Current methods of monitoring subsurface CO 2 , such as repeat seismic surveys, are episodic and require highly skilled personnel to acquire the data. Simulations based on simplified models have previously shown that muon radiography could be automated to continuously monitor CO 2 injection and migration, in addition to reducing the overall cost of monitoring. In this paper, we present a simulation of the monitoring of CO 2 plume evolution in a geological reservoir using muon radiography. The stratigraphy in the vicinity of a nominal test facility is modelled using geological data, and a numerical fluid flow model is used to describe the time evolution of the CO 2 plume. A planar detection region with a surface area of 1000 m 2 is considered, at a vertical depth of 776 m below the seabed. We find that one year of constant CO 2 injection leads to changes in the column density of 1%, and that the CO 2 plume is already resolvable with an exposure time of less than 50 days.
The use of underground geological repositories, such as in radioactive waste disposal (RWD) and in carbon capture (widely known as Carbon Capture and Storage; CCS), constitutes a key environmental priority for the 21 st century. Based on the identification of key scientific questions relating to the geophysics, geochemistry and geobiology of geodisposal of wastes, this paper describes the possibility of technology transfer from high-technology areas of the space exploration sector, including astrobiology, planetary sciences, astronomy, and also particle and nuclear physics, into geodisposal. Synergies exist between high technology used in the space sector and in the characterization of underground environments such as repositories, because of common objectives with respect to instrument miniaturization, low power requirements, durability under extreme conditions (in temperature and mechanical loads) and operation in remote or otherwise difficult to access environments.
The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. S U M M A R YCosmic ray muons are highly penetrating, with some reaching several kilometres into solid rock. Consequently, muon detectors have been used to probe the interiors of large geological structures, by observing how the muon flux varies with direction of arrival. There is an increasing need to discriminate between materials differing only slightly in bulk density. A particularly demanding application is in monitoring underground reservoirs used for CO 2 capture and storage, where bulk density changes of approximately 1 per cent are anticipated. Muon arrival is a random process, and it is the underlying expectation values, not the actual muon counts, which provide information on the physical parameters of the system. It is therefore necessary to distinguish between differences in muon counts due to real geological features, and those arising from random error. This is crucial in the low-contrast case, where the method can reach the information theoretic limit of what a data source can reveal, even in principle. To this end, methods to analyse information availability in low-contrast muon radiography have been developed, as have means to optimally interpret the available data, both for radiography and for tomography. This includes a method for calculating expectation values of muon flux for a given geological model directly, complementing existing Monte Carlo techniques. A case study, using a model of carbon capture is presented. It is shown that the new data analysis techniques have the potential to approximately double the effective sensitivity of the detectors.
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