A data assimilation framework to constrain the driving processes of anthropogenically induced subsidence

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Authors

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

Abstract

Surface movement can be induced by many human subsurface activities: natural gas production, geothermal heat extraction, ground water extraction, phreatic groundwater level lowering, storage of natural gas and CO2. In this manuscript, we focus on subsidence caused by gas production. While geological interpretations, seismic campaigns and flow modeling often provide a relatively rich pre-existing knowledge, understanding of the driving mechanisms for induced subsidence is still poor and forecasts are often very uncertain. This is related to the multiple poorly constrained models that translate gas production to ground surface displacements. We have therefore devised and deployed an integrated workflow, coupling fast forward models and honoring uncertainties in our prior knowledge and in our model choices. We run ensemble-smoother algorithms to assimilate the ground-surface displacements and we demonstrate that the reservoir-rock compaction process driving subsidence can be effectively identified and constrained. This is crucial to build confidence in our subsidence forecasts. The predictive power of the integrated workflow 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: 2021-04-26 10:24

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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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