Coastal monitoring strategy for geochemical detection of fugitive CO2 seeps from the seabed

Greenwood, Jim; Craig, Peter D.; Hardman-Mountford, Nick J.

doi:10.1016/j.ijggc.2015.05.010

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…Simulated leaks have been used to predict footprints subsequently being used to optimize sensor layout [61][62][63][64][65]. They used a threshold that was based on the Cseep method [66] that is dependent on measurements of different biochemical tracers.…”

Section: Detectable Area Vs Detection Probabilitymentioning

confidence: 99%

Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges

et al. 2020

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The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers from unknown locations, large areas will need to be covered with limited resources. To increase the detectability of marine gas seepage we propose a deep probabilistic learning algorithm, a Bayesian Convolutional Neural Network (BCNN), to classify time series of measurements. The BCNN will classify time series to belong to a leak/no-leak situation, including classification uncertainty. The latter is important for decision makers who must decide to initiate costly confirmation surveys and, hence, would like to avoid false positives. Results from a transport model are used for the learning process of the BCNN and the task is to distinguish the signal from a leak hidden within the natural variability. We show that the BCNN classifies time series arising from leaks with high accuracy and estimates its associated uncertainty. We combine the output of the BCNN model, the posterior predictive distribution, with a Bayesian decision rule showcasing how the framework can be used in practice to make optimal decisions based on a given cost function.

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Section: Detectable Area Vs Detection Probabilitymentioning

confidence: 99%

Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges

et al. 2020

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“…Building on knowledge of leak morphology, natural variability and anomaly criteria, models allow us to devise the most cost-efficient deployment of sensors to maximise detection. By quantifying how water movement impacts dispersion of CO 2 plumes, models can determine the minimum number of sensors and their optimal locations [28,29,30] , or the optimal deployment pathway of Autonomous Underwater Vehicles (AUVs) to maximise the likelihood of detection using Bayesian techniques [31] (Fig 4). Research is underway to develop machine learning techniques [32], inverse methods [33] and "greedy set" algorithms [34] to further optimise survey design.…”

Section: Optimizing Sensor Deployment and Locating Leaksmentioning

confidence: 99%

Ensuring Efficient and Robust Offshore Storage – The Role of Marine System Modelling

et al. 2019

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This paper describes the utility of developing marine system models to aid the efficient and regulatory compliant development of offshore carbon storage, maximising containment assurance by well-planned monitoring strategies. Using examples from several model systems, we show that marine models allow us to characterize the chemical perturbations arising from hypothetical release scenarios whilst concurrently quantifying the natural variability of the system with respect to the same chemical signatures. Consequently models can identify a range of potential leakage anomaly detection criteria, identifying the most sensitive discriminators applicable to a given site or season. Further, using models as in-silico testbeds we can devise the most cost-efficient deployment of sensors to maximise detection of CO 2 leakage. Modelling studies can also contribute to the required risk assessments, by quantifying potential impact from hypothetical release scenarios. Finally, given this demonstrable potential we discuss the challenges to ensuring model systems are available, fit for purpose and transferable to CCS operations across the globe.

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“…Field work in which realistic releases are investigated is difficult, expensive and risks harm to the environment. The use of numerical models provides a mechanism by which multiple scenarios can be assessed whilst minimising cost and environmental impacts (Blackford et al, 2008(Blackford et al, , 2013(Blackford et al, , 2020Blackford and Gilbert, 2007;Greenwood et al, 2015;Hvidevold et al, 2015;Phelps et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the rate of transfer of CO2 across the air-sea interface is dependent on the wind speed (Blomquist et al, 2017;Nightingale et al, 2000). Oleynik et al, (2020), Hvidevold et al (2015) and Greenwood et al (2015) have investigated optimal approaches to monitoring for leakage based on the number of instruments available and the sensitivity of those instruments to changes in a number of parameters (particle density, CO2 concentration) and the number of potential release sites. Hvidevold et al (2015) approached the problem of optimal sensor distribution on the seabed from the basis of the probability of a release occurring within a defined region, based on probable pathways to release from a site in the North Sea, where locations in the vicinity of wells and faults are assumed to have a higher probability of a release occurring.…”

Section: Introductionmentioning

confidence: 99%

Optimising environmental monitoring for carbon dioxide sequestered offshore

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Torres

et al. 2021

International Journal of Greenhouse Gas Control

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Carbon Capture and Storage (CCS) provides a mechanism by which CO2 can be removed from the atmosphere and stored in reservoirs. Regulations and stakeholder assurance require monitoring to show storage is robust. The marine environment is heterogeneous and dynamic, and baselines are extremely variable. Hence, distinguishing anomalous CO2 from natural variability is challenging. Monitoring schemes must be designed to identify releases early and with certainty, whilst being cost effective. A key question is how to deploy the smallest number of sensors to ensure effective monitoring?We approached this problem through a 3D hydrodynamic model (FVCOM) coupled to a carbonate system. The unstructured grid resolution ranges from 0.5 km to 3 m and simulates seabed release scenarios ranging from 3 t d− 1 to 300 t d− 1 using the Goldeneye complex as an exemplar test bed. This configuration allows us to characterise and analyse the fate of CO2 in the water column, with the spatial and temporal CO2 patterns shown to be affected by both tides and seasonal mixing/stratification.A weighted greedy set algorithm is used to identify the positions within the model domain which yield the greatest combined coverage for the smallest number of sampling stations, further limited by selecting only a feasible number of sample sites. The weighted greedy set algorithm incorporates the effect of the variable grid spacing as well as the proximity of the sample locations to the Goldeneye complex. The weighted greedy set can identify releases sooner, with a stronger signal than a regular sampling approach.

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Coastal monitoring strategy for geochemical detection of fugitive CO2 seeps from the seabed

Cited by 18 publications

References 18 publications

Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges

Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges

Ensuring Efficient and Robust Offshore Storage – The Role of Marine System Modelling

Optimising environmental monitoring for carbon dioxide sequestered offshore

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