Disentangling Shallow and Deep Processes Causing Surface Movement |
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Authors: | I C Kroon B-L Nguyen P A Fokker A G Muntendam-Bos G de Lange |
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Institution: | (1) TNO, Postbus 80015, 3508, TA, Utrecht, the Netherlands |
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Abstract: | Understanding and predicting surface movement is important both technically and for social reasons. The shallow processes
contributing to subsidence include construction works, peat oxidation, clay compaction, and groundwater withdrawal; deep causes
are hydrocarbon and salt production. We describe an inversion procedure we have devised to disentangle the deep and shallow
causes of surface movement. It employs a Bayesian inversion scheme, using forward models and other ‘a priori’ information
about shallow and deep compaction. Parameter estimation thus takes place at two different depths, thereby disentangling the
deep and shallow compaction processes responsible for surface movement. The uncertainty in the surface measurements and ‘a
priori’ estimates is naturally incorporated. Furthermore, spatial and temporal correlations can be taken into account through
inclusion of the covariance matrix. The inversion scheme is demonstrated for two synthetic cases. The first combines a compacting
gas field and a compacting shallow peat layer. We demonstrate that assumptions on the shape of the subsidence bowl are not
necessary. We also show how neglecting either deep or shallow causes of subsidence can produce spurious results. The advantage
of using the ‘a priori’ estimates of the compaction and the covariance matrix obtained by Monte Carlo simulations is demonstrated
with a second synthetic example involving two polders and different depths of their water table. A robust solution is obtained
for each polder unit, while a simpler (and faster) ‘a priori’ estimate based on the expected average clay thickness fails
to reproduce the actual compaction. Monte Carlo simulations can also be applied to compaction in depleting gas reservoirs.
Information on spatial correlations is often available, even when the absolute values of the ‘a priori’ compaction data are
quite uncertain. Explicitly incorporating such ‘a priori’ known spatial correlations improves the result significantly. |
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Keywords: | Subsidence Compaction History matching Covariance Inversion |
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