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Long-term research into deep subsurface processes under the Netherlands

Eight innovative projects will investigate deep subsurface movements and processes under the Netherlands. The NWO Science Board has awarded a total of almost 9 million euros to these projects within the research programme DeepNL. This programme seeks to improve the fundamental understanding of deep subterranean dynamics that occur under the influence of human interventions. The projects concern, for example, the effect of frictional heating, ground subsidence as a source of information for subterranean processes and predicting surface deformation.

There is insufficient scientific knowledge available about the effect of human interventions deep under the ground. The programme DeepNL therefore wants to build up knowledge about deep subsurface movements and processes. With this programme, NWO is responding to the advice of the Dutch Safety Board to ensure a structural and long-term research programme into the problems related to gas extraction in the province of Groningen. Within the projects, senior researchers together with 31 new PhDs and postdocs will, over the next four years, perform laboratory experiments and fieldwork and use computer models. DeepNL is partly possible due to a financial contribution from NAM and is part of NWO's contribution to the Top Sector Energy.

Assessment of research projects
The focus of this call are improved models and predictions for deep subsurface dynamics resulting from gas production in the Groningen reservoir. Twenty-four full project proposals were submitted for this first call within the programme. Each proposal was assessed by international experts during the peer-review process. After that, an independent international selection committee drew up an overall granting advice for the NWO Science Board.

Follow-up
The projects from this first call will lay the basis for DeepNL. In the coming years, more calls will follow for subjects that are still missing, the integration and application of results, and the structural strengthening of the research field. At the start of 2019, the researchers involved in the eight projects will come together during a first scientific meeting. The Knowledge Programme Effects of Mining established by the Ministry of Economic Affairs and Climate Policy will also join this meeting. The research results from DeepNL will be freely accessible to everybody.

Comprehensive monitoring of the Groningen gas field
University of Twente researcher dr. Kathrin Smetana from the Department of Applied Mathematics is co-applicant of the awarded research project: “Comprehensive monitoring and prediction of seismicity within the Groningen gas field using large scale field observations”, for which prof. dr. J.A. Trampert, Department of Earth Sciences, Utrecht University is principal applicant. Other co-applicants are: prof.dr. M.N.M. van Lieshout, Department of Applied Mathematics, University of Twente, dr. H. Paulssen, Department of Earth Sciences, Utrecht University.

Moreover, the project has several partners: dr. C. Brune, dr. M. Schlottbom, prof. dr. J. van der Vegt (all Department of Applied Mathematics, University of Twente), dr. E. Ruigrok (KNMI and Utrecht University), prof. dr. J. Tromp (Princeton University), and A. van Wettum (Utrecht University). Only considering the researchers from the Department of Applied Mathematics, the project team comprises already experts on quite different fields such as model order reduction, spatial statistics and stochastic operations research, deep learning, inverse problems, and discretization methods for partial differential equations.    

The induced seismicity of the Groningen gas field can only be understood through the relation between gas extraction and subsurface response. To understand the dynamics of the system new approaches are required that are based on observational data. The research team will monitor the seismicity and determine stress changes, reservoir compaction and deformation of the overlying layers from seismic data. Advanced tools based on machine learning, model order reduction and supercomputing will be developed to model the recorded seismograms and to detect subsurface variations. Changes in earthquake risk due to changes in gas production will be assessed by stochastic modelling.