exploration and Production. Working in industry, she applied her expertise in the field of data assimilation to applications of assisted history matching for closed-loop reservoir management, and the integration of seismic and non-seismic data for exploration and reservoir characterization. She also led opportunity evaluation in unconventional resources and portfolio analysis in Shell’s business planning. In September 2016, Femke was awarded a Delft Technology Fellowship, which enabled her to return to TU Delft. Applications of her current research include the use of satellite data for subsidence in coastal regions, the joint inversion of seismic and EM, and the use of data assimilation to improve forecasts of induced seismicity, to name a few. Triggered by current events, she joined an international team that investigates the use of data assimilation to improve the assessment of the COVID-19 pandemic.