Survey strategies to quantify and optimize detecting probability of a CO2 seep in a varying marine environment

Hvidevold, Hilde Kristine; Alendal, Guttorm; Johannessen, Truls; Ali, Alfatih

doi:10.1016/j.envsoft.2016.06.006

Cited by 17 publications

(21 citation statements)

References 20 publications

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“…These fields represent the input needed in the monitor design framework presented in Hvidevold et al . [] and Frøysa [].…”

Section: Seep Simulationsmentioning

confidence: 99%

“…If for instance two sensors have overlapping detection areas, the efficiency of the layout might not be optimal. This leads to the optimization problem of finding the optimal sensor layout maximizing the detection probability [ Hvidevold et al ., ; Frøysa , ].…”

Section: Consequences For Designing a Monitoring Programmentioning

confidence: 99%

“…Hvidevold et al . [] used one of the scenarios and a simplified map of the North Sea, identifying wells and formations, to optimize locations of chemical sensors. It was shown that placing the sensors successively at the location of highest probability is not necessarily the best option.…”

Section: Introductionmentioning

confidence: 99%

“…The design framework presented in Hvidevold et al . [] allows to quantify the uncertainty when claiming no leakage occurs.…”

Section: Introductionmentioning

confidence: 99%

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Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs

et al. 2016

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Risk‐based monitoring requires quantification of the probability of the design to detect the potentially adverse events. A component in designing the monitoring program will be to predict the varying signal caused by an event, here detection of a gas seep through the seafloor from an unknown location. The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. It is shown that the statistical footprint depends on seep location and that this will have to be accounted for in designing a network of sensors with highest probability of detecting a seep. As a consequence, heterogeneous probabilistic predictions of CO2 footprints should be available to subsea geological CO2 storage projects in order to meet regulations.

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“…These fields represent the input needed in the monitor design framework presented in Hvidevold et al . [] and Frøysa [].…”

Section: Seep Simulationsmentioning

confidence: 99%

Section: Consequences For Designing a Monitoring Programmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The design framework presented in Hvidevold et al . [] allows to quantify the uncertainty when claiming no leakage occurs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs

et al. 2016

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“…Hvidevold et al . [] presented a procedure for optimizing placement of fixed sensors on the seafloor that accounts for footprint anisotropy. The same tracer footprint predictions, consisting of time series in an array of 51 × 51 grid points around the seep location from the GCM simulations presented in Ali et al .…”

Section: Introductionmentioning

confidence: 99%

Cost efficient environmental survey paths for detecting continuous tracer discharges

Alendal

2017

JGR Oceans

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Designing monitoring programs for detecting potential tracer discharges from unknown locations is challenging. The high variability of the environment may camouflage the anticipated anisotropic signal from a discharge, and there are a number of discharge scenarios. Monitoring operations may also be costly, constraining the number of measurements taken. By assuming that a discharge is active, and a prior belief on the most likely seep location, a method that uses Bayes' theorem combined with discharge footprint predictions is used to update the probability map. Measurement locations with highest reduction in the overall probability of a discharge to be active can be identified. The relative cost between reallocating and measurements can be taken into account. Three different strategies are suggested to enable cost efficient paths for autonomous vessels.

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Using Bayes Theorem to Quantify and Reduce Uncertainties when Monitoring Varying Marine Environments for Indications of a Leak

et al. 2017

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Survey strategies to quantify and optimize detecting probability of a CO2 seep in a varying marine environment

Cited by 17 publications

References 20 publications

Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs

Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs

Cost efficient environmental survey paths for detecting continuous tracer discharges

Using Bayes Theorem to Quantify and Reduce Uncertainties when Monitoring Varying Marine Environments for Indications of a Leak

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Survey strategies to quantify and optimize detecting probability of a CO2 seep in a varying marine environment

Cited by 17 publications

References 20 publications

Simulating spatial and temporal varying CO2 signals from sources at the seafloor to help designing risk‐based monitoring programs

Simulating spatial and temporal varying CO2 signals from sources at the seafloor to help designing risk‐based monitoring programs

Cost efficient environmental survey paths for detecting continuous tracer discharges

Using Bayes Theorem to Quantify and Reduce Uncertainties when Monitoring Varying Marine Environments for Indications of a Leak

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Product

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Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs

Simulating spatial and temporal varying CO₂ signals from sources at the seafloor to help designing risk‐based monitoring programs