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SUbsurface CO2 storage - Critical Elements and Superior Strategy

Marine monitoring of carbon capture and storage: Methods, strategy, and potential impacts of excess inorganic carbon in the water columnEditorsAbdirahman M. Omar(UiB/Uni Research/Bjerknes Centre)

Contributing authorsGutlorm Alendal (UiB)

Maribel I. García-Ibáñez (Uni Research/Bjerknes Centre)Truls Johannessen (Uni Research/Bjerknes Centre)

Ingunn Skjelvan (Uni Research/Bjerknes Centre)Evgeniy Yakushev (NIVA)

Elizaveta Protsenko (NIVA)

FME SUCCESS Synthesis report Volume 5

2 Synthesis report from FME SUCCESS

Carbon Capture and Storage (CCS) is con-sidered an essential mitigation strategy in order to reduce anthropogenic CO2 emis-sions. To meet the 2°C target set in the Paris Agreement, decarbonization of the global power sector by the 2030s and the heavy industry sector beyond that is critical. CCS is currently the only option for decar-bonizing the steel, chemical and cement industries.

CCS is a proven method (e.g. at Sleipner, Snøhvit, In Salah, Weyburn, Boundary Dam, Quest). There are remaining techni-cal challenges related to upscaling, howev-er, and cost is a critical factor in large-scale deployment of CCS.

In order to stimulate relevant research, the Norwegian Research Council has estab-lished a scheme of Centers for Environ-ment-friendly Energy Research (FME) to develop expertise and promote innovation by focusing on long-term research in select-ed areas of environment-friendly energy, in-cluding CCS.

The FME SUCCESS centerThe SUCCESS center for SUbsurface CO2 storage was awarded FME status in 2009 and was formally inaugurated on 1 January 2010.

Key to public acceptance and successful deployment of CCS, the FME SUCCESS center focuses on effective and safe stor-age of CO2. To meet the regulatory require-ments for Measurement, Monitoring and Verification (MMV), the SUCCESS center seeks to provide a sound scientific base for CO2 injection, storage and monitoring in or-der to fill gaps in strategic knowledge, and to provide a system for learning and devel-opment of new expertise. Such knowledge is vital in order to ensure conformance (con-cordance between observed and predicted behavior), containment (proving storage performance in terms of security of CO2 retention) and contingency (leakage quan-tification and environmental impacts).

The FME SUCCESS center on CO2 storage

The following objectives were defined in the FME SUCCESS application:• To improve our understanding and ability to quantify reactions and flow in carbon storage.• To develop advanced modeling tools for multiphase flow and reaction.• To investigate the integrity of sealing materials, and test their retention capacity.• To improve our understanding and develop new models for the relationship between satu-

ration, flow and geomechanical response.• To improve our understanding and develop new models for geochemical and geome-

chanical interactions.• To improve our understanding and modeling tools for flow and reaction in faults and frac-

tures.• To test, calibrate and develop new monitoring techniques and instrumentation.• To improve the understanding of shallow marine processes and the ecological impact of

CO2 exposure, and develop marine monitoring methods.• To reduce risk and uncertainties in sub-surface CO2 storage.• To facilitate extensive and high-quality education on CO2 storage.

Field excursion Unis CO2 lab workshop, Svalbard 2012

Marine monitoring of carbon capture and storage 3

The FME SUCCESS center on CO2 storage

One of the strengths of the FME SUCCESS center is its expertise within fundamental, theoretical research, which is internation-ally recognized; the center hence focuses on basic research, interpreting the results of field and laboratory experiments in order to predict the long-term effects of CO2 stor-age. In particular, the center has used the theoretical platform to address critical and relevant scientific issues related to CO2 storage.

Upon inauguration, the SUCCESS center was organized into six scientific work pack-ages and one educational work package

Mid-term evaluationIn 2013, the Norwegian Research Council conducted a mid-term evaluation of the FME centers. The mid-term evaluation of the SUCCESS center concluded that the center needed to undertake major changes in the organization and operational struc-ture to secure integration and industry rel-evance.

Following the recommendations of the mid-term evaluation, the SUCCESS center re-organized the scientific activities into three work packages:

• Work Package 1: Reservoir • Work Package 2: Containment• Work Package 3: Monitoring

An integration Work Package, WP0, was also established for the final two year-pe-riod of the center. WP0 aimed to test and verify new knowledge and methodology developed at the SUCCESS center in con-nection with two case studies. The Skade and Johansen formations were originally chosen as case studies. The Johansen For-mation was later replaced by the Smeaheia project case, which is the selected reservoir candidate for Norwegian full-scale demo project.

Final reportsAs part of the center’s scientific reporting, the center’s partners and board agreed that a set of reports would be written and SUCCESS fall seminar 2015 Norwegian

Petroleum Directorate (NPD)

Brummundal sandstone, SUCCESS fall seminar 2016 field excursion


summarize the major scientific findings and achievements. These reports have been re-ferred to as Long-term Deliverables (LTD).

Knowledge and lessons from the two field pilots, Snøhvit and Sleipner, have been synthesized in separate summary reports (Volume 6 and 7). Lessons from the Long-yearbyen CO2 Lab, which has been an im-portant test site for the SUCCESS center, have been and will be published in dedicat-ed summary volumes of scientific journals.

The case studies on the Smeaheia fault block (deep, confined reservoir) and the Skade Formation (shallow, saline aquifer)

in the North Sea are presented in separate reports in order to demonstrate the value of the results achieved at the SUCCESS cen-ter and associated projects, and determine how they can be applied to better quantify the storage feasibility of untested aquifers. They allow testing of the lessons and knowl-edge from the Snøhvit and Sleipner field pi-lots, and may constrain the range and use of the methods and models developed.

Long-term deliverablesThe LTD reports (5) include the SUCCESS center’s final report on the above-men-tioned deliverables. They aim to synthesize the results and findings of the SUCCESS center, and directly address the objectives of the SUCCESS center (see the figure be-low). The LTD reports cover the following topics:Storage capability (Volume 1)This report summarizes the SUCCESS center’s work on storage capability, which is the ability of a formation to safely store CO2. An important objective of this center has been to identify geological factors and the hydro-geomechanical processes that are most important for determining stor-age capability. The most important factor is whether the storage reservoir is open or closed.

Leakage risks (Volume 2)Summarizing the results from field, experi-mental and theoretical studies of potential leakage mechanisms and their relevance to CO2 storage site risk assessment, this re-port demonstrates that viscous deformation of the shales can play an important role in

their ability to keep CO2 contained and that material properties and their dynamic be-havior in response to the stress introduced by CO2 injection need to be evaluated in order to safeguard operations.

Injectivity (Volume 3)This report presents experimental and com-putational results that have enhanced our understanding of reservoir injectivity, includ-ing a basic understanding of mechanisms, quantification of the expected impact, mod-el calibration and case specific implications. A main outcome is a workflow that includes new computational tools, new geochemical and geomechanical experimental design/data and research-based advice.

Geophysical monitoring (Volume 4)The geophysical monitoring report summa-rizes the SUCCESS center’s work on rock physics related to pore pressure and satu-ration and estimating these two parameters via geophysical monitoring. By estimating their spatiotemporal distribution, we can monitor the migration of injected CO2 and determine whether the containment of stor-age complex is secure.

Marine monitoring (Volume 5)This report synthesizes relevant knowl-edge and data regarding marine monitoring methods and strategies for inorganic car-bon in the water column, based on model-ing and observational work. A cost-effective strategy for a marine monitoring program should optimize the probability of detecting a leak.

The Long term deliverables (LTD) set of reports (5) include the SUCCESS centre final reporting, and they aim at synthesizing results and findings of the SUCCESS centre and relate directly to the objectives of the SUCCESS centre

Christian Hermanrud (SUCCESS funded Prof II position, UiB) with some of his master students


Relevance of workThe collective work of the SUCCESS center addresses various groups of stakeholders and the reporting structure is relevant to dif-ferent communities. The report on storage capability is particularly relevant to storage site selection and Norwegian CO2 storage capacity estimates, based on better con-strained trapping efficiency and immobili-zation potential. The leakage risks report addresses important issues regarding safe operation of CO2 storage and risk manage-ment. The report on injectivity provides valuable knowledge on the planning of CO2 operations and reservoir utilization. Finally, there are two reports on monitoring: the re-port on geophysical monitoring addresses methods for measurement, monitoring and verification (MMV) of the subsurface; while the report on marine monitoring is particu-larly relevant to risk management and miti-gation in the event of leakage to the water column.

Arvid Nøttvedt, Center Manager FME SUCCESS

Ivar Aavatsmark,Scientific Leader FME SUCCESS

Per Aagaard,Scientific Leader FME SUCCESS 2010-2014

Alvar Braathen,Scientific Leader FME SUCCESS 2014-2018

Future work and recommendationsCO2 storage has been successfully demon-strated at Million-tonne scale, but needs to be ramped up to Giga-tonne scale in order to achieve global emissions reductions tar-gets. A shown in the report on Large-scale Storage of CO2 on the Norwegian Shelf, there are no technical showstoppers for ramping up CO2 storage (Tangen at al., 2014). However, ramping up to Giga-tonne scale requires 1) better estimate of storage capacity, 2) better pressure management strategies, and 3) smart methods for con-trolling and optimizing CO2 injection (Nøt-tvedt, A., pers. comm. "Mission innovation workshop", 2017).

Better estimate of storage capacity requires more reliable forecasting of CO2 migration and trapping processes, with range of un-certainties. This, in turn, requires improved physics and chemistry-based understand-ing of CO2 flow and transport processes at multiple scales within heterogenous rock media.

Better pressure management strategies im-ply control on pressure limits at both near-well and reservoir scales and quantification of allowable pressurization. Consequently, better understanding of the effects of stress field, pressure history, reservoir/caprock heterogeneities, including faults and frac-tures, is needed.

Smart methods for controlling and optimiz-ing CO2 injection include effective control and handling of transmissivity, near-well geochemical processes, formation dam-age, etc. Well stimulation and next-gener-ation well technologies need to be demon-strated to enable large-scale CO2 injection.Future advances in CO2 storage will likely occur at the interface between industry and academia and be coupled to the execution of ramp-up CO2 storage projects.


Marine monitoring of carbon capture and storage:

Methods, strategy, and potential impacts of excess inorganic carbon

in the water column

EditorAbdirahman M. Omar (editor)1,3

Contributing authors Guttorm Alendal2, Maribel I. García-Ibáñez3, Truls Johannessen1,3, Ingunn Skjelvan1,3, Evgeniy Yakushev4 and Elizaveta Protsenko4

1 Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway, (UiB)2 Department of Mathematics, University of Bergen, Bergen, Norway, (UiO)

3 Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway, (Uni)4 Norwegian Institute for Water Research, Niva)


Photos and illustrations Marit Hommedal, Ole Jørgen Bratland (Statoil), Helge Hansen, Statoil), Harald Pettersen, (Statoil), Daniel Byeers (ISGS),

Tor de Lange (UiB), Olav Olsen (Aftenposten) and photos/illustration from all of the FME SUCCESS Partners.Idea, layout/design: Per Gunnar Lunde and Charlotte Gannefors Krafft, CMR

Table of contents

The FME SUCCESS center on CO2 storage 2

Executive Summary 8

Introduction 9

Marine monitoring strategy 11

Determination of

excess carbon in the water column 15Seawater carbonate system variables and measurements 15Baseline measurements 17Excess carbon determination using the Cseep method 19

Potential impacts of CCS leakage on the structure and functions of marine communities 20Experimental set-up 20Biological responses to high CO2 exposure 21Best practice on biological monitoring at the SUCCESS centre 22

Conclusions and best practice recommendations 24

References 26


Executive Summary

For CCS (Carbon Capture and Storage) technologies to be classified as a climate change mitigation option, efficient, safe and enduring storage needs to be veri-fied through site-specific monitoring pro-grammes, which is mandated by national and international statutes such as the Nor-wegian Petroleum Activities Act and the EU Emissions Trading Scheme (EU ETS). In the case of offshore geological storage, high spatiotemporal natural variability hin-ders the interpretation of leakage signals above background measurements. It is therefore necessary to establish spatiotem-poral natural variability through baseline studies when designing an efficient moni-toring programme.

Given these complexities, the Geophysical Institute at the University of Bergen, the De-partment of Mathematics at the University of Bergen, Uni Research Climate, and the Norwegian Institute for Water Research have concentrated part of their research on improving the understanding of shallow ma-rine processes and the ecological impact of CO2 exposure, and developing marine monitoring methods and strategies, based on both modelling and observational work at the SUCCESS centre. During the project, the above-mentioned institutions were also involved in other relevant projects that pro-duced new knowledge about the risks asso-ciated with potential exposure of the marine environment to potential leakages of stored CO2 and how to detect such leaks through monitoring.

The CLIMIT-funded CO2 BASE project em-phasized that a proper environmental base-line is a prerequisite for detecting anoma-lies and serves as an assurance against unjustified accusations of adverse environ-mental events. The EU’s FP7 project ECO2,

which investigated Sleipner and Snøhvit, among other sites, studied the connection between subsurface and marine processes. A number of leak scenarios were considered, and the Cseep method was developed. This method was used to optimize deployment of fixed installations on the seabed with the highest probability of detecting potential leaks. Development of this method has continued, applying Bayesian methodology, in the Horizon 2020 project STEMM-CCS and the CLIMIT-funded BayMoDe project.

This report synthesizes relevant knowledge and data acquired during the above-mentioned projects regarding monitoring methods and strategies for inorganic carbon in the water col-umn, based on both modelling and observational work. The following are the most important findings from the activities conducted.

• A cost-efficient strategy for a monitoring programme will optimize the chances of detect-ing a leak.

• Optimization of leak detection needs: (i) a map of probable leak locations and potential rates and topological features, preferably quantifying their internal relative probability at the different sites; (ii) a proper environmental baseline; and (iii) probabilistic footprint predictions of leakages through modelling.

• Monitoring programmes will ensure that real leak alarms are detected, minimizing false positives.

• The use of monitoring techniques that eliminate natural variability from the measure-ments (Cseep method) captured through the baseline may improve the threshold for leak detection; i.e. may lower the degree of anomaly needed for a leak signal to become statistically significant.

• Macrofauna is currently the first taxon choice for baseline monitoring. As the primary bio-logical method for detecting possible leaks, a survey should be performed of the possible spatial-related behavioural responses of benthic megafauna using remote methods in the area overlying the reservoir. This should be done together with a study of the physical and/or chemical properties of bottom water.

"It is ... necessary to establish spatiotemporal natural variability through baseline studies when designing an efficient monitoring programme. "


Introduction

During the past decade (2006–2015), burn-ing of fossil fuel and changes in land use have resulted in an annual emission of 10.3 GT C into the atmosphere. Approxi-mately 25% of this has been absorbed by the ocean (Le Quéré et al., 2016). When CO2 enters the ocean, carbonic acid and eventually protons (H+) are formed, which affects the marine inorganic carbon system. The ocean gradually transforms into a more acidic reservoir, as more CO2 dissolves and the amount of available carbonate is reduced, which is the process known as ocean acidification (OA). At present, there is evidence of changes to the structure and functionality of marine ecosystems due to OA (e.g., Gattuso and Hansson, 2011), and there is thus no doubt that excess CO2 entering the atmosphere and oceans, i.e. CO2 from human perturbation, represents a challenge.

For a decade or so, the CCS (Carbon Cap-ture and Storage) process has been put for-ward as a way of reducing the excess CO2 entering the ocean and atmosphere. The

effectiveness of CCS as a mitigation option (IPCC, 2005; IEA GHG, 2008) depends in part on demonstration of efficient, safe and enduring storage, verified through monitor-ing. In the case of offshore CCS, monitor-ing programmes must be planned in even greater detail than on-shore CCS sites, as marine operations are more expensive. Marine monitoring is challenging due to the spatiotemporal natural variability and the extent of the area that migrating CO2 might leak to from the seabed. An environmental baseline, characterizing natural variability,

is therefore necessary. Primary monitor-ing of the integrity of storage reservoirs will likely be based on seismic techniques that are capable of detecting the evolution of the CO2 plume, and large leakage fluxes (in the order of 103 T CO2). However, smaller, low flux leakages would only be detected near or at the seabed, suggesting a need to monitor the surface seabed and the water column above it in order to achieve com-plete certainty (Blackford et al., 2015).

Different leak scenarios were studied in the EU’s FP7 project ECO2 , distinguish-ing between large-scale flux rates over a large area through a chimney reactivation (max. flux rate of 150 T/day within a 500 m diameter circle), a blow out (100 T/day, 50 m diameter), a leaky well (20 T/day, 1 m diameter) and an elongated conduit (15 T/day, 200x2000 m). Time evolution of these scenarios was delivered as input to a model study on the footprint created by the sce-narios (Alendal et al., 2014). Even though the models used varied greatly, they all in-dicated that the footprint of a leak would be very localized in the vicinity of the leak.

Unsurprisingly, the flux of CO2 governs the maximum concentration and the spatial extent of the footprint. The topology of the leak (dispersed leaks over a large area vs. large flux at a point) and the bubble size dis-tribution of the leakage also influence the footprint.

"When CO2 enters the ocean, carbonic acid and eventually protons (H+) are formed, which affects the marine inorganic carbon system. The ocean gradually transforms into a more acidic reservoir, as more CO2 dissolves and the amount of available carbonate is reduced, which is the process known as ocean acidification."

"Even though the models used varied greatly, they all indicated that the footprint of a leak would be very localized in the vicinity of the leak. "


Proper and reliable predictions regarding how CO2 reaches the seabed are hence important in order to estimate the spatio-temporal footprint of a leak. Strong currents will carry the CO2 cloud over a larger dis-tance in a short period of time, usually also implying higher shear, at least along the seabed, and thus stronger turbulent mixing. The varying direction of the currents also determines the bearing taken by the CO2 cloud. Even though the mean signal will be very low when it moves away from the leak, patches of higher concentration may pass by a location.

In 2010, the Research Council of Norway granted funds for a Centre for Environment-friendly Energy Research (CEER) called SUbsurface CO2 storage – Critical Ele-ments and Superior Strategy (SUCCESS). The main goal of the centre was to address several areas of importance to subsea CO2 storage: storage performance, sealing properties, injection, monitoring and conse-quences for the marine environment. Spe-cifically, the objective of activity five of the centre was to improve the understanding of shallow marine processes and the ecologi-cal impact of CO2 exposure, and to develop marine monitoring methods.

The partners in the centre were also in-volved in several other international and national projects that addressed the devel-opment of measurement procedures and technology for CO2 in marine waters. For in-stance, the GASSNOVA-CO2 Marine, CO2 BASE, and the EU’s FP7 CARBOCHANGE projects involved gathering baseline data in the North Sea in order to document and un-derstand the variations and trends in back-ground CO2 concentrations.

In contrast, the aforementioned EU FP7 project ECO2 aimed to achieve a better understanding of the risks associated with CCS, e.g. the likelihood of leakage from sub-seabed storage, the effects of leakage, and monitoring strategies, using the Sleipner and Snøhvit fields as two of the study sites. The ECO2 project ended in 2015, and its final report presented a framework of best environmental practices to guide the man-agement of offshore CO2 injection and stor-age (Wallmann et al., 2015). The ongoing STEMM-CCS project began in March 2016 and seeks to test CO2 leakage detection, impact assessment, and decision-making support techniques that are currently at Technology Readiness Level (TRL) stages 4–5 and support their progress to a higher TRL and ultimately commercialization .

"Proper and reliable predictions regarding how CO2 reaches the seabed are hence important in order to estimate the spatiotemporal footprint of a leak."

This report synthesizes relevant knowledge and data acquired during the above-men-tioned projects regarding monitoring meth-ods and strategies for inorganic carbon in the water column, based both on modelling and observational work conducted by the Geophysical Institute at the University of Bergen (GFI, UiB), the Department of Math-ematics at the University of Bergen (Math, UiB), Uni Research Climate (UniRC), and the Norwegian Institute for Water Research (NIVA). Monitoring strategies are sum-marized in section two, observations lead-ing to the determination (detection and/or quantification) of excess carbon in the wa-ter column are described in section three, a summary of the potential impact of CCS leakage on the structure and function of marine communities is provided in section four, and conclusions and recommenda-tions are made in section five.

"The main goal of the centre was to address several areas of importance to subsea CO2 storage: storage performance, sealing properties, injection, monitoring and consequences for the marine environment. "


Marine monitoring strategyGeological CO2 storage projects require an adequate monitoring programme in order to demonstrate safe and enduring stor-age, following regulation like the London Convention, OSPAR and EU directives. A three-phase monitoring programme has been suggested, consisting of: 1) a detec-tion phase; 2) a confirmation and location phase; and 3) a leak quantification phase. Some authors suggest a fourth step – im-pact assessment (Blackford et al., 2015).

Work at the SUCCESS centre and on re-lated projects mentioned in this report has focused on the detection phase, where the monitoring programme looks for anomalies in the environment. The major questions are as follows. How can a potential leak-age be detected? Is it possible to quantify the certainty of detecting a leak? In offshore storage projects, the answers to these questions requires us to ask “Where will a leak most likely occur?”, “How will a leak trail materialize in the water column?” and “ Will we be able to distinguish the signal from the background variability?”

The answers to the first two questions, “Where will a leak most likely occur?” and “How will a leak trail materialize in the water column?” are provided through site charac-terization. A map of probable leak locations is an intrinsic part of site characterization. Injection wells are believed to be the most probable leakage pathway, but transport of CO2 within a formation might create new pathways to the surface (Oldenburg and Lewicki, 2006), possibly far from the injec-tion well. The internal relative probability between different areas will dictate where it will be most important to search for leaks.

Leaks following weakness zones in geo-logical structures or old wells might cause high CO2 flux rates far from the injection site. Possible CO2 migration through the overburden reaching the surface may cre-

ate more diffusive leaks over a relatively large area. Leak scenarios will be an intrin-sic component of site characterization and lay the groundwork for the risk and impact assessments that are necessary in order to obtain a CCS permit.

The answer to the third question, “Will we be able distinguish the signal from the background variability?” initially relies on establishing the natural spatiotemporal variability of the storage site through an en-vironmental baseline study. Baseline stud-ies will include environmental parameters such as currents, natural gas seeps and biogeochemical parameters. Long time se-ries will capture natural seasonal variability and long-term trends. In particular, it will be important to capture the expected acidifica-tion caused by the increase in atmospheric CO2 levels (Caldeira and Wickett, 2003). It is therefore necessary to combine histori-cal data and new data collected during site characterization.

It is equally important to understand how to recognise a leak. CO2 seeps shallower than 500 m will rise in the water column as gas bubbles (Alendal and Drange, 2001). De-

pending on the flux rate, this might create individual bubbles, bubble trains or bubble plumes. The dynamics of these regimes dif-fer, with the plume dynamics being the most challenging to model due to the two-way coupling with the surrounding seawater.

In all cases, an increase in the CO2 con-centration is expected near a leak (Black-ford et al., 2010), with subsequent poten-tial environmental impacts that may cause changes to the bottom fauna and pelagic ecosystems, possibly materializing as new occurrences of bacterial mats (Wegener et al., 2008). The spatial extent of the impact-ed area is expected to be limited (Dewar et al., 2013, 2014), but might become severe, depending on the size of the leak and the dilution rate in the water column.

At the SUCCESS centre, we have simulat-ed possible changes to the seasonal vari-ability of biogeochemical parameters in the upper sediment (up to 10 cm), the benthic boundary layer (0.5 m), and the water col-umn in various leakage scenarios using the 1D benthic-pelagic Bottom RedOx Model (BROM) (Yakushev et al., 2017). During these simulations, CO2 was injected just

"The answer to the third question, “Will we be able distinguish the signal from the background variability?” initially relies on establishing the natural spatiotemporal variability of the storage site through an environmental baseline study."


Figure 1: Simulated interannual variability of pH in the water column and at the sediment–water interface for scenarios 1 (a) and 2 (b) using the 1D Bottom RedOx Model (BROM).

a b

above the sediment–water interface, imitat-ing the leakage through a crack or chimney. Four different leakage scenarios were con-sidered: 1. Blowout 1: 26.8 mmolC/sec for 1 year2. Blowout 2: 26.8 mmolC/sec for 4 years3. Chimney reactivation 0.2 mmolC/sec for 1 year4. Leaky well 0.045 mmolC/sec for 1 year

These are based on the scenarios simulat-ed in the ECO2 project [Alendal et al., 2014]. The rates above are purely hypothetical and are based on upper bound estimates. Blowout scenarios correspond to the forma-tion of chimneys and pipes reaching the sea bottom due to sediment mobilization during CO2 injection described in [ Yarushina et al., 2015a; Yarushina et al., 2015b]. The chim-ney reactivation scenario assumes that the CO2 plume reaches the relict seismic chim-ney located near the injection site.

Abandoned wells penetrate geological for-mations in oil and gas exploration areas and thus break through the natural sealing

layers. This results in possible pathways for migration of fluid and volatiles from deep geological formations up to the seabed. This process represents the ‘leaky well’ scenario. However, very little is known about the realistic leakage rates for all of the above-mentioned scenarios, and future research should cast light on the expected leakage rates. Calculations for the leaky well (scenario 4) did not show any significant differences, compared with the baseline state; i.e. a short-lived leakage for a one year at a small rate is not detectable and causes few changes to the natural conditions. Nor does a one order of magnitude increase in the leakage rate expected for chimney reacti-vation considered under scenario 3 have a significant influence on the marine environ-ment. In contrast, the short-lived blowout (scenario 1) and the long-lived blowout (scenario 2) where the leakage rate is four orders of magnitude larger than for the leaky well will result in significant changes to the marine environment. Within the first few centimetres of the sediment, the pH in the water column drops from 6 – the base-line value – to 7, which results in water

acidification (see Fig. 1). Correspondingly, concentrations of calcium carbonate remain low during the whole leakage period, pore-waters are undersaturated with aragonite and calcite.

The leakages expected for blowout sce-narios could create dangerous conditions for the benthic ecosystem with a low pH, which will restrict the carbonate formation. This causes negative effects for the bottom ecosystem: limiting the ability of calcifying organisms to deposit their hard parts, re-ducing the transportation of organic carbon-ate to deep sediments, shifting the balance of the ‘biological carbon pump'.

The main question remains: What level of certainty will be required in order for the monitoring programme to sound the alarm? In order to reduce the cost of mobilizing the resources needed to confirm and locate a leak, it will be preferable to treat data from the monitoring programme in order to quan-tify the certainty of a leak alarm.

Research activities at the SUCCESS centre include studies in close collaboration with other related projects. The leak scenarios

Figure 2: Footprints from leaks, illustrat-ing the depency on location. Figure from Ali et al. (2016)

"... future research should cast light on the expected leakage rates."


(Alendal et al., 2014) and the subsequent predicted footprints of a leak (Ali et al., 2016) from the ECO2 project (Figs. 2 and 3) were used as a basis for studies of optimal layout of fixed sensor installations in the vi-cinity of the Sleipner field (Hvidevold et al. 2015, 2016) (see Fig. 4). The optimal placement of sensors with the highest probability of detecting a leak was determined using a map of wells in the vi-cinity of Sleipner from the Norwegian Petro-leum Directorate (see Figure 4).

The study was expanded on in Hvidevold et al. (2016), where the concerns regarding false alarms were addressed and different methods for treating time series in order to avoid false alarms were assessed. If the average concentration in the measured time series must be above a threshold, this restricts the area monitored by a sensor,

Figure 3: Snapshots of the instant CO2 concentration footprint along the seabed at differ-ent times. The units in the colour bar are lmol kgsw-1. This illustrates the high spatiotem-poral variability anticipated from a footprint of a leak. Figure from Ali et al. (2016).

Figure 4: North Sea map (77 × 77 km) showing the loca-tions of wells (black crosses) and faults (black lines), from the Norwegian Petroleum Directorate web-site. The red circles indicate the optimal locations for one (up-per left), two (upper right), three (lower left) and four (lower right) sensors. Figure from Hvidevold et al. (2015).


Figure 5: Upper left (a) shows the location of a measurement (white cross) and the local probability of it being the location of the leak (colours). The remaining images show the monitored measurement area at the white cross for (b) the average method: the average concentration is above the threshold; (c) the single method: accounts for the relative frequency of concentrations as being above the threshold; and (d) the event method: a single high-concentration event which triggers an alarm. Note that Fig. (d) shows a larger spatial area than (a), (b) and (c). Figure from Hvidevold et al. (2016).

and the time to detect a leak increases. In contrast, the area monitored will expand if a single event of increased concentration is enough to trigger an alarm regarding a potential leak in the area monitored. How-ever, this expanded area must be balanced against the higher rate of false alarms that will likely be the result of the latter method. A method in between counts the relative time the concentration is above a threshold and, in a frequentists’ way, introduces the probability of the presence of a leak. Ali et al. (2016) documented the role of spatial dependency on the footprint and the under-lying simulations used in Hvidevold et al. (2015, 2016). This probabilistic reasoning was sub-sequently expanded on in Alendal et al. (2017a) by turning to the Bayes theorem. This theorem was used to quantify the un-certainties involved and show how Bayes-ian decision theory can be used to balance the cost of false alarms (see Fig. 5). Bayes-ian methodology has also been used to develop three strategies for optimal routing of Automatic Underwater Vehicles (AUV) (Alendal et al., 2017b; Witman, 2017). The threshold applied in these studies was based the stoichiometric approach devel-oped in Botnen et al. (2015), the Cseep trac-er, which allows the elimination of natural variability from CO2 measurements.

" The most critical factor in the detection is the accurate identification of the anomaly in the seawater CO2 concentration produced by the leakage. This is challenging because the ocean exchanges CO2 with both the atmosphere and the biosphere, and contains background CO2 that is hugely variable, especially on shelf and coastal systems."


Determination of excess carbon in the water columnGeochemical methods can be used to de-tect and quantify CO2 leakage in the form of extra carbon dissolved in the water re-sulting either from bubbles that dissolve as they rise or from dissolved CO2 advecting with deep waters. The most critical factor in the detection is the accurate identification of the anomaly in the seawater CO2 con-centration produced by the leakage. This is challenging because the ocean exchanges CO2 with both the atmosphere and the bio-sphere, and contains background CO2 that is hugely variable, especially on shelf and coastal systems (Blackford and Gilbert, 2007).

An important prerequisite for anomaly de-tection is therefore the establishment of a sound baseline. For quantification pur-poses, an estimate of the water volume impacted by the leakage is necessary in addition to determination of the CO2 en-richment. A combination of autonomous in situ measurements and high-sensitivity analysis methods are thus important tools for easy identification of leakage footprints. The following subsections describe the wa-ter column baseline acquired at the SUC-CESS centre and the analytical methods developed. The detection and quantification of leakage CO2, familiarity with seawater carbonate system variables, and measure-ment of these are all important, and will be discussed first.

Seawater carbonate system variables and measurementsInorganic carbon is present in seawater as bicarbonate (HCO3-, 90%), carbonate (CO32-, 9%) and carbon dioxide (CO2 gas + HCO3-, <1%) and shares of these are governed by a set of equilibria called the marine inorganic carbon system, which is described by four measur-able variables: DIC, TA, fCO2, and pH. DIC is the total amount of dissolved inorganic carbon components in the seawater; total alkalinity (TA) describes the ability of the water to neutralize acid, i.e. the buffer capacity of the seawater; fCO2 is the fugacity of CO2; and pH expresses the proton concentration in the seawater.

If two out of these four parameters are measured, in addition to seawater temperature and salinity, the remaining two parameters can be determined (Park, 1969; Dickson et al., 2007). Additional information provided by these calculations is the carbonate concentration and the saturation state of carbonate, Ω. If Ω > 1, the seawater is oversaturated with respect to car-bonate, while a value of Ω < 1 indicates undersaturation of carbonate. There are differences in the precision of each pair of parameters when estimating the carbonate concentration and Ω (see Fig. 6).

Figure 6: Percentage uncertainties in carbonate ion concentration and the saturation state of carbonate (Ω) when estimated using different pairs of input parameters. AT refers to total alkalinity, pCO2 to partial pressure of CO2 and CT to total dissolved inor-ganic carbon.

% u

ncer

tain

ty


However, this view must be combined with consideration of which pair of parameters is the most feasible and cost-efficient. The sampling and instrumentation used to analyze seawater carbonate system parameters follow international standards described in the Guide to Best Practices for Ocean CO2 Measurements (Dickson et al., 2007). Data are collected either by us-ing sensors deployed at certain depths and continuously logging data or by collecting and analyzing discrete samples. A third way of collecting data is using flow-through sys-tems on ships where they measure semi-continuously in unattended operation.

The DIC and TA samples were collected by filling glass bottles with seawater from the depths of interest. The analyses were performed either on the ship or on shore, and in the latter case, the water samples required preservation in order to prevent biological activity that influenced the re-sults. DIC measurements were conducted by first acidifying the water sample, next extracting the CO2 and, then conducting coulometric titration and photometric detec-tion of the CO2. TA analyses were carried out using potentiometric titration with 0.1 N hydrochloric acid. Those measurements were performed using VINDTA 3D and 3S analytical systems (Marianda, Germany) for DIC and TA, which are bench instruments that have been used for many years. The DIC and TA values were calibrated using the Certified Reference Material (CRM) supplied by A. Dickson, Scripps Institution of Oceanography, and the measurement precision was 1 µmol kg-1 for DIC and 1 µmol kg-1 for TA.

While there are no available sensors for measuring the DIC and TA, both pH and pCO2 can be determined in situ using sen-sor technology. One of the most commonly used pH sensors is the SAMI2-pH sensor

(Submersible Autonomous Moored Instru-ment) from Sunburst Sensors, which can be used from surface depth down to 600 m. The SAMI2-pH sensor mixes an indica-tor dye with water in a flow-through cell, and the colour change, which is a function of the amount of CO2 in the water, is de-tected optically. The pH of the water is then calculated. The precision of this sensor is reported to be better than 0.001 pH units, while the accuracy checked against CRMs was ± 0.003.

Semi-continuous in situ pCO2 measure-ments at different depths can be performed based on similar principles as for pH, us-ing a SAMI-CO2 sensor (Sunburst Sensors) with indicator and optical detection. For the SAMI-CO2 sensor, water flows close to a semi-permeable cell filled with indica-tor, which changes colour according on the amount of CO2 diffusing into the cell. The colour change is detected optically, and the precision is reported as being better than 1 µatm, while the accuracy checked against US National Institute of Standards and Technology gas standards was ± 3 µatm.Semi-continuous surface fCO2 can also

be measured using flow-through systems installed on cargo ships and research vessels. These flow-through systems are designed to run unattended and to pro-duce data with an accuracy better than ± 2 µatm. The fCO2 instrument is provided by General Oceanics, and uses a shower head equilibrator and a non-dispersive in-frared (NDIR) CO2/H2O-spectrophotometer (LI-COR 6262, LI-COR Biosciences), which measures the CO2 concentration in air in equilibrium with a continuous stream of sur-face water (Pierrot et al., 2009). The instru-ment was calibrated using three traceable gas standards supplied by the US National Oceanic and Atmospheric Administration (NOAA).

A detailed list of inorganic carbon variables measured at the SUCCESS centre and their specifications can be found in Table 1.As mentioned previously, it is enough to know two of the marine inorganic carbon system parameters in order to calculate the other two.

Here, we used the DIC and TA together with temperature, depth (pressure), salinity, phosphate, and silicic acid in the chemi-cal speciation equilibrium model CO2SYS (Pierrot et al., 2006) to calculate the pH and fCO2, in addition to aragonite and calcite saturation (ΩAr and ΩCa, respectively). For this calculation, we used the carbon-ate system constants from Mehrbach et al. (1973), modified by Dickson and Millero (1987), and the total pH scale (pHT) using the constant for HSO4- from Dickson (1990) at 25°C. The calcium concentration ([Ca2+]) was assumed to be proportional to salinity by Mucci (1983), and corrected for pressure following Ingle (1975).

"Data are collected either by using sensors deployed at certain depths and continuously logging data or by collecting and analyzing discrete samples."

"..... it is enough to know two of the marine inorganic carbon system parameters in order to calculate the other two. "


Table 1 Inorganic carbon variables measured at the SUCCESS centre and their specifica-tions.

Parameter Method of analysis

Response time

Precision Accuracy Calibration method

Reference

DIC V I N D T A 3D (Mari-anda, Ger-many)

15 min. per sample

1.0 µmol kg-1

1.0 µmol kg-1

C o m p a r i -son with CRM* (A. D i c k s o n , Scripps)

Dickson et al. (2007)

TA V I N D T A 3S (Mari-anda, Ger-many)

20 min. per sample

1.0 µmol kg-1

1.0 µmol kg-1

C o m p a r i -son with CRM* (A. D i c k s o n , Scripps)

Dickson et al. (2007)

pH C o l o r i m -etry

3 min. < 0.001 ± 0.003 Using DIC, TA, and CRM*

DeGrand-pre et al. (1995)

fCO2 C o l o r i m -etry

5 min. < 1 µatm ± 3 µatm Using DIC, TA, and CRM*

DeGrand-pre et al. (1995)

fCO2 Equilibra-tion, NDIR

3 min. ± 2 µatm 3–4 stan-dard gas-ses

Pierrot et al. (2009)

* CRM= Certified Reference Material

Baseline measurements The surface seawater variability of CO2 and pH in the North Sea have been studied using mainly continuous fCO2 measure-ments from cargo ships (Trans Carrier and Nuka Arctica) (Omar et al., 2010, 2016). The findings show that in the seasonally-stratified North Sea, including the western Norwegian fjords, carbon is taken up by phytoplankton in the surface and respired in the subsurface layer. The area is there-fore undersaturated with respect to CO2 throughout the year, driving a flux of CO2 into the ocean.

There are also great seasonal variations (Table 2) driven by (in descending order) biological activity and mixing, temperature changes, and air–sea CO2 exchange. The interannual variations are also significant and their magnitude seems to be depen-dent on the area and the season. In gen-eral, interannual variations are enhanced in the southern North Sea and during the summer/spring. The long-term trends are still poorly quantified due to a lack of proper long time series, but low-resolution repeat-ed measurements published indicate that the increase in atmospheric CO2 is driving

a secular increase in the CO2 in surface waters of the North Sea (Omar et al., 2010; Clargo et al., 2015). This is supported by high-resolution time series data for the period 2004–2014 acquired by the authors (Omar et al., 2017 in prep.). Moreover, Salt et al. (2013) reported that the CO2 system in the North Sea responds to external and internal expressions of the North Atlantic Oscillation index. All of the surface CO2 data from the container ships (Trans Carrier and Nuka Arctica) are available in the SOCAT database (www.socta.info).

Table 2 Summary of mean values and variability in the Northern North Sea (data from Glodap v2; Key et al., 2015; Olsen et al., 2016).

Parameter Surface (≤10 m) Bottom (≤ 50 m above bottom depth)

Summer Winter Summer WinterDIC(µmol kg-1)

2 045(1927–2128)

2 103(2039–2132)

2 140(2074–2209)

2 136(2078–2210)

TA(µmol kg-1)

2 293(2099–2357)

2 291(2235–2320)

2 321(2290–2358)

2 305(2280–2339)

pHT25* 7.961(7.830–8.147)

7.845(7.787–7.918)

7.813(7.677–7.976)

7.799(7.602–7.895)

fCO2(µatm)

288.5(184.6–360–5)

358.5(296.3–397.9)

373.8(284.3–539.0)

404.8(334.1–631.9)

T(ºC)

11.91(6.97–17.80)

8.70(5.12–12.50)

7.91(6.00–17.30)

8.10(5.91–12.50)

S 33.996(22.733–35.379)

34.445(31.800–35.353)

35.012(31.821–35.373)

35.032(32.699–35.354)

* pHTis indicates pH at total scale and 25 ºC.

"In general, interannual variations are enhanced in the southern North Sea and during the summer/spring."


Background CO2 variations in the water col-umn were studied using discrete CTD sam-pling from three locations: the area around Sleipner, the Jan Mayen natural venting fields (JMVF) in the Norwegian Sea, and in Isfjorden in Svalbard. The first two areas were surveyed during a summer expedition on board the research vessel G. O. Sars as part of the EU’s ECO2 project. Carbon measurements during the first leg of the expedition were performed in order to map the background variations on the Sleipner subsea CO2 storage complex. Seawater samples taken from CTD bottles at 20 sta-tions were analysed for DIC, TA, and pH using the methods described in section 2.1. Preliminary analyses of these data indicate that the observed variability of background concentrations is governed by freshwater dilution and biology.

At the JMVF, carbon measurements were performed in order to study the effect of the venting CO2 on seawater carbon concen-trations. The DIC, TA, and nutrients were ascertained at 10 CTD stations and a few

samples were taken with a ROV from the ‘chimney’ and adjacent bottom water. The latter samples showed extremely high DIC values (>3 000 µmol kg-1). Higher up in the water column, the DIC and TA concentra-tions measured were mainly impacted by water mass mixing and biological activity, although the DIC concentrations around the vents appeared to be slightly higher than at the reference station. A new sensitive tracer for CO2 seepage was developed (Cseep) and successfully tested in order to quantify the extent of CO2 enrichment due to the venting of CO2 (Botnen et al., 2015). There is a brief discussion of the Cseep tracer, which ascer-tains the impact of venting-related CO2 on water chemistry, in the following section.

High-frequency in situ pH measurements have been attempted on several occasions, mainly as part of the testing of newly-pur-chased SAMI2-pH and SAMI-CO2 sensors. The SAMI2-pH sensor was deployed to the bottom water for 12 hours during the 2012 G. O. Sars expedition in the North Sea. The sensor was attached to a bottom lander,

Figure 7: Half-hourly bottom water pH data (above) recorded by a SAMI2-pH sensor attached to a bottom lander (left) in the North Sea in June 2012. Hourly pCO2 data in uatm (below) recorded by a SAMI-CO2 sensor (right) in the extreme environment of the Red Sea, October 2015–Oc-tober 2016.

which was submerged to 100 m depth for 12 hours. The half-hourly pH data recorded by the sensor revealed significant short-term variations, with a magnitude of 0.3 pH units, which is nearly 50% of the sea-sonal variation in the bottom water over the Sleipner storage complex. Such high short-term background variation illustrates the huge challenge involved in identifying leakage CO2 using simple concentration measurements.

The SAMI sensors were also deployed in other oceanic environments that are more extreme than the North Sea (see left photo below). In 2014–2015, a one-year deep mooring deployment of the sensors was carried out at 800 m depth in the coastal Red Sea (19.720°N, 37.395°E). A SAMI-CO2 sensor was placed on the upper part of the mooring (34–37 m) and acquired high-quality hourly pCO2 data throughout the year despite the extreme Red Sea environ-ment, with high temperatures, high salinity, and heavy biofouling (see Fig. 7).


Another one-year deployment of SAMI pH and CO2 sensors was carried out on a 100 m depth mooring in the Arctic environ-ment of Dicksonfjorden, Svalbard (October 2016–September 2017). Preliminary quality checks showed that the sensors worked satisfactorily during the deployment, and the resulting data is expected to yield in-sights into the natural variability of the ma-rine inorganic system related to changes in sea ice.

Excess carbon determination using the Cseep methodThe CO2 content of seawater is highly dy-namic due to natural processes, and varies between seasons and from year to year. It is therefore challenging to quantify the natural variability of the near-seabed CO2 system. In order to address and minimize this prob-lem, we customized an existing technique that was designed to estimate the oceanic uptake of excess CO2 from the atmosphere in order to ascertain the amount of excess CO2 originating from a submarine leak. The new Cseep tracer separates the natural vari-ability from the leakage signal (Botnen et al., 2015) based on the back-calculation technique used to estimate the oceanic up-take of atmospheric CO2 of anthropogenic origin (e.g. Gruber et al., 1996).

The Cseep method is based on the fact that biological production of CO2 is always as-sociated with a certain amount of oxygen consumption and nutrient release while CO2 leakage has no specific effect on oxygen and nutrient levels in ambient bot-tom waters. The sensitive Cseep tracer was originally computed using discrete sampling from natural submarine vents in the Norwe-

gian Sea and highly-accurate desktop in-strumentation for DIC and TA (Botnen et al., 2015). The concept proved to be successful in determining the amount of excess CO2 that entered the seawater from the subma-rine CO2 vents studied. However, the com-bination of discrete sampling and desktop instrumentation is resource-intensive and time-consuming, which means that it is not ideal for monitoring purposes.

In order to optimize the method for vari-ables that can be measured at high fre-quency and in an autonomous mode, we are assessing the effect of variables on the performance of the method by computing the Cseep tracer twice: first using discrete DIC and TA, and second using pCO2 and pH (García-Ibáñez et al., 2017). The results show that the Cseep tracer increases the sig-nal to noise ratios ten times, compared to the raw measurement data, regardless of the variables used. Cseep findings can hence be automated using in situ sensor-based measurements of pCO2, pH, and dissolved oxygen, together with algorithms that com-pute Cseep. Baseline and monitoring surveys should thus seek to measure pCO2, pH, sa-linity and oxygen, and apply these data in order to determine the concentration of the Cseep tracer in ambient bottom waters above the storage complex, which should cluster at values close to zero prior to the onset of the storage operation.

Sources of uncertainty when computing the Cseep tracer are changes in the air–sea CO2 fluxes, changes in the Redfield ratios used to estimate the biologically-mediated changes in CO2, and changes in water masses between the reference station and the area monitored. Assessment of the changes in air-sea CO2 fluxes during a monitoring expedition has determined that they are smaller than a few ppm and can therefore be disregarded. The uptake of anthropogenic CO2 via the air–sea in-terface induces a continuous increase in background CO2. Measurements at the ref-erence station away from the storage site can be used to assess this effect during the operational phase.

Evaluation of the spatiotemporal changes in the Redfield ratios in the Sleipner area leads to an estimated Cseep tracer uncertain-ty of 20 ppm. Assessment of the changes in water masses between the reference station and the area monitored covers both changes in salinity/TA and changes in the Redfield ratios. Changes in salinity/TA caused by differences in the water mass distribution lead to poor detection of leak-age signals. Water mass characterization is therefore a critical factor when choosing the location of a reference station.

The Cseep method can be a powerful tech-nique if automated measurements can be performed from mobile platforms. There is therefore a need for instrumentation for continuous monitoring of the marine inor-ganic carbon system. Accurate sensors for continuous monitoring of the four measur-able variables of the marine inorganic car-bon system (DIC, TA, fCO2 and pH) are be-ing developed within the framework of the EU’s STEMM-CCS project.

The successful development of such in-strumentation will lead to more accurate separation between natural variability and leakage signals in the marine inorganic carbon system. This will improve the detec-tion threshold of programmes to monitor offshore CCS storage sites.

"The CO2 content of seawater is highly dynamic due to natural processes, and varies between seasons and from year to year."

"The Cseep method can be a powerful technique if automated measurements can be performed from mobile platforms."


Potential impacts of CCS leakage on the structure and functions of marine communitiesThe biological studies in the North Sea within the frames of the SUCCESS centre were devised in conjunction with similar studies in the FP7 ECO2 project, which are presented in detail on the project’s website (http://www.eco2-project.eu/).

A variety of methods and approaches have been used to assess the potential impacts of CCS leakage on the structure and func-tion of marine communities, allowing us to identify generic patterns and paradigms that are independent of the recognised, method-specific weakness associated with single approach studies. Tightly-controlled mesocosm-based studies allow us to dem-onstrate cause and effect between specific environmental drivers (such as pH, hypoxia and salinity) and key biological responses. A large mesocosm facility has been used to examine the potential impacts of CCS leak-age on typical Northern European soft sedi-ment biological communities and process-es. Concurrently with these experiments, there have been field studies of the eco-systems, communities, species and their functions. By comparing mesocosm results with observations from field sites which are naturally exposed to elevated levels of CO2, we can determine if experimental re-sponses are seen in more complex, natural, marine systems.

Experimental set-up A high CO2 exposure system was built at the NIVA marine research station in Sol-bergstrand in Oslofjorden in Norway. It was used to perform an exposure experiment (2012/2013) where 5 CO2 concentration

treatments were used (control (400 ppm), 1 000, 2 000, 5 000 and 20 000 ppm of CO2) to investigate the impacts of a leakage from a CCS injection site or pipeline on benthic communities and processes. Sampling took place after 2 and 20 weeks of exposure, in order to identify the potential short and medium-term impacts.

The severity and speed of impact on the diversity and structure of the micro, meio and macrobenthic communities were es-tablished. Moreover, the impact on benthic ecosystem processes was also determined at each time point by measuring changes in bioturbation activity (i.e. mixing of sediment particulates by burrowing infauna), bioir-rigation (i.e. flushing of benthic sediment by burrowing fauna through burrow venti-lation), and community respiration, which were all expected to reflect the physiologi-cal impact of environmental stressors.

A variety of biogeochemical parameters and processes were also measured in order to identify potential changes in sedimentary nutrient and organic matter cycling. These included sedimentary pH profiles, fluxes of nutrients (PO4, NH4, SiO4, NO2, NO3), and the rate of ammonia oxidation in the seawater and sediment in connection with experimental treatments. Seawater total al-kalinity, total inorganic carbon, temperature, salinity, seawater and sedimentary pH were

Figure 8: Lines in the benthic mesocosm system at NIVA during the acclimatiza-tion period.


Figure 9: Vertical distributions of pH, redox potential, alkalinity, phosphate, nitrate+nitrite, ammonia, manganese (II) , iron (II), DIC, ΩAr and ΩCa in the sediment porewaters for control (top) and hypoxic (bottom) water. The sediment depth is given in cm.

also monitored. Sedimentary properties, in-cluding particle size distributions and poros-ity, were also measured.

Another mesocosm experiment was de-vised to assess the short-term impact of the release of hypoxic brine from formation waters during a sub-seabed CO2 injection. The high-CO2 mesocosm system was thus modified to accommodate this work, and this experiment ran at the end of the sum-mer of 2013. As we were interested in the impact of hypoxic brine, we also investigat-ed the individual effects of hypoxia and high salinity on benthic biota and processes in order to disentangle the potential contribu-tions of each stressor to potential changes in the benthos.

A fourth impact scenario was considered, with simulation of tidal flushing of a brine plume by standard seawater. This experi-ment therefore gauged five experimental treatments: control, high salinity (48 g/l NaCl), hypoxia (1.4 g/l O2), mixed (48 g/l NaCl and 1.4 g/l O2), and tidal (see sec-tion 2.1.2 for details). These exposures lasted for 2 weeks. In order to assess the impact on the benthic community structure and processes, the same end points were measured as for the high-CO2 experiment. Additional measurements were carried out for sedimentary sulphide and redox profiles.

Biological responses to high CO2 exposureThe studies performed by comparing ex-perimental data and observations on the natural seeps near Panarea Island showed that the effect of CO2 seabed emission was clearly visible on porewater chemistry in the form of pH reduction, increase of DIC and alkalinity, and enhanced chemical weather-ing (high concentration of iron, manganese and silicate) along the sediment profile of CO2-impacted sites. The total organic car-bon (TOC) and total nitrogen content (TN) in the sediment and C:N ratios did not differ between the seep and control areas. The following effects on the biological charac-teristics are described below (Berge, 2015).

Macrofauna. Although diversity did not differ, the macrofauna composition at CO2 seep sites differed from the reference site based on the occurrence of more oligo-chaetes and amphipods and less poly-chaetes and gastropods at the seep sites.

Grazing marks by the sea urchin Paracen-trotus lividus were less abundant at the impacted sites, compared to the reference site. Grazing marks by the fish Sarpa salpa were more abundant at the impacted sites. Meanwhile, the macrofauna density was the lowest in the event of weak CO2 supply compared with high CO2 and the reference site.

Meiofauna densities were significantly higher in the control sediments at the ref-erence site compared to the CO2 seep, while the opposite was true in the seagrass shoots. Where CO2 seepage occurred, meiofauna densities were highest in the first 2 cm of the sediment and showed a steep decline with depth. At the control site, there was a more gradual decline in densi-ties with depth. Nematode species richness was significantly lower in the CO2-impacted sites, compared to the non-impacted sites. In the seagrass (leaves and shoots), no sig-nificant seepage-related differences were detected in the meiofauna taxa, copepod species and nematode species assem-blages.

Microphytobenthos/Bacteria showed the consistently highest densities at high-CO2 sites; about four times higher than the abun-dance observed at the reference site. In the water column and on the seagrass leaves, there were no significant differences in the bacterial community structure in the sites investigated. Conversely, bacterial com-munity analyses of recovered sediments showed differences in the CO2-impacted sites and the reference site without seep-age. The results also provided evidence of a reduction in the bacterial diversity in seep sites compared to the background site.

The results from planktonic communities indicate that natural CO2 emissions at the sites investigated do not appear to have any clear influence on the phytoplankton and microzooplankton communities.


Best practice on biological monitoring at the SUCCESS centreResponses to CO2 leakage can basically be broken down into those that act at the level of the individual and those that act at the level of the community. Individual biomark-ers/bioindicators can be behavioural (e.g. animals coming to the surface) or physio-logical/biochemical. At the community level, there are 3–6 main potential response can-didates, depending on how different types

of organisms are classified by taxonomy and size: bacteria/Archaea, meiofauna/nanobenthos, and macrofauna/megafauna. Macrofaunal communities have long been used by industry and regulators as an ef-fective environmental monitoring tool for a variety of potential stressors in the marine environment and are also an agreed tool pursuant to the EU’s Water Framework Di-rective.

Macrofauna are an appropriate tool for de-tecting the effects of stressors related to or-ganic enrichment and reduced oxygen con-centrations in bottom water, but this group does not seem to be optimal for detecting the effects of industrial effluents like metal effluents (Oug, 2013).

Although it has been shown that bacteria, meiofauna, and macrofauna communities can be affected by CO2 under certain ex-perimental conditions, these effects cannot be directly used for the purposes of detect-ing and monitoring CO2 leakage. There will also be a question of the objective of the monitoring.

There is a considerable difference between the methods that are suitable for detecting a leak in the first place and methods that are suitable for monitoring the effects of a leak in space and time after it has been detected or before a potential leak has taken place (baseline survey).

Ana M Queirós, benthic ecologist working during the mesocosm experiments (also funded by the FP7 project ECO2)

"In order to be effective, it should be possible to apply the detection method for monitoring to large areas over a relatively short period of time."


In order to be effective, it should be possible to apply the detection method for monitor-ing to large areas over a relatively short pe-riod of time. The method should not require the taking of samples for later treatment. In most cases, detailed physical sampling for species identification will therefore not be a practical method for detecting a leakage, but can be a useful and relevant tool for more detailed mapping of the effects after a leakage has been identified by other means. Modelling indicates that the spatial footprint of a CO2 leak covers a small area. However, this also implies that it is difficult to locate

" It is suggested that the combination of surveying physical and chemical properties and biological responses of megafauna (remote methods) is the best method for detecting a new leak."

a leak. The methods for characterization of biological communities are time-consuming and expensive (especially as regards mac-ro, meio and microbiota) and the results are only available after some delay, depending on the sample treatment time.

The behavioural responses of megafauna may be detected more easily, however, if good automatic methods are available, which does not appear to be the case (see above). Physical and chemical properties, like acoustics and pH measurements, are instant measurements that are more spe-

cific to CO2 and can be applied over rela-tively large areas (surveying along paral-lel lines) with less effort than using most biological methods. It is suggested that the combination of surveying physical and chemical properties and biological respons-es of megafauna (remote methods) is the best method for detecting a new leak. One should nonetheless bear in mind that traces of emerging megafauna tend to disappear over time due to degradation, at least in cases where the end point of the behav-ioural response is mortality.

The following recommendations made in the FP7 ECO2 report (Berge, 2015) thus remain relevant to biological monitoring.

1. Biological baseline monitoring. Macro fauna is at present the first taxon choice for baseline monitoring. Meio and microbiota should not be completely omitted, however, but should be included if this is consistent with the objective of collecting sufficient data in order to be able to design a comprehensive programme for monitoring the effects of an observed leak and recovery at a later stage (see below). The baseline monitoring should cover all of the main types of sediment areas (sand, clay, etc.) overlaying the reservoir and should encompass typical depth intervals. The orientation of the station network should depend on the shape of the reservoir and possible weak zones in the sedimentary overburden (some sort of grid is most relevant). Expected dispersal pat-terns for leaks and benthic conditions should also be taken into consideration.

2. Detecting a new leak. As the primary method for detecting possible leaks, a survey of possible spatial-related behavioural responses of benthic megafauna should be con-ducted using remote methods in the area overlying the reservoir. This should include the physical and/or chemical properties of bottom water. If a leak is suspected, the area should be monitored using physical and/or chemical and biological (megafauna) methods in greater detail, in order to locate the epicentre of the leak. When the epicen-tre is identified, a more detailed monitoring programme should be designed in order to identify the effects.


Conclusions and best practice recommendationsA cost-efficient strategy for a monitoring pro-gramme will initially seek to detect anoma-lies using a minimum number of techniques and measurements across a wide area (Blackford et al., 2015). The monitoring pro-gramme will seek to optimize the probability of detecting a leak. Three main components are necessary in order to optimize the prob-ability of detecting a leak (Ali et al., 2016; Greenwood et al., 2015; Hvidevold et al., 2015): (i) a map of probable leak locations and potential rates and topological features, preferably quantifying the internal relative probability between the different sites; (ii) a proper environmental baseline; and (iii) probabilistic footprint predictions of a leak-age achieved through modelling. Once an anomaly is detected, statistical methods will help design monitoring programmes that ensure that leak alarms are real, minimiz-ing the incidence of false positives (Alendal et al., 2017a), in accordance with relevant national/international regulation.

The use of monitoring techniques that elimi-nate natural variability (captured through the baseline) from measurements (Botnen et al., 2015; García-Ibáñez et al., 2017) may improve the threshold for leak detection, i.e. may lower the requirements regarding the degree of anomaly in order for a leak signal to become statistically significant.

There is a need to align marine monitoring research with other activities. Subsurface risk assessments provide important input in order to establish monitoring programmes, which should combine subsurface and ma-rine methods in a cost-efficient way and in accordance with regulation. The legislative constraints will determine which risk as-sessments are needed and how monitor-

ing programmes are designed. Experts in the fields of natural sciences/technology and social sciences, and policymakers and legislators must work together to establish a balance between the need for assurance and the cost and feasibility of a monitoring programme.

The transport and storage part of the sub-sea CCS chain needs to be viewed in a broader perspective, e.g. considering the implementation of marine spatial planning in for example the EU and Norway . Even though they might constrain CCS imple-mentation, other marine monitoring and survey activities can serve as an aid in ob-taining the necessary environmental base-line. In addition, costs can be reduced by collaborating with such programmes, e.g.

MAREANO, ICOS, etc. In short, the follow-ing are required.

• Align subsurface monitoring and risk assessments with marine monitoring and potential impact.

• Study the relationship between legisla-tion and monitoring/risk assessments.

• Align CCS marine research with other activities. For example, in order to ob-tain a baseline in collaboration with other marine monitoring programmes like ICOS, MAREANO, INTAROS, SO-CAT, etc.

• Align CCS with ecosystem-based ma-rine management as a result of IOC implementation, for example in the EU and Norway.

"There is a need to align marine monitoring research with other activities. Subsurface risk assessments provide important input in order to establish monitoring programmes, which should combine subsurface and marine methods in a cost-efficient way and in accordance with regulation."


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Postal AddressFME-SUCCESS

Christian Michelsen Research ASP.O. Box 6031

NO-5892 Bergen, Norway

Visiting AddressChristian Michelsen Research AS

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www.fme-success.no

Contributing partners 2010 – 2018

This work was funded by the FME SUCCESS center for CO2 storage under grant no. 193825/S60 from the Research Council of Norway.

The FME SUCCESS center is a consortium of partners from industry and science, hosted by Christian Michelsen Research AS.

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