Humankind is increasingly using the earth’s subsurface as a resource but also as repository. Clear water reservoirs need the highest protection level as critical resources; nuclear waste and greenhouse gases must be stored safely for centuries; and the subsurface storage of energy in the form of heat, methane gas and/or compressed air is becoming a key technology for the energy revolution. Ever since the industrial revolution’s waste products first polluted water, the number and complexity of major conflicts between these different uses have been steadily rising and continue to do so today. Contemporary problems include the long range effects of mechanical, chemical, and thermal energy storage on groundwater, and the complex aftereffects of hydraulic stimulation on deep geothermal energy or shale gas extraction. Furthermore, research into the underlying processes cannot solely concentrate on subsurface domains but must also examine the interaction with the atmosphere. To mention just two examples of this interaction: atmospheric pressure influences the efficiency of compressed-air energy storage; and the land surface water budget strongly depends on how the porous structure of the soil and the highly turbulent flow distribution in the atmospheric boundary layer interact.
Predicting and controlling the reciprocal influence of subsurface projects, their impact on (ground) water, and their interaction with the atmosphere are some of the key challenges that must be solved for a sustainable, safe society. Classical simulation approaches are failing to deliver useful predictions for decision support in this respect. Despite the increasing amount of in-situ and remote sensing data, relevant subsurface properties cannot be adequately characterized due to their immense scale complexity. Thus, microtomography rock images may offer a wealth of information but they only work for very small samples. At very large scales, geophysical exploration also generates rich data, but the intermediate scales relevant for decision supporting predictions are data poor. Cutting edge porous media research results in ever new model concepts, yet with a limited range and scale of validity, and with insufficient data to choose among them. Multiscale simulations and scale-bridging techniques suffer from data scarcity on relevant scales, which precludes unique calibration.
We are convinced that dataintegrated simulation science can solve these problems on numerous fronts: Direct numerical simulations on pore-scale rock imaging data will continue to deepen fundamental process understanding. Scale-bridging techniques will transfer the improved concepts to relevant scales, flanked by appropriate uncertainty quantification to address data scarcity on the relevant scales.
Appropriate machine learning methods will extend numerical and physics-based homogenization techniques by learning smaller-scale models in agreement with basic physical principles. Dynamic recalibration and data assimilation algorithms will use incoming data from subsurface sensors to continuously update model parameters and improve forecasts. The residuals observed during data assimilation will support a context-sensitive, adaptive choice between scientifically competing model concepts, and they will be used to learn intelligent bias corrections. Active learning algorithms will suggest optimal extensions of subsurface sensor networks to enrich the incoming data stream. Novel control algorithms will improve subsurface operations while reliably restricting environmental impact to acceptable threshold levels. Runtime metadata collected from the entire simulation workflow will be used for the dynamic adaptation of models, numerical schemes, and computing resources. Novel context-aware visualization techniques and traceability concepts will make it possible to interpret and manage the wealth of data sources and simulation results in an intuitive way. Eventually, users will interact with this distributed data-integrated simulation environment from anywhere using a variety of immersive devices, thus facilitating the making of informed decisions about complex engineered geosystems on demand. Today, the methods and techniques are not yet existant for realizing such far-reaching scenarios, but the research outlined in this proposal will prepare the ground for these and other innovations.