A data assimilation framework to constrain anthropogenically-induced geomechanical processes at depth: the subsidence case

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Authors

Thibault Candela, Alin Chitu, Elisabeth Peters, Maarten Pluymaekers, Dries Hegen, Kay Koster, Peter Fokker

Abstract

Subsurface activities, such as reservoir gas production, geothermal heat extraction, ground water extraction, phreatic groundwater level lowering, storage of natural gas and CO2, potentially lead to geomechanical risks. The two most critical instances of these risks are anthropogenically-induced seismicity and subsidence.
A combination of geological interpretations with seismic campaigns and flow modeling often provides a relatively rich pre-existing knowledge of the underground around anthropogenic subsurface activities. However, our understanding of the driving mechanisms for induced seismicity and subsidence is still poor and our modelling forecasts still very uncertain.
In our companion paper [Candela et al., 2021] the focus is on induced seismicity; here the focus is on subsidence induced by natural gas extraction. The translation of reservoir pressure depletion to ground surface displacements involves multiple types of poorly constrained physics-based models. Deploying a data assimilation procedure based on ensemble smoother algorithms, we demonstrate that (i) the reservoir compaction process driving subsidence can be effectively constrained and (ii) our subsidence forecasts can be well aligned with the geodetic observations. The identification of the physical process at work is crucial to build confidence in our subsidence forecasts.
The performance is achieved by honoring uncertainties at each step of the workflow from reservoir depletion to ground surface displacement and by assimilating not only vertical displacements (that is the subsidence) but also horizontal displacements. The predictive power of the procedure is demonstrated with an ensemble of synthetic but complex reservoir flow simulations mimicking all the characteristics and uncertainties representative for real gas fields in the north of the Netherlands.

DOI

https://doi.org/10.31223/X5T02M

Subjects

Earth Sciences, Environmental Sciences, Oil, Gas, and Energy, Statistics and Probability

Keywords

Inverse theory

Dates

Published: 2021-03-25 08:29

Last Updated: 2021-03-25 15:29

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The data generated by the integrated approach are available on request from the corresponding author.

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