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• W2735814571 abstract "The design of efficient monitoring programmes required for the assurance of offshore geological storage requires an understanding of the variability and heterogeneity of marine carbonate chemistry. In the absence of sufficient observational data and for extrapolation both spatially and seasonally, models have a significant role to play. In this study a previously evaluated hydrodynamic-biogeochemical model is used to characterise carbonate chemistry, in particular pH heterogeneity in the vicinity of the sea floor. Using three contrasting regions, the seasonal and short term variability are analysed and criteria that could be considered as indicators of anomalous carbonate chemistry identified. These criteria are then tested by imposing a number of randomised DIC perturbations on the model data, representing a comprehensive range of leakage scenarios. In conclusion optimal criteria and general rules for developing monitoring strategies are identified. Detection criteria will be site specific and vary seasonally and monitoring may be more efficient at periods of low dynamics. Analysis suggests that by using high frequency, sub-hourly monitoring anomalies as small as 0.01 of a pH unit or less may be successfully discriminated from natural variability – thereby allowing detection of small leaks or at distance from a leakage source. Conversely assurance of no leakage would be profound. Detection at deeper sites is likely to be more efficient than at shallow sites where the near bed system is closely coupled to surface processes. Although this study is based on North Sea target sites for geological storage, the model and the general conclusions are relevant to the majority of offshore storage sites lying on the continental shelf." @default.
• W2735814571 created "2017-07-21" @default.
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• W2735814571 date "2017-09-01" @default.
• W2735814571 modified "2023-09-27" @default.
• W2735814571 title "Monitoring of offshore geological carbon storage integrity: Implications of natural variability in the marine system and the assessment of anomaly detection criteria" @default.
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• W2735814571 doi "https://doi.org/10.1016/j.ijggc.2017.06.020" @default.
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