Seismic Hazard and Risk Assessment
Groningen Field
update for Production Profile
GTS - raming 2020

March 2020

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Errata:
On 13th March NAM submitted the report “Seismic Hazard and Risk Assessment Groningen Field
update for Production Profile GTS - raming 2020” to the Minister of Economic Affairs and Climate
Policy. The report was shared by the Minister with SodM for an initial check. In particular, SodM
checked whether the assessment report was complete and all tables, graphs and figures requested in
the expectation letter (verwachtingenbrief) had been included. SodM identified a number of sections
in the report that lacked clarity and requested a number of additional tables and graphs to be added.
As a result, the following amendments were made to the report:







The tables with the annual probabilities for occurrence of earthquakes exceeding a set
magnitude (Tab. 5.2a and 5.2b) were further refined.
The tables with the annual probabilities for occurrence of earthquakes exceeding a set
magnitude (Tab. 5.2a and 5.2b) provided these probabilities for gas-years. Similar tables for
calendar years have been added in appendix H.
Reconciliation of the number of buildings in the different safety groups relative to the norm
between HRA GTS-raming 2019 and HRA GTS-raming 2020 has in the main text been
presented in Sanky diagrams (Fig. 7.12, 7.13 and 7.14). The information contained in these
figures is also presented in tables (for both operational strategies) in Appendix J.
Text in chapter 7 on risk metrics has been edited to improve clarity. A reference to the report
on the fragility and consequence model (v6) has been added.

© EPEP202003201727
Dit rapport is een weerslag van een voortdurend studie- en dataverzamelingsprogramma en bevat de stand
der kennis van januari 2020. Het copyright van dit rapport ligt bij de Nederlandse Aardolie Maatschappij B.V.
Het copyright van de onderliggende studies berust bij de respectievelijke auteurs. Dit rapport of delen daaruit
mogen alleen met een nadrukkelijke status-en bronvermelding worden overgenomen of gepubliceerd.

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Contents
Samenvatting........................................................................................................................................... 7
Executive Summary ............................................................................................................................... 11
Introduction ............................................................................................................................... 11
HRA-Model improvements since HRA GTS-raming 2019 ......................................................... 11
Reservoir and Pressure Modelling............................................................................................. 11
Subsidence................................................................................................................................. 11
Seismic Event Rate .................................................................................................................... 11
Seismic Hazard Assessment....................................................................................................... 12
Seismic Risk Assessment ........................................................................................................... 12
Structural Upgrading Plan ......................................................................................................... 12
Building Damage........................................................................................................................ 12
1

2

Introduction ................................................................................................................................... 13
1.1

Previous Hazard and Risk Assessment Reports ..................................................................... 13

1.2

Expectation Letter (verwachtingenbrief) 2020 ..................................................................... 14

1.3

TNO Hazard and Risk Assessment ......................................................................................... 14

HRA-Model improvements since HRA GTS-raming 2019 .............................................................. 17
2.1

Introduction ........................................................................................................................... 17

2.2

Exposure Database ................................................................................................................ 17

2.2.1

3

Recent activities to improve the expose database ....................................................... 18

2.3

Fragility and Consequence Models for building typologies .................................................. 21

2.4

Farmhouses (URM1F_HA, URM1F_HC and URM1F_B)......................................................... 21

2.5

Terraced masonry houses (URM3L and URM4L) .................................................................. 22

2.6

Masonry apartment buildings (URM3M_B and URM3M_U) ................................................ 22

Reservoir and Pressure Modelling................................................................................................. 23
3.1

Production: Demand profiles GTS-raming 2020.................................................................... 23

3.2

Spatial distribution of production (Operational Strategy) .................................................... 24

3.2.1

Production regions ........................................................................................................ 24

3.2.2

Operational Strategies................................................................................................... 24

3.2.3

Start-up sequence ......................................................................................................... 25

3.2.4

Load Factor .................................................................................................................... 26

3.3

Modelling setup..................................................................................................................... 26

3.4

Forecast Scenarios ................................................................................................................. 26

3.4.1

Average temperature .................................................................................................... 26

3.4.2

Cold gas-year 2020/2021............................................................................................... 35

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3.4.3
4

Subsidence..................................................................................................................................... 41
4.1

Compaction model ................................................................................................................ 42

4.1.1

5

6

Input to compaction model ........................................................................................... 42

4.1.1.1

Pressure scenario for the reservoir and laterally connected aquifers ...................... 43

4.1.1.2

Reservoir Thickness ................................................................................................... 43

4.1.1.3

Rock compressibility .................................................................................................. 44

4.2

Influence model..................................................................................................................... 46

4.3

Calibration of compaction model parameters ...................................................................... 46

4.4

Uncertainty estimation.......................................................................................................... 47

4.5

Subsidence forecast .............................................................................................................. 49

Seismic Event Rate ........................................................................................................................ 55
5.1

Event Rate forecast ............................................................................................................... 55

5.2

Exceedance Probabilities ....................................................................................................... 58

5.3

Event Density Maps ............................................................................................................... 58

Seismic Hazard Assessment........................................................................................................... 71
6.1

7

Warm gas-year 2020/2021 ............................................................................................ 38

Hazard metrics....................................................................................................................... 71

6.1.1

Risk Assessment ............................................................................................................ 71

6.1.2

Building Damage Assessment ........................................................................................ 71

6.1.3

Hazard Map for Peak Ground Acceleration................................................................... 72

6.2

Hazard Assessment................................................................................................................ 74

6.3

Disaggregation of Seismic Hazard ....................................................................................... 100

Seismic Risk Assessment ............................................................................................................. 105
7.1

Risk Metrics ......................................................................................................................... 105

7.1.1
7.1.1.1

Object-related Individual Risk and Individual Risk .................................................. 105

7.1.1.2

Inside and Outside Local Personal Risk ................................................................... 105

7.1.1.3

Current Practice in Risk Assessments ...................................................................... 107

7.1.2
7.2

Individual Risk Metrics................................................................................................. 105

Group Risk and Maatschappelijk Veiligheidsrisico ...................................................... 108

Number of buildings compared to the safety norm............................................................ 110

7.2.1

Assessment of Local Personal Risk (LPR) ..................................................................... 110

7.2.2

Number of years P90 LPR > 10-5/year.......................................................................... 123

7.3

Maps of Buildings compared to the Meijdam-norm Risk Levels ......................................... 124

7.4

Insights into the development of the buildings above the norm........................................ 138

7.4.1

Introduction ................................................................................................................. 138

7.4.2

Comparing the results of risk assessments ................................................................. 138
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7.4.3
7.5

Structural upgrading program ............................................................................................. 142

7.6

Disaggregation of Local Personal Risk (LPR) ........................................................................ 143

7.6.1
7.7
8

Sensitivity to epistemic uncertainties ......................................................................... 147

Group Risk and Maatschappelijk veiligheidsrisico .............................................................. 150

Damage Assessment.................................................................................................................... 154
8.1

Classification of Building Damage; Building Damage States ............................................... 154

8.1.1

9

Update to buildings above the norm .......................................................................... 139

European Seismological Commission, EMS-1998........................................................ 154

8.2

Forecast for Damage State 1 (DS1) aesthetic damage ........................................................ 156

8.3

Earthquake catalogue of events .......................................................................................... 156

8.4

Exposure model ................................................................................................................... 156

8.5

Results ................................................................................................................................. 157

8.6

Forecast for Damage State 2 (DS2) and Damage State 3 (DS3) .......................................... 159

References ................................................................................................................................... 163

Appendix A – Relevant Correspondence ............................................................................................. 167
Expectation Letter (Verwachtingenbrief) ........................................................................................ 167
Voorlopige beoordeling van de actualisatie van de HRA-modellen................................................ 173
Appendix B – Glossary of Terms .......................................................................................................... 177
Appendix C – List of Abbreviations...................................................................................................... 182
Appendix D – Reconciliation of Request for proposal of the operational strategy for gas-year 2020/2021
............................................................................................................................................................. 185
Rapportage ...................................................................................................................................... 186
Bijlage .............................................................................................................................................. 187
Appendix E – Additional tables for buildings above the Meijdam-norm for calendar years. ............. 189
Appendix F – Pressure Differences...................................................................................................... 195
Appendix G – Seismic Event Rate ........................................................................................................ 206
Appendix H - Exceedance Probabilities Calendar-year ....................................................................... 208
Appendix J – Tables for comparing the results of the risk assessments ............................................. 210

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Samenvatting
Deze Nederlandstalige samenvatting van dit rapport met de dreigings- en risicoinschatting is
opgenomen in de Operationele Strategie. Deze is hier herhaald zodat dit rapport ook als een
zelfstandig rapport gelezen kan worden.
In de Mijnbouwwet Artikel 52c is vastgelegd dat de NAM elk jaar op verzoek van de Minister van
Economische Zaken en Klimaat (hierna: minister) één of meerdere operationele strategieën moet
indienen bij de minister. Voor dit jaar heeft de minister door middel van een verwachtingenbrief (ref
DGKE / 20018021, hierna: “de Verwachtingenbrief”) de NAM verzocht om twee operationele
strategieën uit te werken en in te dienen. Het verschil tussen deze twee strategieën is de manier
waarop de Groningenveld productie over de verschillende clusters wordt verdeeld. De clusters zijn
verspreid over het veld, en afhankelijk van welke clusters wanneer worden ingezet, wordt er meer of
minder gas uit een bepaald deel van het Groningenveld geproduceerd. Dat is van belang omdat dit
effect heeft op de seismiciteit.
Voor Operationele Strategie 1 worden de productievolumes verdeeld over de clusters Zuidoost,
Zuidwest, Centraal-Oost en Bierum. Als basis voor de productie wordt cluster Bierum ingezet. Op
momenten van hoge vraag worden achtereenvolgens de clusters Zuidoost, Zuidwest en tot slot
Centraal-Oost ingezet. Als gevolg van de lagere capaciteitsbehoefte op het Groningenveld zal cluster
Eemskanaal in tegenstelling tot voorgaande jaren niet meer noodzakelijk zijn om momenten van
uitzonderlijk hoge piekvraag af te dekken. Voor Operationele Strategie 2 worden de productievolumes
verdeeld over de clusters Zuidoost en Zuidwest en zal het clusters Centraal-Oost alleen gebruikt
worden op momenten van hoge vraag. Cluster Bierum zal onder deze operationele strategie net als
cluster Eemskanaal niet meer nodig zijn om de piekcapaciteitsvraag op het Groningenveld af te dekken.
Onderstaande figuur geeft een overzicht van de clusters op het Groningenveld. Het cluster Loppersum
is sinds februari 2018 definitief ingesloten en wordt niet meer ingezet in deze operationele strategieën.

De minister zal besluiten op welke wijze het Groningenveld moet worden geopereerd in het gasjaar
2020-2021. Om te zorgen dat de minister een goed afgewogen besluit kan nemen, staat in dit
document beschreven wat de consequenties van de twee operationele strategieën zijn. Welke
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consequenties de NAM precies inzichtelijk moet maken staat beschreven in de Mijnbouwwet, de
Mijnbouwregeling en de Verwachtingenbrief. De belangrijkste conclusies uit de door de NAM
gemaakte analyse van de twee operationele strategieën worden hieronder gegeven.
Volume: Onder beide operationele strategieën wordt relatief gezien het meeste volume onttrokken
uit het zuidoosten van het Groningenveld. Voor een gemiddeld jaar wordt ongeveer 50% van het totale
volume uit cluster Zuidoost geproduceerd. Onder Operationele Strategie 1 zal het cluster Bierum nog
aanzienlijke productievolumes laten zien terwijl onder Operationele Strategie 2 er helemaal geen
productie meer uit Bierum wordt verwacht. De volumes die onder Operationele Strategie 1 uit cluster
Bierum worden geproduceerd zullen onder Strategie 2 over de clusters Zuidoost, Zuidwest en CentraalOost worden verdeeld. Beide strategieën laten voor het cluster Eemskanaal geen volume bijdrage
meer zien.
De gevolgen van de productie uit het Groningenveld op bodembeweging zijn door middel van een
dreigings- en risicoanalyse bepaald. Deze modelmatige berekeningen combineren de hele oorzaakgevolg keten, beginnend met de gasproductie en eindigend met schade en risico. De modellen zijn
gevoed met de meest recente stand van de wetenschap rond seismiciteit in het Groningenveld en
nemen alle relevante onzekerheden op een wetenschappelijk verantwoorde wijze mee.
Dreiging of “hazard”: Dreiging en risico zijn twee termen die in het dagelijkse spraakgebruik vaak door
elkaar heen worden gebruikt, maar een andere betekenis hebben. De dreiging wordt door de NAM
inzichtelijk gemaakt met de zogenaamde “hazard kaarten”. Op deze kaarten is te zien welke maximale
grondversnelling er op basis van modellering te verwachten is boven het Groningenveld. Het risico
bestaat uit de kans dat de dreiging en het gevolg hiervan werkelijkheid wordt.
De hazard kaarten die gemaakt zijn laten zien dat Operationele Strategie 1 minder dreiging in het
zuiden tot gevolg heeft in vergelijking met Operationele Strategie 2. De verschillen zijn echter klein.
Een andere manier om de dreiging te duiden is door inzichtelijk te maken wat het verwachte aantal
bevingen is en wat de mogelijke sterkte van de bevingen is die verwacht kunnen worden bij elk van de
twee operationele strategieën. De volgende tabel laat de resultaten van deze analyse zien met daarin
de jaarlijkse kans op bevingen boven een bepaalde magnitude. De verschillen zijn hier ook klein. Door
de verminderde gaswinning neemt de dreiging de komende jaren verder af.

Schade: Schade als gevolg van bodembeweging wordt gedefinieerd als de nadelige en zichtbare
gevolgen die de bodembeweging heeft op gebouwen zonder dat de aanwezigheid in dit gebouw extra
persoonlijk risico’s met zich mee brengt. Om inzichtelijk te maken hoeveel schade wordt verwacht door
de gasproductie uit het Groningenveld, is gemodelleerd hoeveel DS1, DS2 en DS3 schade er bij de
uitvoering van de beide operationele strategieën is te verwachten. Het blijkt dat dit nagenoeg gelijk is
voor de beide strategieën (verschillen < 1%). Gevolgen van bodembeweging voor gebouwen die wel
persoonlijk risico met zich mee brengen worden gecategoriseerd als risico.
Risico: De bovengenoemde dreiging krijgt betekenis als deze gecombineerd wordt met de
kwetsbaarheid van woningen wat uitgedrukt wordt in het risico. Een nieuwe woning die gebouwd is
volgens de laatste bouwnormen zal bij een blootstelling aan eenzelfde dreiging een ander risico
hebben dan een oude boerderij.
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Er zijn verschillende manieren om risico te definiëren. De minister heeft aan de NAM gevraagd om het
risico te berekenen waarbij er van uit wordt gegaan dat de mensen 100% van de tijd aanwezig zijn in
hun huis1; het zogenoemde plaatsgebonden persoonlijk risico of “LPR”.
De belangrijkste uitkomsten van de HRA 2020 ten opzichte van de HRA 2019 zijn:
De kans op aardbevingen en daaropvolgende schade is afgenomen als gevolg van de dalende
gaswinning;
Er zijn geen gebouwen meer in deze modelmatige berekening die niet aan de Meijdamnorm voldoen.
Fluctuaties: De wijze waarop de productievolumes verdeeld worden over het Groningenveld heeft ook
invloed op de grootte van de regionale productiefluctuaties. Bij een sterke voorkeursonttrekking uit
bepaalde clusters, bijvoorbeeld uit cluster Zuidoost, zullen op momenten van hoge vraag overige
clusters moeten worden opgeregeld. Aangezien deze clusters op andere momenten van lage vraag
weinig of niets produceren leidt dit tot grotere en frequentere variaties in de desbetreffende
productievolumes. Door de lagere productieniveaus op het Groningenveld en de gewijzigde inzet van
UGS Norg zijn deze variaties frequenter en relatief groter dan in voorgaande jaren. Voor Operationele
Strategie 1 worden hierbij meer overschrijdingen van de fluctuatie bandbreedtebeperking verwacht
ten opzichte van Operationele Strategie 2. Dit is een gevolg van de inzet van cluster Bierum dat niet
meer zal produceren onder Operationele Strategie 2 en hiermee ook geen fluctuaties kan veroorzaken.
Onderstaande tabel geeft op kwalitatieve wijze op een aantal hoofdcriteria een vergelijking tussen de
twee uitgewerkte operationele strategieën.

1

De definitie waarbij uitgegaan wordt van een meer realistische aanwezigheidstijd (OIA) is opgenomen in de HRA
2020 documentatie (Appendix A).

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Executive Summary
In this executive summary the main conclusions from the hazard and risk assessment for production
profile GTS-raming 2020 are listed:

Introduction




The Hazard and Risk Assessment for production profile GTS raming-2020 is the tenth since
2012 and last assessment prepared by NAM on instruction of the Minister of Economic Affairs
and Climate Policy.
TNO has developed an own seismic risk assessment tool (modellentrein) and will from 2021
onwards prepare the annual Hazard and Risk Assessment.

HRA-Model improvements since HRA GTS-raming 2019




Since the previous Hazard and Risk Assessment was submitted in March 2019, the HRA-tool
has been further developed with focus on the dynamic reservoir model, the seismological
model and ground motion prediction.
Enhancement of the exposure database was modest due to limited additional data availability
to NAM. Focus was on farm houses and barns. Additional modelling was done for different
farm houses. Fragility and consequence models were developed for the house and barn
section of these buildings separately.

Reservoir and Pressure Modelling



Two Operational Strategies (OS) have been proposed. Main difference is the role of the Bierum
production cluster, located north of Delfzijl. In OS2 the Bierum cluster is effectively closed-in.
The difference in pressure between the two OS shows for OS1 a slightly lower pressure north
of Delfzijl and higher pressure near Hoogezand. After 2022 the pressure differences are very
small.

Subsidence




The compaction/subsidence model was calibrated using results from 16 levelling campaigns
spanning from 1964 to 2018.
Subsidence forecasts were made for 2025 and 2030 based on OS1.
In 2030 some 40 cm of surface subsidence (since start of production) is expected in the deepest
point of the subsidence bowl, with a P95 uncertainty range estimated at about 15%.

Seismic Event Rate








The number of earthquakes with magnitude larger than M=1.5 declines. By gas-year
2025/2026 the expected number of earthquakes has declined to three per year with an
uncertainty of between 0 and 9 per year.
Comparison with a case where the field is closed in on 1st October 2020 (start gas-year
2020/2021) shows that the seismicity in later years is primarily caused by equilibration of the
current pressure differences in the field. The low production levels in the remaining years have
limited impact on the seismic event rate, regardless of which the Operating Strategy is
employed.
The effect of a warm or cold gas-year 2020/2021 and the operational strategy on the seismic
event rate is much smaller than the uncertainty.
Probability of an earthquake with magnitude larger than the Huizinge earthquake (M=3.6) has
reduced to 4.74% for gas-year 2020/2021. In the HRA GTS-raming 2019, submitted March
2019, this probability was estimated at 9.3% for the same gas-year.

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Seismic Hazard Assessment


The hazard shows a declining trend. Although the hazard declines in the whole Groningen area,
the decline is fastest in the south-east of the field. As a result, the remaining hazard after 2024
is concentrated to north-west of the village of Loppersum.

Seismic Risk Assessment








As a consequence of the reduced gas production, seismic risk is also declining.
The assessment shows that all buildings in the Groningen field area meet the life safety risk of
LPR< 10-5/year (Meijdam-norm).
For a limited number of buildings there is a 10% probability that the building does not meet
the life safety risk of LPR< 10-5/year. These are buildings in the P90-group.
o For Operating Strategy 2, 82 buildings have been evaluated to belong to the P90 group
in gas-year 2020/2021. After four years no buildings are left in the P90 group.
o For Operating Strategy 1, 162 buildings have been evaluated to belong to the P90 group
in gas-year 2020/2021. After three years no buildings are left in the P90 group.
The buildings in the P90 group all belong to the same typology: barns of farmhouses. This
means there are no dwellings in the P90 group. There is a debate whether barns should be
assigned occupancy and therefore whether they need to comply with the life safety norm. In
this seismic risk assessment cautiously the position is taken that, based on the available
knowledge, occupancy should be assigned to barns as well, and that these should therefore
comply with the safety norm.
All barns in the P90 group are located around the village of Loppersum, west of Appingedam.

Structural Upgrading Plan


The HRA is a probabilistic assessment of the number of buildings that do not meet the
Meijdam-norm. This does not immediately translate into an estimate of the structural
upgrading scope. The involvement of the Minister of EZK over the past years with the
structural upgrading has been formally implemented in the Mining law in December 2019.

Building Damage


With declining seismic hazard, damage caused to primary (occupied) buildings will decline as
well. The assessment for gas-year 2020/2021 shows the chance that more than 1,000 buildings
will receive DS1 damage is below 10%. This result is for both operational strategies.

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1 Introduction
1.1 Previous Hazard and Risk Assessment Reports
Since 2012 NAM has prepared hazard and risk assessments (HRA) for different production scenarios.
Table 1.1 provides an overview of these HRA reports. The first calibrated and fully probabilistic HRA
was submitted to SodM and the Ministry of Economic Affairs in November 2015 (Ref. 7). This was
followed by Winningsplan 2016 (Ref. 8 to Ref. 14). The Mining Law requires that Winningsplannen are
approved by the Minister of Economic Affairs and Climate Policy (Minister). The approval was granted
in the Instemmingsbesluit Winningsplan Groningenveld, issued on the 30th of September 2016 (Ref.
27).
In response to the specific instruction in the Instemmingsbesluit, NAM prepared the report
“Assessment of Hazard Building Damage and Risk for Induced Seismicity in Groningen – November
2017” (Ref. 15), which was submitted to the Minister of Economic Affairs and Climate Policy and to
SodM on 1st November 2017. The Wijzigingsbesluit of 24th May 2017 (Ref. 22) limited the production
in an average temperature year to 21.6 Bcm/year. Due to the fact that in the Wijzigingsbesluit special
circumstances were identified that could require an increase in the production from the field, the
Hazard and Risk Assessment of November 2017 (Ref. 15) was prudently based on an average annual
gas production level of 24 Bcm/year, which also covered these eventualities.
To assess the effect of different production profiles on seismic risk, a complementary set of production
profiles covering a wide range of production levels was presented in the addendum to the November
2017 Hazard and Risk Assessment (Ref. 15), The report was issued March 2018. The set of production
profiles analysed included the production aspirations as outlined in the Coalition Agreement
(Regeerakkoord of 10/10/2017) (Fig. 1.1) and several production profiles included in reports by GTS,
which were based on different utilisation of the existing nitrogen blending plant and the option of the
construction of an additional nitrogen blending plant.
The letter sent by the Minister of Economic Affairs and Climate Policy to Parliament (Kamerbrief) on
29th March 2018 (Ref. 23) announced the ambition of the cabinet to reduce the production from the
Groningen field as soon as possible, leading to cessation of production around 2030. It contained
annual production volumes for the period 2018-2031, which was labelled “Basispad Kabinet” (Fig. 1.1).
Different production profiles were presented for cold, average and warm temperature years.
An Expectation Letter (verwachtingenbrief) was sent to NAM on 2nd May 2018 (Ref. 25) by the Minister
of Economic Affairs and Climate Policy. It detailed the expectations for further NAM technical studies
in preparation of a new Winningsplan decision (due by 15th November 2018 latest). NAM was
requested to perform a Hazard and Risk Assessment for the “Basispad Kabinet” production profile, to
indicate the impact of the strong reduction of production on safety risk and the scope of the structural
upgrading needed to comply with the Meijdam-norm (Ref. 28 to Ref. 30). With the Expectation Letter
the Minister of Economic Affairs and Climate Policy provided the demand and production profile for
Groningen quality gas (Fig. 1.3) to NAM, which served as the basis for the Hazard and Risk Assessment
for the production profile “Basispad Kabinet” (Ref. 17).
On 6th June 2018, the Minister of Economic Affairs and Climate Policy sent a letter to Parliament on
the progress of the measures to end production from the Groningen field (Ref. 24). In this letter, a
number of additional measures are referenced that were not yet incorporated in the “Basispad
Kabinet” as presented on 29th March 2018. The risk impact of a profile based on the maturation of
these additional measures to reduce Groningen gas demand was not assessed, but this would
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directionally have reduced the risk further as compared to the Hazard and Risk Assessment based on
the production profile “Basispad Kabinet” (Ref. 17 and Ref. 18).
On 12th February 2019, NAM received the Expectation Letter with the updated production profile
prepared by GTS: “GTS-raming 2019” (Ref. 19). Actual production realised in gas-year 2018-2019 was
18.8 Bcm compared to 19.4 Bcm in the production profile of “Basispad Kabinet”. This 0.6 Bcm lower
production has been included in this Hazard and Risk Assessment. In figure 1.1 the annual production
rates for the five production profiles; (1) Coalition Agreement, (2) Basispad Kabinet March 2018 Letter,
(3) Basispad Kabinet Expectation May 2018 Letter, (4) Expectation Letter February 2019 and (5)
Expectation Letter February 2020 are compared. The comparison is shown in this figure for cold,
average and warm year gas demand.

1.2 Expectation Letter (verwachtingenbrief) 2020
In accordance with article 52c of the Mining Law, NAM proposes two operational strategies based on
the premises for these strategies contained in the expectation letter (Ref. 31). In this report the
subsidence, seismic event rate, hazard, risk and building damage consequences of these operational
strategies will be presented. The expectation letter has been attached to this report as Appendix A.
On 21st February, the Minister of Economic Affairs and Climate Policy sent a letter to parliament on the
production profile GTS-raming 2020 (Ref. 32). In his letter the Minister also announced a reduction in
the gas production for the current gas-year (2019/2020) from 11.8 Bcm/year to 10 Bcm/year. In this
report this reduction in production for the current gas-year has not been taken into account. The
production for gas-year 2019/2020 used in this report is based on the actual production from 1st
October 2019 until 1st January 2020 and the prognosed production of GTS-raming 2019 for the
remainder of this gas-year.
Apart from the premises for the operational strategies, the expectation letter also describes the maps,
graphs and tables to be included in the current report. In order to present a clear analysis additional
maps, graphs and tables have been included in this report when required for clarity. The hazard,
building damage and risk assessment has been requested to be presented based on gas-years. Gasyears are the 12-month period starting at 1st October. The gas-year 2020/2021 is the period from 1st
October 2020 up to and including 30st September 2021. Gas-years are used to avoid the high gas
demand winter period to be split over two reporting periods. To avoid confusion and very long sections
of tables, the main document will present the analysis in gas-years, while the tables for calendar years
are presented in Appendix E. The assessment of subsidence included in this report uses calendar years
to be in line with other subsidence reports.

1.3 TNO Hazard and Risk Assessment
TNO has built their own modelling tool for assessment of hazard and risk in Groningen (de TNO
Modellentrein). Using this modelling tool, TNO will take over the preparation of the hazard and risk
assessment from NAM, starting with the HRA 2021.

14

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Production Profiles - Groningen gas field
30

Gas production [Bcm]

25

20

15

10

5

0
18/19

19/20

20/21

21/22

22/23

Coalition Agreement - 2017
Basispad Kabinet - Average
Basispad Kabinet - Cold+ Supplemental
Expectation Letter - Cold
Expectation Letter - Warm
Expectation Letter - Average + Supplemental
GTS-2019 - Average
GTS-2020 - Cold
GTS-2020 - Warm
Figure 1.1

23/24

24/25

25/26

26/27

27/28

28/29

29/30

Basispad Kabinet - Cold
Basispad Kabinet - Warm
Basispad Kabinet - Average + Supplemental
Expectation Letter - Average
Expectation Letter - Cold + Supplemental
GTS-2019 - Cold
GTS-2019 - Warm
GTS-2020 - Average

Comparison of the production profiles:
1. Coalition Agreement (Regeerakkoord) (10/10/2017),
2. “Basispad Kabinet” from Kamerbrief (29/3/2018) in blue (Ref. 23),
3. “Basispad Kabinet” from the Expectation Letter (verwachtingenbrief) (2/5/2018) in red (Ref. 25)
4. Production profile GTS-raming 2019 in green (Ref. 19) and
5. Production profile GTS-raming 2020 in green (Ref. 31) and
Production profiles for a warm gas-year are shown in blue. Those for average and cold temperature gas-year
in green and red respectively.

15

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Hazard and Risk Assessment

Ref

Submitted
to EZK &
SodM

Winningsplan 2013 and Technical Addendum
to Winningsplan 2013
Supplementary Information to Technical
Addendum to Winningsplan 2013

1, 2

November
2013

2

December
2013

Hazard Assessment Eemskanaal Region and
Addendum to: Hazard Assessment for the
Eemskanaal area of the Groningen field
Dreigings- en risicoanalyse voor
geïnduceerde seismiciteit Groningen Onderzoek 1 dreigingsanalyse and
Onderzoek 2 risicoanalyse
Hazard and Risk Assessment for Induced
Seismicity in Groningen

3, 4

May
2014

Requested by SodM.

5, 6

May
2015

Requested by Scientific Advisory Committee and
SodM.

Uncalibrated
assessment.

7

November
2015

Requested by Scientific Advisory Committee and
SodM.

First calibrated probabilistic hazard and risk
assessment. This was also the first HRA where LPR
results could be compared to the Meijdam-norm (Ref.
28 to Ref. 30).

Winningsplan 2016, Technical Addendum to
Winningsplan Groningen 2016
HRA 2017 - Addendum to WP 2016

8–
14

April
2016

15

HRA for a selection of production profiles

16

HRA Basispad Kabinet

17,
18

November
2017
March
2018
August
2018

HRA GTS-raming 2019

19

HRA GTS-raming 2020
Table 1.1

March
2019
March
2020

Production profile

In the Technical Addendum to the Winningsplan
hazard for several production scenarios were
assessed.
Letter to NAM: Mijnbouwwet instemmingsbesluit
winningsplan Groningenveld; aanvullingsverzoek, 2012-2013.

Ref

Probabilistic hazard assessment combined with a
scenario based risk assessment.
20

Verwachtingen brief

21

Wijzigingsbesluit of
24th May 2017.
Assessments were prepared for a wide range of
production profiles.
Letter to parliament 29 March 2018 en 6 Juni 2018
Verwachtingenbrief of
2nd May 2018

22

Verwachtingenbrief of
12th February 2019
Verwachtingenbrief of
1st February 2020

Comment

Two production scenarios were requested: (1)
‘market demand’ scenario and (2) ‘market demand’
scenario with closing in five clusters around
Loppersum (LRM, OVS, PAU, POS and ZND).
Additional hazard assessment for production from
the cluster located close to the city of Groningen.

probabilistic

hazard

and

risk

Production scenario with 24 Bcm/year plateau was
prudently used.
Report was prepared to inform decision on the future
production from the Groningen gas field.

23,
24
and
25
25
This report.

Overview of the Hazard and Risk Assessments prepared by NAM.

16

2 HRA-Model improvements since HRA GTS-raming 2019
2.1 Introduction
A number of improvements and changes have been implemented in this Hazard and Risk Assessment
since the HRA-model was used in the preparation of the HRA for GTS-raming 2019, which was
submitted in March 2019. The regulator, SodM, has reviewed these updates of the Hazard and Risk
Model (Ref. 33). Their preliminary assessment of the update of the HRA model has been included in
this report in Appendix A (Relevant correspondence).
The main improvements and changes implemented in the HRA-tool since the previous HRA (HRA GTSraming 2019) issued at 1st March 2019 are:










The dynamic model of the Groningen gas field has been updated (Ref. 34 and Ref. 35). The prime
objective of the update was to improve the predictive ability for the long-term pressure
development in the reservoir following cessation of production from the gas field. Focus of the
update was therefore on the lateral aquifers connected to the field and low-saturation gas in the
aquifer below the gas-water-contact (Ref. 36 and Ref. 37). The impact of these changes will be
limited in the central area of the field and therefore not significantly impact seismicity. The impact
is expected to be primarily on subsidence in the outer regions of the field and areas outside the
field boundary.
A number of improvements were incorporated in the development of the ground motion model
(Ref. 38 and Ref. 39). The most important change is the switch from using the G4 geophones
recordings acquired at 200 m depth to the G0-station accelerograms acquired at surface. This
change was made possible by the expansion of the database of ground motion recordings.
Previous investigations into the seismological model focussed on the temporal and spatial
distribution of induced earthquakes in the Groningen area (Ref. 40 and Ref. 41). The update of
the seismological model addresses the occurrence of larger magnitude earthquakes relative to
smaller magnitude earthquakes (Ref. 42 and Ref. 43).
In 2018, the exposure database was updated with the results from 189,126 documents retrieved
from municipality archives during 353 visits. With this extended update implemented in HRA GTSraming 2019, nearly all information publicly available and to which NAM has access had been
incorporated in the exposure database. The update since the previous HRA (GTS-raming 2019) is
therefore relatively modest and focussing primarily on farm houses.
The fragility of several building typologies was further investigated.

These improvements of the HRA-model have been documented extensively (Ref. 34 to Ref. 43) in
reports available at the research report page (onderzoeksrapporten pagina) of www.nam.nl.
Improvements that are of direct interest for the hazard and risk assessment and the reconciliation of
risk at building level will be further described in this section.

2.2 Exposure Database
To be able to assess the risk for the buildings and community in Groningen resulting from induced
earthquakes, knowledge of the occupied building stock in the region of the Groningen field is required.
To assign a unique building typology to each individual building in the earthquake area, a program of
building inspections was initiated in 2013. The program consists of the collection of building data from
existing databases and data sources. This is supplemented by building data gathered from public
sources (e.g. observation from public areas (street level) and Google Street View) and engineering
drawings of buildings, publicly available at the municipality office. The taxonomy of building typologies
17

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

of GEM (Global Earthquake Model) is used to assign typologies, based on the structural system of each
building.
For practical reasons the Groningen field area has been divided in two areas2. The core area consists
of the seismically most active area and contains some 20,000 buildings. Additionally, data on the use
of the buildings and occupancy are collected. The data on the buildings in the Groningen field area are
stored in the exposure database (EDB). An earlier version of this database (V2) was used for the Hazard
and Risk Assessment of November 2015 (Ref. 7) and Hazard and Risk Assessment for Winningsplan
2016 (Ref. 8 to Ref. 14). Early 2017 and mid-2018, updated versions of the database were issued (Ref.
45, Ref. 46 and Ref. 47). The exposure database of mid-2018 (Ref. 47) was used in the Hazard and Risk
Assessment for the production profile “Basispad Kabinet”, which was issued June 2018.
The exposure database combines many data sources (BAG, AHN, Deltares top soil, etc.) together with
inference rules to assign typologies to individual buildings. The datasets used for the EDB are
categorised as follows:

Source data Datasets which have been received and maintained by external sources such as
government departments.

Project data Datasets which have been produced within the project such as inspection datasets
and desktop studies. This includes project information produced by ARUP and external
consultants.

Processed data Datasets which ARUP has created utilising source datasets, assumptions and
analysis to provide information that is not available from external sources.
For many buildings this leads to a non-unique typology description. In the core area, almost every
building has a unique typology assigned, but away from this area, the typology of many buildings is
based on inference rules, reflecting the experience of local engineers with knowledge of the
development of local building methods. These inference rules are also updated in light of the on-going
building data gathered. The Hazard and Risk (HRA) model applies the assessed earthquake hazard to
the buildings and the population in the exposure database to assess the earthquake risk. The inference
rules will in most cases not be able to establish the building typology uniquely and will assign a number
of typologies to the building, each with a probability. On a regional level this provides a reliable
assessment of the number of buildings where the safety-norm is exceeded and a risk-based ranking of
all building in the Groningen field area.

2.2.1 Recent activities to improve the expose database
The development of EDB V7 included the update of several datasets. The source datasets used in EDB
V7 are described in Table 2.1. Most of these datasets are inputs into algorithms which generate process
datasets that provide the classification parameters required for the assignment of the appropriate
Structural System(s) to each building in the database.

2 These areas have been introduced by the NCG based on a hazard map of KNMI. The 0.2 g contour of the 2015 KNMI hazard map was chosen

as the boundary between these areas.

18

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Dataset
Apartment Dataset
Opening Percentage and
Storey Count
Drawing Data

Data source
Arup
Ticinum Aerospace (TA)

Date
2017
2019

Arup

2019

Data Collection

Arup

2019

Extended Visual
Screening (EVS)
Rapid Visual Screening
(RVS)
Desktop Visual
Inspections (Streetview)

Arup

2015

Notes
Included as part of the drawing data.
Data as delivered on September 2019.
Updated from V6.
Included information collected up to
December 2019 from other inspection
activities.
Included information collected up to
December 2019. Incorporates the
information collected as part of the
forgotten building lists.
No update. Included as per V5.

Arup

2015

No update. Included as per V5.

Arup

2019

Desktop Visual
Inspections (Streetview)
Strengthened buildings

JBG

2019

Included information collected as part of
the ‘WBH’ visual inspections and sample
checking exercises.
No update. Included as per V5.

NCG

-

BAG (Buildings,
Addresses and
Occupancy)
Lidar data (height map)

Basisregistratie Adressen en
Gebouwen (BAG)

2019

Actueel Hoogtebestand
Nederland (AHN)
Dataland

2009

No update. Included as per V5.

2018

Dienst Uitvoering
Onderwijs (DUO),
Basisregister Instellingen
Rijksinstituut voor
Volksgezondheid en Milieu,
Nationale Atlas
Volksgezondheid
NCG

2019

Data as per 26th September 2018.
Included as per V6.
Data as per January 2019. Included as
per V6.

Tony Taig
CBS

Architectural and
building descriptions
Educational buildings
(for building use and
population analysis)
Hospitals (for building
use and population
analysis)
Inhabitant and Employee
population (for
population analysis)
Footfall data (for
population analysis)
CBS Buurten, Wijken
and Gementee data (for
community and
population analysis)
Table 2.1

No update provided since 28 January
2019 by CVW. Any progress in
structural upgrading is not considered in
this report.
Data as per August 2019. Updated from
V6.

2019

Data as per January 2019. Included as
per V6.

2018

2015

Data as per April 2018 (provided by
NAM December 2018). Included as per
V6.
No updates. Included as per V5.

2018

No update. Included as per V6.

Source datasets in EDB V7.

19

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

The process datasets used in EDB V7 are described in Table 2.2. All the datasets below are calculated
by Arup.
Dataset
Adjacency calculation
Building Use analysis
Exposed Footprint Length
Usable Area
Footprint Area
Storey count proxy
Geometric parameters
Table 2.2

Notes
Algorithm has been updated and improved. Includes also
latest BAG data.
Updated with latest BAG data.
Updated with latest BAG data.
Updated with latest BAG data.
Updated with latest BAG data.
Updated with latest BAG data.
Updated additional buildings identified in latest BAG data.

Data input
BAG
BAG
BAG
BAG
BAG
BAG
BAG, AHN

Main processed datasets in EDB V7.

Aside from data updates and new parameters, the EDB V7 also features several methodology updates
in comparison to the ones described in EDB V6. Since the list of buildings above the norm in previous
HRA was dominated by farmhouses the main focus has been to improve the identification of different
types of farmhouses.
An overview of the methodology updates is found in Table 2.3.
Structural Layout
Farmhouse subtypes
Inspection data for structural
system
Blockpart uniformity

Irregularity

Table 2.3

Updates around the WB / WBH identification including revised
Dataland flag and visual inspections.
Identification of WBH (farmhouse) subtypes of ‘Aggregate’ and
‘Continuous’.
Additional project data incorporated including inspections
around V6’s ‘Forgotten buildings’ and any additional
inspections undertaken by Arup.
Revised adjacency algorithm to better identify uniform
blockparts. Results have been used to review assignment of
structural layout and irregularity
Opening percentage cut off has been adjusted to 85%. Updates
also include removal of TA data and revised building year
inferences.

Overview of methodology updates in EDB V7.

The sub-typologies have been defined making use of the analysis results generated within the fragility
function modelling developments for several farmhouses. Two farmhouse subtypes have been
identified. This includes:



Subtype A – characterised by a connection (either direct or via a narrow ‘neck’) between the
house and barn but without the continuity of the roof ridge.
Subtype C - characterised by a continuous roof structure from the house to the barn.

The defining characteristic for the sub-typologies is therefore based on the geometric relationship
within the farmhouse between the house and the barn geometry parts.

20

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

2.3 Fragility and Consequence Models for building typologies
In 2019 the effort to update and refine fragility and consequence models has focussed on buildings
with relatively high risk. Figure 2.1 below illustrates that farmhouses, masonry terraced houses and
masonry apartment buildings received most attention.

Figure 2.1:

Risk profile per building type as per GTS-raming 2019, Operational Strategy 2 and average temperature
demand.

2.4 Farmhouses (URM1F_HA, URM1F_HC and URM1F_B)
Typical farmhouses in Groningen consist of a relatively small residential part and a barn with large
span. It has become clear that seismic behaviour of the house differs from the barn leading to
significantly different risk profiles for the people inside the house and the barn.
While the 4 farmhouse index buildings were modelled entirely to include interaction between the 2
parts, fragility and consequence models were developed separately:



URM1F_HA and URM1F_HC representing the house part depending on the subtype A or C.
URM1F_B representing the Barn part.

Figure 2.2:

These 4 index buildings form the basis of Farmhouse fragility and consequence models

21

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

This will also facilitate addressing TNO’s suggestion to re-evaluate farmhouse building risk profiles
based on inspection visits. In Advies vaststellingsbesluit Groningen gasveld 2019/2020 of 7 May 2019
(Ref. 54) TNO advised the Minister that in the event a barn has storage function, there is no occupancy,
and there is no need to include them the risk assessment. In the meantime, NAM have cautiously
assumed that all these barns do have occupancy.

2.5 Terraced masonry houses (URM3L and URM4L)
Another update relates to typology URM3L and URM4L. These represent the terraced masonry houses
with cavity walls and concrete floors that are widespread across the Groningen region. The difference
between the types relates to the percentage of ground floor openings in the façade walls, with URM4L
having the larger openings and being the more vulnerable type.
For both types additional index buildings have been modelled and SDOF model results were calibrated
to MDOF results (assurance panel recommendation). URM3L is now based on 4, and URM4L on 2 index
buildings.

2.6 Masonry apartment buildings (URM3M_B and URM3M_U)
Following shake table test results late 2018 and early 2019, the LS-Dyna constitutive model was
recalibrated to more accurately predict the behaviour of heavy loaded walls. Both index buildings
representing URM3M_B and URM3M_U have been reanalysed, and the fragility functions updated.

For a more detailed description of V7 Fragility and Consequence models please refer to Ref. 55

22

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

3 Reservoir and Pressure Modelling
Modelling the pressure in the Groningen gas reservoir forms the starting point for both the seismic
hazard and risk assessment as well as the subsidence forecast. The future pressure distribution in the
reservoir depends on the amount of gas produced and on how this production is regionally distributed.

3.1 Production: Demand profiles GTS-raming 2020
The Ministry of Economic Affairs and Climate Policy provided demand profiles for Groningen gas with
the Expectation Letter (Verwachtingenbrief, Ref. 31) sent to NAM on the 31st of January 2020. The
demand for Groningen-quality gas has been determined by GTS. For gas-year 2020/2021 30 daily
demand profiles are provided based on temperature profiles of the last 30 gas-years. Three reference
years were chosen, for which longer term daily and monthly demand profiles have been supplied.
These reference years correspond to the temperature profiles of gas-years 1996 (cold year), 2012
(average year), and 2007 (warm year). For the reservoir pressure modelling monthly production
profiles are used.
The GTS demand profiles assume 100% utilisation of the GTS nitrogen plants Ommen, Wieringermeer
and Zuidbroek (once available in 2022) to produce pseudo-G gas. Further processing of the datasets
has been done by GasTerra to account for the contributions of UGS Norg and PGI Alkmaar, resulting in
net Groningen production profiles.
As requested in the Expectation Letter (Verwachtingenbrief, Ref. 31), the HRA is performed for three
temperature scenarios for gas year 2020/2021 (cold, average, warm), with subsequent gas years based
on average temperature. These three production profiles are given in Figure 3-1.

Monthly Production Volume (bcm)

2.5

Cold
Average
Warm

2.0

1.5

1.0

0.5

Figure 3-1:

Oct-26

Apr-26

Oct-25

Apr-25

Oct-24

Apr-24

Oct-23

Apr-23

Oct-22

Apr-22

Oct-21

Apr-21

Oct-20

0.0

Groningen field monthly gas production according to GTS-raming 2020. The warm and cold scenarios are
applied to gas-year 2020/2021 only.

23

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

3.2 Spatial distribution of production (Operational Strategy)
3.2.1 Production regions
In Article 1.3a.1 of the Mining Regulations (“Mijnbouwregeling”), the Groningen production regions
(“clusters”) are defined as follows:
a) Bierum: production location Bierum
b) Eemskanaal: production location Eemskanaal
c) East-Central (“Centraal-Oost”): production locations Amsweer, Schaapbulten, Oudeweg,
Siddeburen, and Tjuchem
d) South-East (“Zuidoost”): production locations De Eeker, Scheemderzwaag, and Zuiderpolder
e) South-West (“Zuidwest”): production locations Kooipolder, Slochteren including
Froombosch, Spitsbergen, Tusschenklappen including Sappemeer, and Zuiderveen
f) Loppersum: production locations De Paauwen, Leermens, Overschild, ’t Zandt, and Ten Post
Use of the Loppersum cluster stopped in February 2018 following the instruction from the Minister of
Economic Affairs and Climate Policy.

3.2.2 Operational Strategies
In the 2018 Production Optimisation study (Ref. 56) NAM investigated whether the seismic hazard and
risk in Groningen could be influenced by the way the production volume was distributed over the
various production locations and for which distribution the seismic risks would be minimized as much
as possible. In a letter to the Ministry of Economic Affairs on 16/10/2018, SodM advised the Minister
to use NAM’s 2018 optimisation study to steer the production distribution in the field (Ref. 57).
Production distribution as calculated for the optimisation metric “population weighted Peak Ground
Velocity” was judged to minimize the seismic risks over the entire Groningen gas field in a socially
responsible manner. This distribution resembles “Operational Strategy 1” in NAM’s 2018 “Bouwstenen
document”, the document which outlines options for the Operational Strategy of gas-year 2018/2019
(Ref. 58). Operational Strategy 1 was adopted as the operational strategy of choice in the 14/11/2018
Ministerial Instemmingsbesluit (Ref. 59) and has been applied in the field since. In the
Vaststellingbesluit for gas-year 2019/2020 (Ref. 60) the Minister instructed NAM again to adopt
Operational Strategy 1.
For this 2020 HRA update, the Ministry of Economic Affairs and Climate Policy requested NAM to
propose two Operational Strategies. The first is Operational Strategy 1 as currently in use. In the
second Operational Strategy the same order of cluster priorities is to be followed with the exception
of the Bierum cluster. Rather than utilising Bierum at a constant rate like in Operational Strategy 1
(OS1), in Operational Strategy 2 (OS2) Bierum will only be utilised when demand requires this, similar
to (but prioritised before) the Eemskanaal cluster.




Operational Strategy 1 (OS1)
Gas is produced preferentially from the South-East. If more production is required, production
locations in the South-West and Central-East regions are added. Cluster Bierum is kept at a
constant rate whereas cluster Eemskanaal is only utilised to provide additional capacity when
required.
Operational Strategy 2 (OS2)
Gas is produced preferentially from the South-East. If more production is required production
locations in the South-West and Central-East regions are added. The clusters Bierum and
Eemskanaal are only utilised in periods of high demand if their capacity is required.
24

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

3.2.3 Start-up sequence
To implement the Operational Strategies in the Integrated Production System Model, the production
regions are further divided into control groups (Figure 3-2). In the model forecast, the production wells
within these control groups are opened in order of their control group priority until the required
production level is reached.

Production regions

Figure 3-2:

Production controls

Production regions and control groups as used in the start-up sequence

The start-up sequence for the two Operational Strategies is given in Table 3-1.
Control Group
OS1
Control Group
BIR
Constant rate
EKR/SZW/ZPD
1
EKR/SZW/ZPD
SPI/ZVN
2
SPI/ZVN
SAP/TUS
3
SAP/TUS
SCB/OWG
4
SCB/OWG
FRB/KPD/SLO
5
FRB/KPD/SLO
AMR/SDB/TJM
6
AMR/SDB/TJM
EKL
7
BIR
EKL
Table 3-1:

OS2
1
2
3
4
5
6
7
8

Production start-up list for OS1 and OS2. Starting from the top, groups of production locations are sequentially
opened-up by the surface network model until the total required production is achieved.

In OS1, a constant rate is set for the Bierum cluster. This rate is 6 mln Nm3/d until 1-4-2021 in line with
the current operational strategy. Since gas demand decreases over time, the fixed rate for Bierum is
also stepped down: to 4 mln Nm3/d on 1-4-2021 and 2 mln Nm3/d on 1-10-2021 (start of next gas year).
It is assumed Bierum is closed in from 1-4-2022 onwards. In months where gas demand is lower than
the Bierum rate (which happens only in the warm temperature scenario) the Bierum rate is scaled
down to match the total demand which is then provided by Bierum alone.
25

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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3.2.4 Load Factor
The load factor represents the ratio of cluster production to cluster capacity. A maximum value for the
load factor can be set as a constraint in the surface network model. This is needed to correctly reflect
the distribution of production volume over the clusters. The model uses monthly volumes and timesteps, in which daily fluctuations are averaged out. The maximum load factors have been decreased
compared to the values used in the HRA 2019 analysis, to bring the model results better in line with
operational reality. The maximum load factor for gas year 2020/2021 ranges between 50% and 65%
and is set to 40% for subsequent gas years.

3.3 Modelling setup
An Integrated Production System Model is used for forecasting. In this set-up, the dynamic reservoir
model in MoReS is coupled to the surface network model in Genrem. Dynamic reservoir model V6 is
used (Ref. 35). The V6 model includes several additional features compared to model V5 (used in the
2019 HRA):




Gas-in-aquifer (Slochteren formation)
Carboniferous formation (both gas and water bearing)
Depletion in the Lauwerszee aquifer resulting from the fields Roden, Vries, Pasop and Faan

The inclusion of gas-in-aquifer improves the subsidence match and GWC rise in the North-West
compared to model V5. The overall model match to measured pressures (SPG data) in V6 is slightly
improved from V5, with an RMSE of around 2 bar with all pressure data up to 1-1-2020 included.
The history-match period was run in (stand-alone) MoReS until the end of calendar year 2019. GenremMoReS forecasting starts from 1-1-2020 onwards, where the Latest Estimate (LE) is used to constrain
production up to 1-10-2020. This LE is in accordance with the graaddagen formula of the
Vaststellingsbesluit for gas-year 2019/2020. The various pressure forecast scenarios therefore have a
common starting point at the start of gas-year 2020/2021.

3.4 Forecast Scenarios
As requested in the Expectation Letter (“verwachtingenbrief”, Ref. 31), seismic hazard and risk
calculations are performed for 6 scenarios: 3 temperature scenarios 2 operational strategies. The
distribution of production volume over the regions and the forecasted pressure distributions are
discussed below. For each temperature scenario results for Operational Strategy 1 and 2 are
compared.

3.4.1 Average temperature
Figure 3-1 shows the required monthly production volume for each temperature scenario. The average
scenario is based on a temperature profile like in gas-year 2012. Figure 3-3 compares the distribution
of production volume over the regions for the two Operational Strategies. Figure 3-4 shows these
distributions for each of the next 6 gas-years. The key difference is that in OS2, cluster Bierum does
not produce since it is low in the start-up sequence (Table 3-1) and it is not required in order to satisfy
the demand. As Ref. 56 shows, there is sufficient excess capacity.

26

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 3-3:

Monthly production volume per region for the next 6 gas-years for OS1 (top) and OS2 (bottom) in the average
temperature scenario. Note the Bierum production cluster (BIR) is indicated in green.

Figure 3-4:

Regional distribution of production volume per gas-year for OS1 (top) and OS2 (bottom) in the average
temperature scenario. Note the Bierum production cluster (BIR) is indicated in green.

27

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 3-5:

Cluster capacity (blue) and production rate (green) for OS1 (left) and OS2 (right) in the average temperature
scenario

Figure 3-6 shows the modelled reservoir pressure distribution on 1-10-2020, which is the start of gasyear 2020/2021. This distribution is the common starting point for the 6 forecast scenarios. Figure 3-7
plots the difference in pressure between OS2 and OS1 at the end of an average gas-year 2020/2021.
The maximum absolute local pressure differences between the Operational Strategies are around
2.5 bar. Figure 3-8 to Figure 3-12 show similar pressure maps and pressure difference maps for each

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gas-year up to 1-10-2030. Appendix F contains additional maps up to 1-10-2051. The same colour
scales are used for the pressure and pressure difference maps throughout this document.

Pressure (bar)

Figure 3-6

Modelled reservoir pressure at the start of gas-year 2020/2021

Pressure difference (bar)

Figure 3-7

Pressure difference between the two Operational Strategies (pressure OS2 – pressure OS1) at the end of an
average gas-year 2020/2021

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 3-8

Pressure and pressure difference maps for average temperature scenario: start (top) and end (bottom) of gas year 2021/2022. See for colour scales Figure 3-6 and Figure 3-7.

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Figure 3-9

Pressure and pressure difference maps for average temperature scenario: start (top) and end (bottom) of gas year 2023/2024. See for colour scales Figure 3-6 and Figure 3-7.

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Figure 3-10:

Pressure and pressure difference maps for average temperature scenario: start (top) and end (bottom) of gas year 2025/2026. See for colour scales Figure 3-6 and Figure 3-7.

32

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Figure 3-11:

Pressure and pressure difference maps for average temperature scenario: start (top) and end (bottom) of gas year 2027/2028. See for colour scales Figure 3-6 and Figure 3-7.

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Figure 3-12:

Pressure and pressure difference maps for average temperature scenario: start (top) and end (bottom) of gas year 2029/2030. See for colour scales Figure 3-6 and Figure 3-7.

34

3.4.2 Cold gas-year 2020/2021
Figure 3-13 and Figure 3-14 show the distribution over the regions in the case of a cold gas-year
2020/2021 followed by average years. Again production from Bierum is not required in OS2. Figure
3-15 shows production compared to capacity for each region in the two Operational Strategies.

Figure 3-13:

Monthly production volume per region for the next 6 gas-years for OS1 (top) and OS2 (bottom) in the cold gasyear scenario. Note the Bierum production cluster (BIR) is indicated in green.

Figure 3-14:

Regional distribution of production volume per gas-year for OS1 (top) and OS2 (bottom) in the cold gas-year
scenario

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 3-15:

Cluster capacity (blue) and production rate (green) for OS1 (left) and OS2 (right) in the cold gas-year scenario

Figure 3-16 shows the pressure distribution at the end of a cold gas-year for the two Operational
Strategies and their difference map.

36

Figure 3-16:

Pressure maps for the two Operational Strategies and pressure difference map at the end of a cold gas-year 2020/2021

37

3.4.3 Warm gas-year 2020/2021
Figure 3-17 and Figure 3-18 show the distribution over the regions in the case of a warm gas-year
2020/2021 followed by average years. Figure 3-19 shows production compared to capacity for each
region in the two Operational Strategies.

Figure 3-17:

Monthly production volume per region for the next 6 gas-years for OS1 (top) and OS2 (bottom) in the warm
gas-year scenario. Note the Bierum production cluster (BIR) is indicated in green.

Figure 3-18:

Regional distribution of production volume per gas-year for OS1 (top) and OS2 (bottom) in the warm gas-year
scenario

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 3-19:

Cluster capacity (blue) and production rate (green) for OS1 (left) and OS2 (right) in the warm gas-year scenario

Figure 3-20 shows the pressure distribution at the end of a warm gas-year for the two Operational
Strategies and their difference map.

39

Figure 3-20:

Pressure maps for the two Operational Strategies and pressure difference map at the end of a warm gas-year 2020/2021

40

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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4 Subsidence
In this HRA report for Groningen, subsidence is assessed for the period 2020-2030 for both the gas
field and connected aquifers. Ongoing pressure depletion of these reservoirs is driving the subsidence.
This chapter presents the forecast of surface subsidence based on “GTS-raming 2020”, average
temperature and Operational Strategy 1 as described in Chapter 3 of this report. In a series of updates
of the Winningsplan Groningen 2016 (Ref. 8 to Ref. 14), this chapter is an updated version of relevant
paragraphs on subsidence. As the subsidence and its effects will be less than assessed in Winningsplan
2016, only significant changes are presented.
The study area is defined by the Groningen gas field and the most important connected aquifers
surrounding the field (Figure 4-1). These aquifers are:
-

The “Southern Lauwerszee Trough (Zuidelijke Lauwerszee Trog)” aquifer located between
the Groningen field and the smaller fields of Vries and Roden.
In the north, the “Mowensteert” aquifer is connected to the Groningen gas field causing
possible subsidence in the Waddenzee.
The Rysum aquifer is connected to the eastern part of the field.
In the south, an aquifer between Annerveen and Groningen is connected to the Groningen
field.

The white coloured aquifers in Figure 4-1, e.g. the Goldhoorn aquifer to the east of the Groningen,
have no connection to the gas field due to large offset faults blocking lateral fluid flow and hence
preventing pressure communication. The “Noordelijke Lauwerszee trog” is mainly connected to the
Bedum and Warffum fields.
The subsidence model consists of a compaction model that is described in section 4.1 and an influence
model that translates the compaction in the subsurface to the earth’s surface. The influence model is
described in section 4.2.
Calibration of the model, the uncertainty estimation and the resulting subsidence forecast are
presented respectively in sections 4.3, 4.4 and 4.5.

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 4-1

Overview of the most important lateral aquifers attached to the Groningen field. In red the field names that
are mentioned in the text.

4.1 Compaction model
For the subsidence forecast the RTCiM model (Ref. 48) was used, as it gives the best match to core
deformation experiments when compared to any of the other compaction models. Another advantage
of the RTCiM model is that it spans a wide range of temporal behaviours to reflect the possible viscoplastic behaviour of the sandstone. The RTCiM model can behave more linear with depletion or exhibit
time decay and temporal characteristics. None of the other models is as versatile. This choice concurs
as well with the findings of the LTS-II research (Ref. 49).

4.1.1 Input to compaction model
Reservoir compaction is mainly dependent on pressure depletion, reservoir thickness and rock
compressibility. These parameters will be described in the following sections.
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4.1.1.1 Pressure scenario for the reservoir and laterally connected aquifers
The basis for the pressure scenario is the V6 reservoir model which includes a depleting Carboniferous
as a separate layer (Chapter 3).
In Operational Strategy 1 cluster Bierum is kept at a constant rate, while most of the volume is
produced from the South-East and South-West regions. When demand is high, production locations in
the Central-East region are added. In this report subsidence is modelled using the pressure results of
the Genrem-MoReS simulation using Operational Strategy 1 (base case).
For both the history-match and forecast period, annual reservoir pressure grids are exported from the
reservoir model to be used in the subsidence model. Dynamic reservoir model V6 is used (Ref. 35). The
V6 model includes several additional features compared to model V5, which provided the pressures
for the winningsplan 2016 and 2018 update calculations (Ref. 8 to Ref. 14, Ref. 56), e.g.:


Gas-in-aquifer (Slochteren formation)



Carboniferous formation (both gas and water bearing)



Additional depletion in the Lauwerszee aquifer resulting from the fields Roden, Vries, Pasop
and Faan

The inclusion of gas-in-aquifer improves the subsidence match and GWC rise in the north-western area
compared to model V5. The overall model match to measured reservoir pressures (SPG data) in V6 is
slightly improved from V5, with a Root Mean Square Error of around 2 bar when all pressure data up
to 1-1-2020 is included.
While there is ample pressure data available across the Groningen gas field, pressures in the
surrounding aquifers are much more uncertain due to lack of pressure data. The connected aquifers
(see Figure 4-1) are taken into account by the Groningen reservoir model and the V6 model gives a
pressure realisation constrained by measured subsidence data and pressures in the gas field. The V6
model includes pseudo-aquifer wells to mimic aquifer depletion in the Lauwerszee aquifer due to the
gas fields to the west (Roden, Vries, Pasop and Faan).

4.1.1.2 Reservoir Thickness
A reservoir thickness map from the V6 reservoir model reflecting the net thickness was used as input
for the compaction model (Figure 4-2). Net thickness is specified for both the ROSL and DC.

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Figure 4-2

Net reservoir thickness [m] of the V6 reservoir model. Left: Net thickness for ROSL. Right: Net thickness for DC.

4.1.1.3 Rock compressibility
In previous subsidence studies a relationship was used between the porosity and the compressibility
(Cm) of the rock. A recent study (Ref. 51), comparing Cm core plug measurements, in-situ
strain/compaction data (DSS) and sonic velocity, all acquired in the ZRP-3 well, showed that the
correlation of (DSS derived) Cm with sonic velocity is better than with porosity. The relation between
the compressional slowness (𝐷𝑇𝐶) and the compressibility (𝐶𝑚 ) estimated from the strain
measurements (assuming uniform depletion), derived by linear regression is:
𝐶𝑚 = 0.42 ∗ 𝐷𝑇𝐶 − 24.33
with 𝐷𝑇𝐶 in µs/ft and 𝐶𝑚 in µm/m/bar.
Figure 4-3 shows a comparison of the Cm core plug measurements, DSS estimated Cm values and Cm
derived from the compressional sonic velocity. A velocity map was derived from the Groningen seismic
cube and translated into a prior Cm grid for the ROSL applying the equation above. This ROSL Cm grid
is presented in Figure 4-4 (left).

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 4-3

Comparison of Cm values from core data (green markers), Cm values estimated from DSS (black), and Cm
derived from the compressional sonic, using the suggested linear regression (red line). Units used for Cm are
µm/m/bar for all three data sets.

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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The value for the DC Cm value was deduced from recent core experiments. These experiments show a
range of Cm values from 0.1 to 0.6 10-5bar-1. As no clear relation between compressibility and e.g. the
porosity of the samples was observed, we assumed a constant (average) value of 0.3 10-5bar-1 for the
DC (Figure 4-4, right).
These Cm maps for ROSL and DC were used as prior information into an inversion scheme, using the
geodetic data to derive the final Cm grids.

Figure 4-4

Input (prior) grid of Cm [10-5 bar-1] for the ROSL (left) and DC (right)

4.2 Influence model
The influence model translates the compaction of the reservoir into surface subsidence. In Ref. 50 on
subsidence above the Ameland field, it was concluded that a thick salt layer above the reservoir
significantly impacts the temporal behaviour of the subsidence. Compared to the Groningen field, the
Ameland field is relatively small, where compaction leads to stress arching in the overburden, changing
the shear stress above the reservoir. These shear stresses cause creep deformation in the salt resulting
in a narrower and more profound subsidence bowl. Due to its large size, stress arching is nearly absent
above the Groningen field and therefore it is assumed in this study to neglect salt creep. Still the salt
can result in a steeper edge of the subsidence bowl, a phenomenon that already was recognised and
described earlier (Ref. 52). We adopted therefore the influence model as described in Ref. 52, that
combines a half-space model with a rigid basement using a value of 7 km for the rigid basement and a
Poisson’s ratio of 0.2. The effect of this model results in a change of the bowl shape that is time
independent.

4.3 Calibration of compaction model parameters
Following a similar methodology as described in Ref. 50, the input data described above was combined
with surface deformation measurements to calibrate the compaction model parameters, i.e. optical
spirit-levelling data was used for inversion to compaction and calibration of the compaction model.
Measurement results from 16 levelling campaigns spanning from 1964 to 2018 were used. After
network adjustment of the individual campaigns, spatio-temporal double-differences were derived to
serve as input to the modelling/inversion workflow. Only stable benchmarks (Ref. 53) were used in the
calibration, providing a selection of some 10000 double differences.
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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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New items in the inversion workflow are the application of strain data and the V6 reservoir model
pressure data for both the ROSL and DC reservoirs. The inversion was carried out for both the ROSL
and DC. Figure 4-5 shows the results for the (final) ROSL and DC 𝐶𝑚 maps.

Figure 4-5

Left: Cm of the ROSL after the inversion using both velocity data and geodetic data. Right: Cm for the DC
(carboniferous). (Cm in 10-5bar-1)

4.4 Uncertainty estimation
A new approach was adopted to quantify the uncertainty of the model results. The objective of the
approach is to define a 95% prediction interval, i.e. 95% of the measured double differences should
fall within this interval/range around the modelled values. It is likely that the absolute difference
between the modelled and measured values increases with magnitude and the simplest
implementation is to adopt an uncertainty that is a linear function of the double difference value. To
define this relation an intercept value and a slope need to be assessed. We therefore sort the modelled
double differences from low to high values and split them up in 30 bins (Figure 4-6). Per bin we choose
the uncertainty such that 95% of the data falls within the range. This way each bin produces a point
that represents the uncertainty value per bin. Next a linear regression line is calculated through all
points (Figure 4-7).

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 4-6

Modelled double differences (green) and their (absolute) difference with the measured subsidence

Figure 4-7

Uncertainty per bin with linear regression.

The following linear expression was found to obtain the P95 uncertainty:
𝑃95 𝑢𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦 = 1.53 (𝑐𝑚) + 𝑚𝑜𝑑𝑒𝑙𝑙𝑒𝑑 𝑠𝑢𝑏𝑠𝑖𝑑𝑒𝑛𝑐𝑒 ∗ 0.032 (𝑐𝑚)
With this equation the boundaries of the prediction interval were calculated.

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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4.5 Subsidence forecast
In this section the current status and expected development of subsidence up to 2030 is presented.
Subsidence caused by gas production and aquifer depletion from the Groningen field is combined with
the effects from ongoing gas production from neighbouring fields as published in the
“winningsplannen” for these fields. Most of the expected subsidence has already occurred over the
last 60 years.
The forecast uncertainty is a combination of uncertainties of the various model components. The
measured subsidence since the start of production and all available intermediate measurement
intervals were used to calibrate the model parameters as accurately as possible by means of inversion,
explained in section 4.3.
Figure 4-8 shows the results of the subsidence model in comparison with the measured subsidence in
benchmark locations across the gas field for the period 1972-2018.

Figure 4-8

Contours (solid blue lines) of the modelled subsidence between 1972 and 2018 compared to the measurements
(green dots with value label) spanning the same period. All values are in cm.

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Subsidence forecasts for the years 2025 and 2030 are presented in respectively Figure 4-9 and Figure
4-10. The base case is presented by the contour lines while the P95 uncertainty is visualized by the
coloured overlay. Note that the uncertainty is valid only for Groningen, i.e. the uncertainty shown
above the neighbouring fields like Annerveen and Vries is the uncertainty in subsidence caused only
by gas production from Groningen. Uncertainty for the other fields is not calculated here.

Figure 4-9

Subsidence in cm for 2025 (contours). The colours indicate the P95 uncertainty in the subsidence.

50

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Figure 4-10

Subsidence in cm for 2030 (contours). The colours indicate the P95 uncertainty in the subsidence.

Figure 4-11 shows a comparison of the current subsidence forecast for 2030 with the previous forecast
presented in the 2018 report “Assessment of Subsidence based on Production Scenario ‘Basispad
Kabinet’ for the Groningen field” (Ref. 18).

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Figure 4-11

Comparison of current subsidence forecast for 2030 with forecast of “Basispad Kabinet” (issued in 2018).
Top: Current forecast. Bottom: “Basispad Kabinet”. All contours in cm.

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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To visualise the match between modelled and measured subsidence over time since the first levelling
surveys, a number of plots are presented in Figure 4-12, showing modelled and measured subsidence
at various benchmark locations over the Groningen field.

Figure 4-12

Subsidence at benchmark locations: dark grey line is the predicted subsidence, grey is the P95 uncertainty
interval, black dots are levelling measurements plus uncertainty, the blue dots are the InSAR measurements.

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5 Seismic Event Rate
5.1 Event Rate forecast
Based on the production profiles GTS-raming 2020, the number of earthquakes with a magnitude
larger than or equal to M=1.5 have been forecasted. Figure 5.1 shows the annual number of
earthquakes forecasted until gas-year 2033/2034 for Groningen field volume production for the “GTSraming 2020” and average temperature profile. The number of earthquakes shows a declining trend.
In gas-year 2018/2019, 11 earthquakes were recorded with a magnitude larger than M=1.5. Table 5.1
shows the recorded earthquakes for recent gas-years and calendar years. The decline in recorded
earthquakes is a consequence of the decreasing gas production. Production from the field ceases,
when the new nitrogen blending plant comes on stream in April 2022.
Starting
year
2012
2013
2014
2015
2016
2017
2018
2019
2020
Table 5.1

Ending
year
2013
2014
2015
2016
2017
2018
2019
2020
2021

Gasyear
21
19
18
12
16
17
11
3*

Calendar
year
16
25
18
20
12
16
14
11
1*

Number of observed earthquakes with a magnitude larger or equal to M=1.5 for both gas-years and
calendar years. The gas-year is the one year period from 1st October in the starting year to 1st October in the
ending year. The calendar year is the one year period 1st January in the starting year to 1st January ending
year. Number of earthquakes indicated with an * are not for a full year, but up to 24 th February 2020.

The seismic activity rate declines to an expected 4 earthquakes per year in gas-year 2023/2024, with
an uncertainty range of 1 to 10 earthquakes per year. The seismic activity rate beyond 2020 is primarily
driven by the pressure equilibration in the field, between the high-pressure area North-West of
Loppersum and the lower-pressure area South-East of the field (Ref. 16).
To further illustrate this, the event rate for a production profile (labelled Reference) based on cessation
of production at the end of the current gas-year (2019/2020) is also shown in figure 5.1. The prognosed
seismic event rate for this profile is very close to that for the GTS-raming 2020 profile. This sensitivity
shows that the prognosed earthquakes are primarily the result of the pressure equilibration in the
reservoir, between the low pressure south-east and the high pressure Loppersum region. This
comparison demonstrates the impact of the gas production after 1st October 2020 on the seismicity is
very limited.
Because the impact of future gas production on the seismic event rate is very limited, it is expected
the impact of the operational strategy for the production of this gas will be even smaller. This can be
seen by comparing both sides of figure 5.4.

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Figure 5.1

Seismic Activity Rate of earthquakes for the period gas-years 2012/2013 to 2034/2035 based on production
profile GTS-raming 2020 for the average weather temperature gas-year and production profile for
operational strategy 1. The dark grey line indicates the expected number of earthquakes in each gas-year
and the grey area the uncertainty band. The solid black line indicated the historical event rate. The dashed
black line indicates the expected number of earthquakes when the field is closed in at the end of the current
gas-year 2019/2020 (1st October 2020). GY2012 refers to gas-year 2012/2013.

Figure 5.2

Seismic Activity Rate of earthquakes for the period 2012 to 2033 (production profile GTS-raming 2020 for the
average weather temperature calendar-year and production profile for operational strategy 1. The dark
grey line indicates the expected number of earthquakes in each calendar-year and the grey area the
uncertainty band.

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The expected impact of weather temperature uncertainty is within the uncertainty band for event rate
of the average temperature profile. In figure 5.3 the seismic activity rate for three profiles is shown;
the average temperature profile, an average temperature profile when gas-year 2020/2021 is a cold
gas-year and an average temperature profile when gas-year 2020/2021 is a warm year and the later
years with average temperature weather. Especially in year 2021 the activity rate is higher for the cold
year profile (11 earthquakes at mean) than for the average temperature profile (8 earthquakes at
mean) and lower for the warm year profile (7 earthquakes at mean).

Figure 5.3

Seismic Activity Rate of earthquakes for the period gas-years 2012/2013 to 2034/2035 based on production
profile GTS-raming 2020 for operational strategy 1 for average temperature profile, cold temperature profile
and warm temperature profile. For the cold temperature profile gas-year 2020/2021 is a cold year (with
higher gas demand) and all following years are again an average year.

The event rate for both operational strategies are compared in figure 5.4. The mean event rate is very
similar for both operational strategies. Only the uncertainty band around the mean event rate shows
very slight differences.

Figure 5.4

The seismic event rate for the average temperature profile gas-year of GTS-raming 2020 for both operational
strategies.

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5.2 Exceedance Probabilities
The probability of an earthquake with a magnitude exceeding a given magnitude can be assessed using
the seismological model (Ref. 40 to Ref. 43). In table 5.2a and 5.2b the annual probability of an
earthquake occurring with a magnitude exceeding the specified magnitude is given. For instance, the
probability of an earthquake occurring in gas-year 2020/2021 with a magnitude exceeding ML=3.6 (the
magnitude of the Huizinge earthquake) is equal to 4.73%. In the HRA for GTS-raming 2019, this
probability was close to 9.3%. The probability of an earthquake occurring in gas-year 2020/2021 with
a magnitude exceeding ML=3.6 has halved between these two assessments (HRA GTS-raming 2019 and
HRA GTS-raming 2020). For larger exceedance magnitudes the reduction between these two HRA
versions is even larger.
Gas-year
2020/2021
2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
2030/2031
Table 5.2a

P(M>=4.0)
1.29%
0.97%
0.83%
0.74%
0.64%
0.57%
0.55%
0.50%
0.46%
0.43%
1.29%

P(M>=4.5)
0.16%
0.12%
0.11%
0.09%
0.08%
0.08%
0.07%
0.07%
0.06%
0.06%
0.16%

P(M>=5.0)
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%

Table with annual probabilities for occurrence of earthquakes exceeding a set magnitude. This table is for
production profile GTS-raming 2020, average temperature gas-year and operational strategy 1.

Gas-year
2020/2021
2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
2030/2031
Table 5.2b

P(M>=3.6)
4.73%
3.64%
3.09%
2.78%
2.43%
2.20%
2.10%
1.93%
1.80%
1.67%
4.73%

P(M>=3.6)
4.88%
3.58%
3.03%
2.74%
2.47%
2.31%
2.09%
1.91%
1.84%
1.67%
4.88%

P(M>=4.0)
1.30%
0.94%
0.84%
0.74%
0.68%
0.60%
0.56%
0.52%
0.48%
0.45%
1.30%

P(M>=4.5)
0.17%
0.12%
0.10%
0.09%
0.09%
0.08%
0.07%
0.07%
0.06%
0.06%
0.17%

P(M>=5.0)
0.02%
0.02%
0.01%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%

Table with annual probabilities for occurrence of earthquakes exceeding a set magnitude. This table is for
production profile GTS-raming 2020, average temperature gas-year and operational strategy 2.

5.3 Event Density Maps
Annual event density maps for the two optimised Operational Strategies are compared in figure 5.5.
These maps are based on the production profile “GTS-raming 2020” for an average temperature year.
The scale for the comparison plot has been chosen at 0.01 earthquake/km2 to highlight the small
differences resulting from the operational strategies in gas-year 2020/2021. Even with this very

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sensitive scale the differences fade away in later years, especially after production from the field has
been terminated.

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2020/2021

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2021/2022

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2022/2023

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2023/2024

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2024/2025

64

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2025/2026

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2026/2027

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2027/2028

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2028/2029

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2029/2030
Figure 5.5

Annual event density maps for the two optimised Operational Strategies compared. These maps are based on the production profile “GTS-raming 2020” for an average
temperature year.

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6 Seismic Hazard Assessment
6.1 Hazard metrics
Different metrics have been proposed to describe the hazard resulting from seismic activity. Most
commonly used are the peak ground velocity (PGV) and peak ground acceleration (PGA). In the current
report, the hazard assessment is based on Ground Motion Prediction Methodology (GMM) Version 6
(Ref. 38 and Ref. 39).

6.1.1 Risk Assessment
Peak Ground Acceleration (PGA) is a widely used metric for ground shaking intensity and was chosen
as the most appropriate hazard metric for the seismic hazard assessment in support of the assessment
of risk. Figure 6.1 shows the measured acceleration near the epicentre during the Huizinge earthquake
of 16th August 2012. For the assessment of the response of a building to ground shaking average
spectral acceleration (AvgSa) is used. It is the mean of the sum of the natural logarithms of spectral
acceleration at 10 different periods of vibration. Ground Motion Prediction methods have therefore
focused on prediction of PGA and spectral acceleration at several periods. These are the most
important hazard parameters for the prediction of full or partial building collapse, failure of building
elements and hence for personal risk.

6.1.2 Building Damage Assessment
For the assessment of the potential to cause building damage at lower damage states (see section 8 of
this report), velocity-based hazard metrics such as PGV (Peak Ground Velocity) are also important.
Empirical evidence elsewhere has shown that building damage at lower damage states (damage states
DS1 and DS2) correlates strongly with PGV. A Groningen-specific (induced) ground motion prediction
method to estimate the value of PGV at specific locations has therefore been developed as part of the
Ground Motion Prediction Methodology since version 4. The assessment of PGV is primarily in support
of assessment of building damage due to historical earthquakes and expected future damage.
The official Dutch guidelines for assessing the impact of vibrations on buildings, as presented in the
document “Building Damage: Measurement and Assessment” by the SBR (Ref. 61), are based on the
ground velocity metric VTOP. To ensure consistency with the SBR Guidelines, apart from the geometric
mean velocity also a ground motion prediction method for the VTOP parameter (the ‘maximum’ value
of PGV in any direction) was developed.

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PGA [cm/s2]

Vertical Component

Huizinge Event 16-8-2012 (Westeremden station)

PGA [cm/s2]

Transverse Component

Time [s]

PGA [cm/s2]

Radial Component

Time [s]

Time [s]
Figure 6.1

Accelerogram of the earthquake near Huizinge recorded at the 16th August 2012 by the accelerometer located
near Westeremden (near the epicentre).

6.1.3 Hazard Map for Peak Ground Acceleration
For the probabilistic description of the ground accelerations (PGA, or generalised to Pseudo Spectral
Acceleration, PSA), a hazard map is used. On this map for each location the acceleration is plotted that
could occur, with a prescribed annualised probability of exceedance (exceedance level), during a
prescribed analysis period. Hazard levels are shown using a gradual colour scale.
The hazard maps shown in this document were constructed according to the following procedure. Each
location in the analysis area during the analysis period is subjected to ground motion accelerations
resulting from induced earthquakes. At some locations at the centre of the field, e.g. near Loppersum,
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the chance of exceeding a given peak ground acceleration threshold is higher than at the periphery of
the field, e.g. in Groningen city (Fig. 6.2).

Figure 6.2

Hazard curves for the average temperature profile gas-year 2020/2021 of GTS-raming 2020 for operational
strategy 1 for two different locations in the Groningen area. Left for the village of Loppersum. Here the
probability of being exposed to an acceleration exceeding larger than 0.09 g is 0.2% per year. On the right the
hazard curve for the city of Groningen, the acceleration with an exposure of 0.2% per year is lower at 0.03 g.
The black line notes the mean hazard curve, whereas the grey area notes the 80% prediction interval.

Equally, at any one location, the chance of exceeding some value of peak ground acceleration
decreases with increasing peak ground acceleration. A set of hazard curves is shown for a number of
locations in figure 6.3. Each declining line indicates the hazard curve for a single location in the field.

Figure 6.3

A set of hazard curves showing average annual exceedance rate for peak ground acceleration at different
locations in the field. Each line corresponds to a location in the field. The bold line indicates the maximum
PGA anywhere within the field for a given exceedance level (bounding envelope). In this figure, the red line
indicates that for an exceedance level of 0.2%/year the highest PGA in the field is 0.119 g. This plot was
prepared based on the average temperature production profile of GTS-raming 2020 for Operational Strategy
1.

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To prepare a hazard map, an exceedance level needs to be chosen. This is not a purely technical choice.
However, inspired by Eurocode 8, part of the current technical standards for structural design in
Europe, it has become common practice to prepare hazard maps for an exceedance level of 0.2%/year.
This exceedance level is equivalent to a 475-year return period for stationary seismicity. The same
exceedance level is also used by KNMI for their hazard maps, which allows for comparison of these
hazard maps. The choice of the exceedance levels (or return period) is only for the representation of
the hazard. This choice of exceedance level does not affect the subsequent assessment of risk. Hazard
maps can also be prepared for spectral acceleration at a specified period. The standard PGA hazard
map is the same as the spectral acceleration hazard map at shortest period, which for this assessment
was chosen at 0.01 s.

6.2 Hazard Assessment
Hazard maps have been prepared for the next ten gas-years starting at 1st October and running to 1st
October a year later. Separate hazard maps are available for the production profile “GTS-raming 2020”
at average temperature profile, cold temperature profile and warm temperature profile for gas-year
2020/2021.
The hazard map for the average temperature profile for each gas-year of the period 2020/2021 to
2029/2030 is shown in figure 6.4 a, b, c. The hazard is, as expected based on the declining gas
production profile, also decreasing over this period. The trend in the largest PGA in these annual
hazard maps is shown in figure 6.5. After gas-year 2021/2022, the differences between the largest PGA
for the different temperatures profiles and operations strategies are very small (within 0.003 g from
the case of average temperature and operations strategy 1).
However, this reduction in PGA is not evenly spread over all areas of the field. In the later years, the
hazard is primarily located in the area North-West of Loppersum. This is consistent with the
equilibration of reservoir pressures during these later years. The gas from the higher-pressure area to
the North-West of Loppersum will continue to flow to the lower pressure South-Eastern area, causing
a continued decrease of pressure in the former area. This effect of gas flow within the reservoir due
to equilibration of pressure differences is referred to as the “remweg effect”. In theoretical remweg
production profiles this effect has been demonstrated (Ref. 16). The hazard map for year 2020/2021
for production profile GTS-raming 2020, an average temperature year and operational strategy 1 is
shown in large format in figure 6.6.
Figure 6.7 shows the impact of the operational strategy. Hazard maps for both operational strategies
are shown for each year from 2020/2021 to 2029/2030, together with a difference map. The scales of
the difference maps are set to +/- 1% of g. The impact of low and high temperature weather in
2020/2021 is shown in figure 6.8.

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Figure 6.4a

2020/2021

2021/2022

2022/2023

2023/2024

Hazard Maps for the average temperature weather profile for the gas-years 2020/2021 (top – left), 2021/2022
(top – right), 2022/2023, (bottom – left) and 2023/2024 (bottom – right). The production profile for these
hazard maps is “GTS-raming 2020, for an average temperature year and Operational Strategy 1. Note that
water-bodies, such as the Schildmeer, are masked in these hazard maps.

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Figure 6.4b

2024/2025

2025/2026

2026/2027

2027/2028

Hazard Maps for the average temperature weather profile for the gas-years 2024/2025 (top – left), 2025/2026
(top – right), 2026/2027, (bottom – left) and 2027/2028 (bottom – right). The production profile for these
hazard maps is “GTS-raming 2020, for an average temperature year and Operational Strategy 1.

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2028/2029
Figure 6.4c

2029/2030

Hazard Maps for the average temperature profile for the gas-years 2028/2029 (left), 2029/2030 (right). The
production profile for these hazard maps is “GTS-raming 2020”, for an average temperature year and
Operational Strategy 1.

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Production Profile GTS-raming 2020
Largest Acceleration on Hazard Map [g]

0,15
Cold

0,14
Average

0,13
Warm

0,12
Cold

0,11

Average

0,1

Warm

0,09

0,08

0,07

2020/2021 2021/2022 2022/2023 2023/2024 2024/2025 2025/2026 2026/2027 2027/2028 2028/2029 2029/2030

Gas-years

Figure 6.5

Development over time of the largest PGA in the hazard maps. The largest PGA for each year in the hazard
map for production profile “GTS-raming 2020 is plotted. Solid lines are for operational strategy 1 and dashed
lines for operational strategy 2.
The blue lines denotes the largest PGA for each year in the hazard map for the GTS-raming 2020 cold
temperature profile.
The green lines denotes the largest PGA for each year in the hazard map for the GTS-raming 2020 average
temperature profile.
The red lines denotes the largest PGA for each year in the hazard map for the GTS-raming 2020 warm
temperature profile .

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Figure 6.6

Hazard Map for the average temperature weather profile for the gas-year 2020/2021. This is the same map
as figure 6.4a (top-left) shown in a larger format. The production profile for these hazard maps is “GTS-raming
2020, for an average temperature year and Operational Strategy 1. Note that waterbodies, such as the
Schildmeer, are masked in these hazard maps.

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2020/2021

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2021/2022

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2022/2023

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2023/2024

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2024/2025

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2025/2026

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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2026/2027

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2027/2028

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2028/2029

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Operational Strategy 2

Operational Strategy 1

Difference
(Operational Strategy 2 – Operational Strategy 1)

2029/2030
Figure 6.7

Annual hazard maps for the two optimised Operational Strategies compared. These maps are based on the production profile “GTS-raming 2020” for an average temperature
year.

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Cold Temperature

Average Temperature
2020/2021

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2021/2022

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2022/2023

Warm Temperature

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Cold Temperature

Average Temperature
2023/2024

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2024/2025

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2025/2026

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2026/2027

Warm Temperature

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Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
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Cold Temperature

Average Temperature
2027/2028

Warm Temperature

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Cold Temperature

Average Temperature
2028/2029

Warm Temperature

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Cold Temperature
Figure 6.8

Average Temperature
2029/2030

Warm Temperature

Gas-year annual hazard maps for the three temperature cases compared. These maps are based on the production profile “GTS-raming 2020”, Operational Strategy 1

99

6.3 Disaggregation of Seismic Hazard
The probabilistic seismic hazard assessment for the Groningen region depends on contributions from
both aleatory variabilities and epistemic uncertainties. We include aleatory variabilities by Monte Carlo
(MC) sampling of the joint probability distributions governing the number, location, origin time,
magnitude, rupture extent, rupture strike and rupture directivity of induced earthquakes and the base
rock spectral accelerations, and near surface amplifications of the associated seismic ground motions.
We include epistemic uncertainties by extending the MC sampling procedures to include an ensemble
of mutually independent and collectively exhaustive alternative hazard models within the hierarchical
structure of a logic tree. This logic tree comprises the full factorial combination of 4 factors: the
frequency-magnitude model (2 levels), the maximum possible magnitude model (7 levels), the groundmotion phi-model (2 levels), and the ground motion tau-model (4 levels). This results in a logic tree
with 112 independent branches that are evaluated by independent MC simulations. These logic tree
results describe a detailed discrete sampling of the joint seismic hazard probability distribution over
the aleatory and epistemic variables.
Seismic hazard maps summarise the pseudo spectral accelerations (PSA) with a given exceedance
probability as the mean of the joint seismic hazard probability distribution. Through disaggregation we
also characterize the marginal hazard distributions for many of the key aleatory and epistemic
variables. Due to practical digital storage and RAM limits we choose not to retain every MC sample of
the joint hazard distribution. Instead, we store discrete joint distributions of PSA and selected aleatory
variables for every spectral period and surface location. The top row of Figure 6.9 shows examples of
these binned joint distribution sampled across the entire logic tree and represented as the mean
annual occurrence rate. Each individual pixel in these plots represents an individual sample bin. The
large number of small pixels within each plot shows the high-resolution of discrete sampling achieved
for each joint distribution. We show these occurrence rates using a logarithmic colour bar spanning 6
decades from 10-3/year down to 10-9/year deep into the tails of these occurrence rate distributions.
Grey pixels denote bins with no sampled occurrences, presumably because their occurrence rates are
smaller than 10-9/year. The joint PSA-magnitude distribution (Figure 6.9, top row, left column) shows
that the largest occurrence rates correspond to the smallest magnitudes that we include in the hazard
analysis (M=3.5). The positive correlation between PSA and magnitude shows how higher magnitude
events typically yield higher PSA events. Looking along a horizontal line across this joint distribution,
we observe a general increase in occurrence rate with increasing magnitude up to some peak rate
corresponding to an intermediate modal magnitude, and beyond that the occurrence rates decrease.
Below this modal magnitude the PSA occurrence rate is limited by the earthquake size whilst above
this modal magnitude the PSA occurrence rate is limited by the earthquake occurrence rate. This modal
magnitude depends on which horizontal line we select, selecting a larger PSA value yields a larger
modal magnitude as expected. We also observe vertical edges in this distribution due to the imprint
of the discrete Mmax distribution, where occurrence rates step down since fewer logic tree branches
permit magnitudes above this value.
As we measure seismic hazard according to exceedance rates and not occurrence rates, we obtain the
marginal hazard distribution for magnitudes (Figure 6.9, bottom row, left column) by summing the
pixels values on and above a given mean PSA value. Doing this for the PSA value with a mean annual
exceedance probability of 0.2105%/year and normalizing the distribution yields relative contribution
of each magnitude bin to the local seismic hazard at that location, spectral period, and exceedance
probability (Figure 6.9, bottom row, left column). In this example, (Loppersum, 0.01s, 0.2105%/year),
the modal hazard contribution corresponds to magnitude, M=4. In comparison the contributions of
M=3.5 and M=5.0 are about 10 times smaller. This supports our MC simulation choice to truncate
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seismic hazard sampling for M < 3.5 events as these make a negligibly small contributions to this mean
hazard metric. We also note that 5 of the 7 Mmax logic tree levels (Mmax = 5.0, 5.5, 6.0, 6.5, 7.0) do not
significantly contribute to this mean seismic hazard as they are all too far above the modal hazard
contribution (M = 4.0).
We repeat this sampling of joint distributions for PSA and the distance, Rrup, between the rupture plane
and the surface location (Figure 6.9, middle column), and for PSA and the total ground motion
variability, GM, about the median ground motion model. These marginal hazard contributions (bottom
row) indicate modal contributions from earthquakes located directly below Loppersum (Rrup = 3 km),
and 1.5 standard deviations of the log-normal ground motion distribution above its median (GM = 1).
Moving from the Loppersum location to the Groningen city location (Figure 6.10), changes the marginal
hazard contributions (Figure 6.10, bottom row) which now exhibit a modal magnitude M = 4.5, a modal
distance Rrup = 7 km and a modal ground motion variability GM = 1. This increase in modal magnitude
and distance indicates that the centre of seismicity that contributes most to this seismic hazard is not
directly below the city of Groningen but instead offset by about 6 km. This likely corresponds to the
secondary centre of event rates located east of Groningen city and south of the primary Loppersum
centre of seismicity. The Loppersum seismicity region contributes as a secondary mode in the marginal
hazard distribution, Rrup = 17 km, (Figure 6.10, bottom row, middle column).
Figure 6.11 shows maps to summarize how these modal contributions vary over the entire region of
seismic hazard analysis. In summary, for seismic hazard with a 0.21%/year chance of exceedance, the
modal hazard contributions throughout the hazard analysis region are due to 3.9 < M < 4.5 magnitudes,
3 km < Rrup < 15 km distances, and 0.5 <  < 1.5 ground motion variability. Spatial variations in the
modal distance contribution reflect the distance to the closest centre of seismicity. Within central
regions located above a higher areal density of reservoir seismicity the modal distance corresponds to
the reservoir depth (3 km) indicating the local seismic hazard is governed by the local underlying
seismicity. Outside this central region, the local underlying seismicity is insufficient to be the primary
cause of seismic hazard, which instead is driven by seismicity occurring in the central region with
seismic ground motions that are diminished by distance. Likewise, modal magnitude is typically M =
3.9 – 4.2 in places located directly above regions with a higher areal density of reservoir seismicity such
as Loppersum. Elsewhere this increases to M = 4.5 in regions where the higher reservoir seismicity
densities are at least 5 km away. In Groningen city, Delfzijl and Eemshaven, the largest contribution
comes from M = 4.2 – 4.5 earthquakes. The small region of M = 3.5 north of Winschoten is characterize
by low seismic hazard and we interpret this feature as a likely finite sample artefact of the seismic
disaggregation process that converges more slowly for lower seismic hazards.

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Figure 6.9

(a) Occurrence rates for peak spectral acceleration at a spectral period of 0.01 s as a function of magnitude,
distance, and GMPE epsilon, ε, for a single surface location directly above the region of maximum reservoir
compaction. Grey denotes no occurrence in any of the simulations. (b) The fractional contribution to the
ground motion with a 0.2% annual probability of exceedance for gas-year 2020/2021 using the production
profile GTS-raming 2020 for the average temperature weather and operational strategy 1.

Figure 6.10

As previous figure, except for a surface location in the centre of Groningen city.

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Figure 6.11:

Maps of the magnitude, M, distance, Rrup, ground motion variability, GM that contribute most to the mean
PSA (T=0.01s) hazard with a 0.21% annual chance of exceedance.

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7 Seismic Risk Assessment
7.1 Risk Metrics
The results from the probabilistic seismic Hazard and Risk Assessment (HRA) are summarised via risk
metrics, which are related to the annualised probability of fatality for an individual person or for groups
of people, taken as an average across the forecast period of the Hazard and Risk Assessment.
When assessing risk, it is important to select a risk metric that is appropriate given the purpose of the
risk assessment. In many cases there is more than one option available as to which metric to use. An
advisory committee, Commissie Meijdam, was established in early 2015 to advise the Minister of
Economic Affairs and Climate Policy on risk policy related to Groningen earthquakes, including the
selection and definition of the appropriate risk metrics. In December 2015, the Commissie Meijdam
shared its third and final advice with the Minister of Economic Affairs and Climate Policy (Ref. 28 to
Ref. 30). The selection of risk metrics for this Hazard and Risk Assessment is based on the final advice
published by Commissie Meijdam. The Meijdam-norm was adopted by the Minister (Ref. 62) and first
implemented in the HRA for Winningsplan 2016 (Ref. 8 to Ref. 14).

7.1.1 Individual Risk Metrics
7.1.1.1 Object-related Individual Risk and Individual Risk
The Commissie Meijdam introduced two individual risk metrics: Individual Risk (IR) and Object-related
Individual Risk (OIA). Table 7.1 lists the definitions of the individual risk metrics used in the assessment
of risk for induced seismicity in the Groningen field area. The fundamental principle of the advice of
the Commission is that living and working in Groningen must be as safe as elsewhere in the
Netherlands. In Groningen the same safety standards must apply as elsewhere in the Netherlands.
Based on this principle the Committee Meijdam established the norm that Individual Earthquake Risk
(IR) should be below 10-5/year.
For buildings with an OIA above 10-4/year, immediate action is required. In principle these buildings
need to be strengthened immediately or vacated. Buildings with an OIA between 10-4/year and
10-5/year need to be strengthened within a reasonable period. The involvement of the Minister of EZK
over the past years with the structural upgrading has been formally implemented in the Mining law in
December 2019.

7.1.1.2 Inside and Outside Local Personal Risk
To perform the calculations, NAM uses as an intermediary result, the individual risk metric LPR (Local
Person Risk) consisting of two components: Inside Local Personal Risk (ILPR) and Outside Local Personal
Risk (OLPR). The use of LPR is analogous to safety assessment domains like those used for industrial
activities and pipelines. “Local Personal Risk” (LPR) is generally defined as the annual probability of
fatality for a fictional person, who is continuously present, without protection, at a specific at-risk
location. For Groningen earthquakes, LPR is defined as follows: “the probability of death of a fictional
person who is permanently in or near a building” (Ref. 30). The location of the person within the
building is uniformly and randomly distributed inside the building. The relationship between the
percentage of collapse (floor percentage covered in debris) and chance of a fatality in the building is
described by the consequence model (Ref. 55 and 80).

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Risk Metric

Dutch Name

Object-related
Individual Risk

Objectgebonden
Individueel
Aardbevingsrisico
(OIA)

Individual
Earthquake Risk

Individueel
Aardbevingsrisico
(IAR)

Inside Local
Personal Risk
(ILPR)
Outside Local
Personal Risk
(OLPR)
Local Personal
Risk (LPR)

Plaatsgebonden
Persoonlijk Risico
Binnen
Plaatsgebonden
Persoonlijk Risico
Buiten
Plaatsgebonden
Persoonlijk Risico

Table 7.1

Definition
The Objectgebonden individual earthquake risk is
the risk that an individual dies in a year due to
collapse or falling objects (as a result of an
earthquake) of a building in which or in the direct
vicinity of which this person is present. The
residence time in/around that building is therefore
taken into consideration.
The individual earthquake risk is the annual risk that
an individual is exposed to in the various structures
in or near which this individual is present.

The probability of death of a fictional unprotected
person who is permanently present in a building.
The probability of death of a fictional unprotected
person who is permanently present near a building.

Purpose(s)
Local individual risk metric to measure fatality risk due to
structural and non-structural collapse (LPR, see below), weighted
by the average residence times of the individuals in/around the
building (OIA), relative to norm of 10-5/year overall individual risk.
Check if any buildings have occupants with an average OIA above
10-4/year (high priority for immediate action).
Individual risk metric that is not considered at present (as requires
knowledge of the presence of all members of the Groningen
community throughout the day, in order to sum up all their objectbound individual risks over a 24-hour period).
Local risk metric to measure fatality risk due to collapse of a given
building and its non-structural elements both inside (ILPR) and
outside (OLPR) the building, relative to the norm of 10-5/year
overall individual risk.
Check if any buildings have Local Personal Risk above 10-4/year
(high priority for immediate action).

The probability of death of a fictional unprotected
person who is permanently present in or near a
building. This person is thought to be inside the
building 99% of the time and outside near the
building 1 % of the time.

Overview of the individual risk metrics used in the assessment of risk for induced seismicity in the Groningen field area.

106

Inside Local Personal Risk is the probability of fatality for an individual continuously present in a
building. It is associated with partial or full collapse of the building. Outside Local Personal Risk is the
probability of fatality for an individual continuously present in the direct vicinity of a building. It is
primarily associated with failure of non-structural elements, the so-called falling objects3 (chimneys,
balconies, parapets, etc.). In this context the vicinity of a building is taken as within 5 m from the
building.
The Inside Local Personal Risk and Outside Local Personal Risk are aggregated to Local Personal Risk
(LPR) assuming a fictional person is 99% of the time inside a building and 1% of the time outside the
building, but in the direct vicinity of the building. The definition of these risk metrics is also listed in
table 7.1.
As Local Personal Risk applies to a fictional unprotected person who is permanently present
(everywhere) in a building, it is a property of this building. It is independent of the actual occupancy
of the building; how many people are present in the building and the duration of their presence are
not taken into account when assessing the LPR of a building. LPR is presented as a cumulative
distribution (of buildings versus risk level), which provides an estimate of the number of buildings that
do not comply with the norm.

7.1.1.3 Current Practice in Risk Assessments
However, the Meijdam-norm applies to Individual Risk. To assess Individual Risk for a person in a
rigorous manner knowledge of the buildings the person visits during the day and the duration the
person is present in these building is required. The Individual Risk of a person is the duration weighted
sum of the LPR of the buildings visited during the day. The time a person spends away from buildings
does not contribute to individual risk.
NAM uses a practical and conservative approach, whereby the number of buildings with a mean LPR
is evaluated and compared to the threshold safety levels of the Meijdam-norm. The underlying
principle was that if all buildings have a mean LPR below the norm, no persons can be exposed to an
IR above the norm. The LPR inherently assumes a full-time (100%) residency of the building. In the
Hazard and Risk Assessment, the mean LPR is the primary metric used to compare against the 10-5/year
individual risk norm (as recommended by Commissie Meijdam, which requires the individual risk for a
person to be less than 10-5 per year). Note that individual risk metrics that account for the proportion
of time a person spends in the building will yield a lower calculated individual risk (IR) than LPR
(particularly for buildings occupied a small proportion of the time).
Mid-2018, the Minister sought the advice of a panel of professors on the implementation of the life
safety norm in the HRA methodology. This panel of professors pointed out that the methodology
implemented in the HRA is a conservative approach (Ref. 63 to Ref. 67), which inherently assumes that
for each building individuals spend their full time there. In the HRA for GTS-raming 2019 (Ref. 19) the
difficulties of implementing a method based on AOI is discussed and estimates for residence times
provided based on a study by the Social Plan Bureau (Ref. 68). Table 7.2 shows the mean residence
time fractions for the different building usage categories. In an advice to the Minister, SodM (Ref. 69)
recommended the practice of estimating and reporting LPR should be continued. In this current report,
we will use the conservative LPR metric for risk, but also report the Object-related Individual Risk (OIA).

3

Falling Objects are sometimes also referred to as High Risk Building Elements (HRBE) or Potential High Risk
Building Elements (PRBE).

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Building Use
Woonfunctie
Logiesfunctie
Industriefunctie
Winkelfunctie
Kantoorfunctie
Bijeenkomstfunctie
Overige gebruiksfunctie
Sportfunctie
Onderwijsfunctie
Celfunctie
Gezondheidszorgfunctie
<blank> -- anything unknown is
assumed to be Residential
Table 7.2

Residence Time
Fraction
75%
50%
24%
24%
24%
24%
24%
24%
24%
100%
100%
75%

Residence times for the building usage categories.

Buildings with both a primary and secondary building use (e.g. a building with a shop (commercial
business) at the ground floor and apartments on the first and higher floors (residential)) will be
assigned the larger of the residence times of the primary and the secondary use.

7.1.2 Group Risk and Maatschappelijk Veiligheidsrisico
Aggregated risk metrics (Tab. 7.3) are commonly applied for the evaluation and management of both
technical risks (e.g. chemical plant explosions) and natural risks (e.g. flooding) (Ref. 70). These group
risk (GR) metrics were developed based on the concept that society has lower tolerance to accidents
involving multiple fatalities in a single event than to multiple events involving single fatalities.
The third and final advisory report from Commissie-Meijdam (Eindadvies Handelingsperspectief voor
Groningen Adviescommissie ‘Omgaan met risico’s van geïnduceerde aardbevingen’) (Ref. 30)
introduced the new aggregated risk metric of Maatschappelijk Veiligheidsrisico (MVR).
Maatschappelijk Veiligheidsrisico is an assessment of the frequency (f) with which defined numbers of
fatalities (N) occur due to earthquakes, with an offset for “basic safety” (assuming everyone exposed
to the earthquake risk is at uncorrelated 10-5/year individual risk). The calculation procedure for
Maatschappelijk Veiligheidsrisico is fully described in the final Commissie Meijdam advice, appendix 2
(Ref. 30).
For Winningsplan 2016, for the first time Maatschappelijk Veiligheidsrisico was calculated for seven
selected communities. The definition of the communities was based on the wijk- en buurten definition
by CBS (Ref. 71).

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Risk Metric

Dutch Name

Definition

Purpose(s)

Group Risk

Groepsrisico

Maatschappelijk
Risico

Maatschappelijk
Risico

Frequency (f) with which defined numbers of
fatalities (N) occur due to earthquakes.
Frequency (f) with which defined numbers of
fatalities (N) occur due to earthquakes, with an
offset for “basic safety” (assuming everyone
exposed to the earthquake risk is at
uncorrelated 10-5/year risk). The calculation
procedure for Maatschappelijk Veiligheidsrisico
is fully described in the final Commissie
Meijdam advice, appendix 2 (Ref. 30).

Input towards prioritisation of buildings/non-structural
elements (which don't comply with individual risk norm)
within structural upgrading program.
Provide risk insights for prioritisation of communities.
Consider additional measures (where “reasonable”) beyond
reducing individual risk to below 10-5/year

Table 7.3

Overview of the aggregate risk metrics used in the assessment of risk for induced seismicity in the Groningen field area.

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7.2 Number of buildings compared to the safety norm
In this section of the report, the number of houses where the risk exceeds one of the two risk safety levels
of the Meijdam safety-norm is discussed. Risk for individual building, is not addressed as such is the domain
of the government and its advisor, who currently directs the structural upgrading effort. Both mean LPR
and mean OIA of a building have been calculated and reported (Tab. 7.3a to f). The mean OIA is calculated
as the product of the LPR and the residence time fraction. These fractions are treated as fixed.

7.2.1 Assessment of Local Personal Risk (LPR)
Figures 7.1a and 7.1b show the number of buildings exceeding an annual mean Local Personal Risk (LPR)
for each gas-year of the 10-year period 2020/2021 to 2029/2030, based on the average temperature year
and Operational Strategy 1. Figure 7.1c shows the number of buildings exceeding an annual mean LPR for
gas-year 2020/2021, based on average temperature year for both operational strategies. The grey bands
in these LPR-graphs indicate the uncertainty range. The impact of the buildings already strengthened to
date has not been incorporated in this assessment, as this data is not available to NAM.
The development of the mean LPR for the Groningen building stock over the period gas-year 2020/2021
to 2029/2030 is shown in figure 7.2. The number of buildings exceeding the Meijdam-norm of mean LPR
10-5/year shows a declining trend. This is particularly evident for the gas-years 2020/2021 to 2023/2024,
where the number of buildings exceeding the norm declines noticeably. For each year in the period
2020/2021 to 2029/2030, tables 7.3a to 7.3f, show the number of buildings for different probabilistic
assessments:


The number of buildings with mean LPR exceeding the 10-4/year level



The number of buildings with mean LPR exceeding the 10-5/year level



The number of buildings with mean OIA exceeding the 10-4/year level



The number of buildings with mean OIA exceeding the 10-5/year level



The number of buildings with P90 LPR exceeding the 10-4/year level



The number of buildings with P90 LPR exceeding the 10-5/year level



The number of buildings with P90 OIA exceeding the 10-4/year level



The number of buildings with P90 OIA exceeding the 10-5/year level

For the average and warm gas-years (for both operations strategies) all buildings are assessed to meet the
10-5/year life safety norm. For the cold gas-year 2020/2021 two agricultural barns are assessed to have a
risk above this norm. At the same time the house part of these farmhouses does meet this norm.
During the period of gas-year 2020/2021 to 2022/2023, the number of buildings with a 10% chance that
the 10-5/year norm is not met, is assessed to decrease from 162 in gas-year 2020/2021 to 72 in gas-year
2021/2022 and to 21 in gas-year 2022/2023, for production profile “GTS-raming 2020” for an average
temperature profile and operational strategy 1. For operational strategy 2 the number of buildings with a
10% chance that these are above the norm is lower. In gas-year 2020/2021, this number is 82 and
decreases to 54 in gas-year 2021/2022and 23 in gas-year 2022/2023. All buildings assessed to have a 10%

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chance to be above the 10-5/year Meijdam life safety norm belong to a single typology, i.e. agricultural
barns4.
The data captured in tables 7.3a to 7.3f is illustrated in figure 7.4, which shows number of buildings where
the mean LPR and the P90 LPR exceed the 10-4/year and 10-5/year, for the different cases (temperature
and operational strategy). The LPR results can be disaggregated to show the separate contributions of the
different building typologies (Fig. 7.5).

4

It should be noted that for all but few of these this relates to part of the building; the house part was found to have
P90 LPR < 10-5/year whereas the barn has P90 LPR >10-5/year.

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Figure 7.1a

2020/2021

2021/2022

2022/2023

2023/2024

2024/2025

2025/2026

Local Personal Risk graphs for the gas-years 2020/2021 to 2025/2026. These show the number of houses that are exposed to a LPR. The thick black lines denote
the mean and the dark grey areas the uncertainty bands. The two horizontal bands in light grey denote the LPR levels of the Meijdam-norm.

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2026/2027

2027/2028

2028/2029

2029/2030
Figure 7.1b

Local Personal Risk graphs for the gas-years 2026/2027 to 2029/2030. These show the number of houses exceeding a certain LPR. The thick black lines denote the
mean and the dark grey areas the uncertainty bands. The two horizontal bands in light grey denote the LPR levels of the Meijdam-norm. The assessment is based
on production profile “GTS-raming 2019” for an average temperature year and Operational Strategy 1.

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Figure 7.1c

Local Personal Risk graphs for the gas-year 2020/2021 for an average temperature year shown for both operational strategies. These show the number of houses
exceeding a certain LPR. The thick black lines denote the mean and the dark grey areas the uncertainty bands. The two horizontal bands in light grey denote the
LPR levels of the Meijdam-norm.

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Figure 7.2

Mean Local Personal Risk graphs for the gas-years 2020/2021 to 2029/2030. These show the number of buildings
exceeding a certain LPR. The uncertainty bands have been left out of this graph but are shown in figures 7.1a and
7.1b. The years are colour coded. The assessment is based on production profile “GTS-raming 2020” for an average
temperature year and Operational Strategy 1.
Note that there are zero buildings exceeding the mean LPR norm of 10-5/year.

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Figure 7.3

Comparison of the mean Local Personal Risk graphs of previous HRA reports. The following is shown:
Production profile “Basispad Kabinet” for Average Temperature and Calendar-year 2019,
Production profile “GTS-raming 2019” for Average Temperature and gas-year 2019/2020
Production profile “GTS-raming 2020” for Operational Strategy 1, Average Temperature and gas-year 2020/2021.

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Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

162
72
21
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

23
15
0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3a

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and average temperature
year. See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020”,
operational strategy 1 for an average weather year.

Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

2
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

245
69
35
16
0
0
0
0
0
0

0

75

0

2

0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3b

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and cold temperature year.
See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020” operational
strategy 1 for a cold weather year.

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Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

87
54
23
0
3
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

9
2
0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3c

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and warm temperature year.
See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020” operational
strategy 1 for a warm weather year.

Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

82
47
10
0
5
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

4
0
0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3d

0
0
0
0
0
0
0
0
0

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and average temperature
year. See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020”
operational strategy 2 for an average weather year.

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Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

1
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

220
72
27
30
0
3
0
0
0
0

0
0
0
0
0
0
0
0
0
0

76
6
0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3e

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and cold temperature year.
See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020” operational
strategy 2 for a cold weather year.

Gas-year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020/2021

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

62
62
15
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
3
0
0
0
0
0
0
0
0

2021/2022
2022/2023
2023/2024
2024/2025
2025/2026
2026/2027
2027/2028
2028/2029
2029/2030
Table 7.3f

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and warm temperature year.
See main text for further explanation. These are shown for each year of the period 2020/2021 to 2029/2030 for the production profile “GTS-raming 2020” operational
strategy 2 for a warm weather year.

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120

Figure 7.4

Graphs show the Local Personal Risk associated with the production profile “GTS-raming 2020” for average, cold
weather and warm weather gas-years for the period 2020/2021 to 2029/2030.
Right graphs:
number of buildings exceeding the norm LPR larger than 10-5/year
Left graphs:
number of buildings exceeding the norm LPR larger than 10-4/year
Top graphs:
number of buildings exceeding the norm for mean LPR
Bottom graphs:
number of buildings exceeding the norm for P90 LPR

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Operational Strategy 1

Operational Strategy 2

Figure 7.5

Mean Local Personal Risk graphs shown per primary typology for gas-year 2020-2021 with average temperature
demand.

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7.2.2 Number of years P90 LPR > 10-5/year
Figure 7.6 gives, for buildings with P90 LPR > 10-5/year in gas-year 2020/2021, an overview of the duration
that these buildings remain above P90. For about half of them this duration is 1 year. All buildings will drop
below P90 within 3 to 4 years. It should be noticed that the barn of the farmhouse (URM1F_B) is most
fragile and shown here. The house parts of these farmhouses all remain below P90.

Figure 7.6

Overview of the duration that these buildings remain above the norm for buildings above the norm in 2019, shown
for Operational Strategy 1 above and Operational Strategy 2 below.

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7.3 Maps of Buildings compared to the Meijdam-norm Risk Levels
The maps of figure 7.7 show all buildings exceeding mean LPR>10-5/year in gas-year 2020/2021 for both
operational strategies. As these maps show no buildings above this norm, figure 7.7 shows the buildings
that have a 10% chance of exceeding mean LPR>10-5/year for the gas-year 2020/2021 to 2024/2025. The
maps in figures 7.7 and 7.8 are shown for the production profile “GTS-raming 2020” with the average
temperature profile for both Operational Strategies.
For the purpose of this risk assessment, the Groningen building stock has not been adjusted for the
ongoing structural upgrading operations, as the list of strengthened buildings is not available to NAM.

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2020/2021
Figure 7.7

Map of all buildings exceeding mean LPR>10-5/year for the gas-year 2020/2021, for the production profile
“GTS-raming 2020” for an average temperature. The top maps show Operational Strategy 1, and the bottom
maps show Operational Strategy 2.

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2020/2021, P90

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2021/2022, P90

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2022/2023, P90

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2023/2024, P90

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2024/2025, P90
Figure 7.8

Map of all buildings exceeding mean LPR>10-5/year for the gas-year 2020/2021, and maps of all buildings
exceeding P90 LPR>10-5/year for the gas-years 2020/2021 to 2024/2025, shown for the production profile
“GTS-raming 2020” for an average temperature. The top maps show Operational Strategy 1, and the bottom
maps show Operational Strategy 2.

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2020/2021
Figure 7.9

2021/2022

Map indicating individual building with Local Personal Risk exceeding 10-5/year for the gas-years 2020/2021 to
2022/2023 and production profile “GTS-raming 2020” (average temperature).

Figure 7.10 shows a reconciliation of the buildings with a P90 LPR above 10-5/year for the average
temperature profile in gas-year 2020/2021, 2021/2022 and 2022/2023 between the two operational
strategies. Moving from operational strategy 1 to operational strategy 2 adds 1 building near Loppersum
(middle panel in figure 7.10a) and removes 81 buildings near Loppersum (right-hand panel of figure 7.9a)
for gas-year 2020/2021. For gas-year 2021/2022, operational strategy 1 adds 25 buildings above the norm
compared to operational strategy 2 (figure 7.9b).

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O S1

Figure 7.10a

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10-5/year in both operational strategies for the average
temperature profile for gas-year 2020/2021. Base is operational strategy 1 and the target is operational strategy
2. The middle figure shows the buildings additionally with a P90 LPR above 10-5/year for operational strategy 2,
while the right figure shows the buildings additionally with a P90 LPR above 10-5/year for operational strategy 1.
This means that the full set of buildings in operational strategy 2 are the buildings indicated in the left and middle
figure together. The buildings associated with operational strategy 1 are the buildings indicated in the left and
right figure together.

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O S1

Figure 7.10b

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10-5/year in both operational strategies for the average
temperature profile for gas-year 2021/2022. The left figure shows the buildings with a P90 LPR above 10-5/year in
operational strategy 2. The right figure shows the buildings additionally with a P90 LPR above 10 -5/year for
operational strategy 1. The buildings with a P90 LPR above 10-5/year in operational strategy 1 are the buildings
indicated in the left and right figure together.

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O S1

Figure 7.10c

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10 -5/year in both operational strategies for the average
temperature profile for gas-year 2022/2023. Base is operational strategy 1 and the target is operational strategy
2. The middle figure shows the buildings additionally with a P90 LPR above 10 -5/year for operational strategy 2,
while the right figure shows the buildings additionally with a P90 LPR above 10-5/year for operational strategy 1.
This means that the full set of buildings in operational strategy 2 are the buildings indicated in the left and middle
figure together. The buildings associated with operational strategy 1 are the buildings indicated in the left and
right figure together.

Figure 7.11 shows the same for the cold temperature production profile for gas-years 2020/2021 to
2022/2023. Moving from operational strategy 1 to operational strategy 2 for gas-year 2020/2021 adds 20
in the area of Loppersum and towards Hoogezand (middle panel in the figure 7.11a) and removes 45
buildings in the area of Loppersum (right-hand panel of the same figure).

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O S1

Figure 7.11a

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10-5/year in both operational strategies for the cold
temperature profile for gas-year 2020/2021. Base is operational strategy 1 and the target is operational strategy
2. The middle figure shows the buildings additionally with a P90 LPR above 10-5/year for operational strategy 2,
while the right figure shows the buildings additionally with a P90 LPR above 10-5/year for operational strategy 1.
This means that the buildings with a P90 LPR above 10-5/year in operational strategy 2 are the buildings indicated
in the left and middle figure together. The buildings above the norm in operational strategy 1 are the buildings
indicated in the left and right figure together.

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O S1

Figure 7.11b

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10-5/year in both operational strategies for the cold
temperature profile for gas-year 2021/2022. Base is operational strategy 1 and the target is operational strategy
2. The middle figure shows the buildings additionally with a P90 LPR above 10 -5/year for operational strategy 2,
while the right figure shows the buildings additionally with a P90 LPR above 10 -5/year for operational strategy 1.
This means that the buildings with a P90 LPR above 10-5/year in operational strategy 2 are the buildings indicated
in the left and middle figure together. The buildings above the norm in operational strategy 1 are the buildings
indicated in the left and right figure together.

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O S1

Figure 7.11c

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

O S1

O S1
and
O S2

O S2

The left figure shows the buildings with a P90 LPR above 10-5/year in both operational strategies for the cold
temperature profile for gas-year 2022/2023. Base is operational strategy 1 and the target is operational strategy
2. The middle figure shows the buildings additionally with a P90 LPR above 10 -5/year for operational strategy 2,
while the right figure shows the buildings additionally with a P90 LPR above 10 -5/year for operational strategy 1.
This means that the buildings with a P90 LPR above 10-5/year in operational strategy 2 are the buildings indicated
in the left and middle figure together. The buildings above the norm in operational strategy 1 are the buildings
indicated in the left and right figure together.

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7.4 Insights into the development of the buildings above the norm
7.4.1 Introduction
The Hazard and Risk Assessment also provides a prioritised list of buildings that are not expected to meet
the norm and of buildings which are expected to meet this norm. In the HRA report for GTS-raming 2019
(Ref. 19) we have shown that the continued decline of production, the improvements to the HRA-model,
the targeted improvements to the exposure database (describing the building stock in Groningen) and the
improvements of the fragility curves (describing the response of those buildings to ground motion) have
had two main effects.
1. Firstly, the overall assessment of risk has decreased and consequently also the assessment of the
number of buildings not meeting the norm has decreased.
2. Secondly, the assessment has increased the focus on specific typologies that are most deserving
of attention in the efforts to further reduce risk.
As a result, the typology representing typical farmhouses with barns dominated the stock of buildings not
meeting the norm, in addition to a few terraced houses with large opening percentages in the ground floor
façade walls.

7.4.2 Comparing the results of risk assessments
This section describes the evolution of the number of buildings in a risk classification between this
assessment and the previous one (Ref. 19). As the basis for this comparison “GTS-raming 2019” gas-year
2019/2020 with average temperature demand and Operational Strategy 1 (i.e. the strategy selected by
the Minister of Economic Affairs and Climate Policy) is presented. For the comparison we will make use of
Sankey diagrams. These are a specific type of flow diagram, in which the width of the arrows is
proportional to the number of buildings.
Figure 7.12 shows a Sankey diagram summarising the overall changes (occupied buildings) per risk
classification group defined by the “Mijnraad”:




Buildings with meanLPR > 10-5/year – “Mijnraad 1500” Group,
buildings with more than 10% probability of having LPR > 10-5/year, but not part of the first bullet the “P90 Group”, and
buildings not part of above risk priority groups – in the figure labelled P10 LPR < 10-5/year.

On the left the numbers of buildings in these three groups are represented as they were in the assessment
of February 2019 and on the right are the numbers in the current assessment. Also buildings that were
previously not in the EDB (V6) but are now, are shown (new in EDB V7). The connections between these
illustrate the movement of the buildings in these categories between these two risk assessments.
The number of buildings above the norm decreased from 435 to 0, and the “P90 Group” reduced from
3,271 to 82 Buildings.

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Figure 7.12

Sankey diagram summarising the changes (occupied buildings) from “GTS-raming 2019” gas-year 2019/2020
Operational Strategy 1 to “GTS raming 2020” gas-year 2020/2021 Operational Strategy 2 with average
temperature demand.

Anticipating that most attention will be drawn to buildings with highest risk profile, the following
paragraphs focus on buildings with mean LPR>10-5/year – i.e. not meeting the Meijdam-norm.

7.4.3 Update to buildings above the norm
The total number of buildings not meeting the 10-5/year norm has now further declined from 435 for GTSraming 2019 (OS1 with average temperature demand gas-year 2019/2020) to 0 for GTS-raming 2020 (both
operational strategies with average temperature demand). That reduction is caused by the following
developments:



The hazard has decreased because of further reduced production.
The HRA-model has been updated with the latest scientific insights.
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



The modelled response of buildings to ground motion as captured in the so called ‘fragility curves’
has changed. Fragility and consequence models were updated for several relatively more vulnerable
building typologies, including farmhouses with barns.
Improvements in Groningen building stock knowledge resulted in updates to the Exposure database
EDB V7.

Of the 435 buildings that were previously assessed as not meeting the norm in gas-year 2019/2020:





0 buildings are still expected not to meet the Meijdam-norm (mean LPR> 10-5/year).
4 buildings are no longer present in the EDB. This means that they are no longer registered with the
Kadaster as occupied building (for example because they have been demolished in the meantime).
The remaining 431 buildings are now all expected to meet this norm. Of those, 34 still have a small
probability of not meeting the norm and are now part of the so-called ‘P90 group’.

The following diagram illustrates the above:

Figure 7.13

Sankey diagram showing developments between ‘GTS-raming 2019” Operational Strategy 1 and “GTS-raming
2020” Operational Strategy 2 (average temperature demand) assessment for the buildings not meeting the
10-5/year norm.

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Figure 7.14

Sankey diagram showing developments between ‘GTS-raming 2019” Operational Strategy 1 and “GTS-raming
2020” Operational Strategy 2 (average temperature demand) assessment for the buildings now not meeting the
P90 LPR 10-5/year criterion.

The assessment of whether individual houses are expected to meet or not meet the Meijdam-norm is one
of the inputs into the risk management policy set by the Minister (Ref. 28 to Ref. 30). How this information
is used to derive inspection and/or structural upgrading programmes is not described in this report and is
outside the remit of NAM.

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7.5 Structural upgrading program
The probabilistic assessment of the number of buildings that do not meet the Meijdam-norm does not
immediately translate into an estimate of the structural structural upgrading scope. There are three main
reasons why the scope of the structural upgrading plan will in general be larger than the probabilistic
assessment of the number of buildings that do not meet the Meijdam norm. The involvement of the
Minister of EZK over the past years with the structural upgrading has been formally implemented in the
Mining law in December 2019.






Efficiency of identifying buildings with mean LPR >10-5/year (Meijdam-norm) has not yet been proven.
The Hazard and Risk Assessment is a probabilistic assessment and does not directly identify each
individual building that needs to be included in the structural upgrading plan. It identifies which
buildings have the highest risk of (partial) failure based on the building features in the Exposure
Database. If a risk-based approach is to be followed, verification of the building features as used in
the Hazard and Risk Assessment (by inspection) would be required. This means that inspection results
will either confirm the building features or otherwise, which will have implications for prioritisation
(for any subsequent engineering and structural upgrading). A risk-based inspection program will be
able to identify which building may need structural upgrading with reasonable efficiency and help
prioritising the effort.
Remaining uncertainty in Hazard and Risk Assessment.
Significant progress has been made towards assessing the risk from Groningen earthquakes.
However, uncertainty remains in the estimate of the number of buildings that do not meet the norm
based on mean LPR > 10-5/year. Further studies, experiments, modelling and building inspections can
help reduce this uncertainty.
The regulator attached therefore special relevance to the P90 estimate for risk for a building. This
serves as an additional conservatism in the risk assessment.
Differences between the Hazard and Risk Assessment and NEN-NPR building code.
Ultimately the structural upgrading scope will be based on the NEN-NPR building code. Improvement
of the Hazard and Risk Assessment and calibration of the building code with the latest technical insight
from laboratory experiments and modelling in the NEN-NPR are likely to reduce the difference
between the HRA assessment and the results of assessing the safety of individual buildings using the
NPR building code. In the advice to the Minister of 11 February 2019 (Ref. 72), 26 June 2019 (Ref. 73)
and 10 October 2019 (Ref. 74), SodM stressed the importance of using the latest scientific insights in
the NEN-NPR. In the advice of 19 December 2019 (Ref. 75), SodM notes: “With regard to the NPR
9998, it appears that there is currently no prospect of introducing a new, improved version. It is
therefore unclear how long it will take before the latest scientific insights are incorporated. This while
there are clear signals that clear improvements are possible”.

The probabilistic estimate of the number of buildings, where the Meijdam-norm safety level is exceeded,
does therefore not directly translate into an estimate of the structural upgrading scope. However, the
Hazard and Risk Assessment provides a useful tool for prioritisation of building inspections. Ultimately the
structural upgrading scope will be based on the assessment of individual buildings based on the NEN-NPR
building code.

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7.6 Disaggregation of Local Personal Risk (LPR)
We analyse the contribution of aleatory variabilities to the mean logic tree Local Personal Risk outcomes
for each surface location and structural system by applying the same method of disaggregation as
previously described for seismic hazard. In this case the selected aleatory variables are magnitude, M,
distance, Rrup, and the average pseudo spectral acceleration, AvgPSA, as defined by the building fragility
model.
Figure 7.15 shows risk disaggregation results for the Loppersum location and three structural systems
typically associated with the highest LPR values. In all three cases, the marginal contribution of magnitudes
to mean LPR is bi-modal with the primary mode at M = 4.7 and the secondary mode at M = 4.4. We
interpret this to indicate the influence of the Mmax = 4.5 logic tree branch that creates a discontinuity in
the distribution as previously seen within the hazard disaggregation results. Indeed, all Mmax values within
the logic tree (4.0, 4.5, 5.0, 5.5, 6.0. 6.5) appear as inflection points in all these distributions of magnitude
contributions to LPR. The distributions of distance contributions to risk show much less complexity with a
clear mode at Rrup = 3 km and a strong clear decline with increasing distance, all as previously seen in the
hazard disaggregation results. The distributions of average PSA risk contributions all show a well-defined
mode at about 0.5 g, but with significant contributions extending above 1.0 g. A small degree of jitter is
visible in these distributions, which may reflect some residual stochastic variability associated with finite
sample effects despite simulating 4.5 x 106 earthquake catalogues for each of the 1008 logic tree branches.
For the Groningen city location (Figure 7.16), we see the modal magnitude contribution to LPR increase to
M = 5.7 for all three structural systems. These marginal distributions exhibit strong inflections points
aligned with the choice of every discrete Mmax value placed on the logic tree. Despite the presence of these
strong secondary maxima, the primary modal value remains clear. Modal distances increase to Rrup = 5 km,
as also previously observed in the hazard disaggregation results. The secondary mode seen in the hazard
disaggregation at Rrup = 17 km is also discernible in these risk disaggregation results although it is much
less-well sampled and may be an artefact of residual stochastic variabilities. The average PSA risk
contributions show a clear mode at AvgPSA = 0.45 g for the URM1F_B structural system, whilst the other
two structural systems show almost equal risk contributions over the interval 0.4 g < AvgPSA < 0.7 g. The
residual stochastic jitter in these distributions means the mode cannot be reliably measured.
Figure 7.17 summarizes the variation of the largest risk contributions associated with magnitude, distance
and average PSA for same three structural systems shown in Figure 7.15 and Figure 7.16. For all three
structural systems, the areal distribution of modal magnitudes and distances are quite similar with
magnitudes in the range 4.0 < M < 5.0 making the largest contribution to mean LPR in the central parts of
the analysis area. Outside this central region, the magnitudes that make the largest contributions to risk
increase to 5.5 < M < 6.5. As seen before in the hazard disaggregation results, most of the seismic risk is
associated with local underlying seismicity in the central region and only on the edges of the analysis area
does this increase up to 15 km. We observe little spatial organization in the modal values of average PSA
but these do exhibit stronger variability between structural systems than seen for magnitudes and
distances.

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(a)

(b)

(c)
Figure 7.15

The fractional contributions to the mean Local Personal Risk (LPR) at the Loppersum location for three structural
systems (a) URM1F_B, (b) URM3L and (c) URM4L given gas-year 2020/2021 and the base case production scenario
(GTS-raming 2020 for the average temperature weather and Operational Strategy 1).

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(a)

(b)

(c)
Figure 7.16

As Figure 7.15, except for the Groningen city location.

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(a)

(b)

(c)
Figure 7.17

Maps of the magnitude, M, distance, Rrup, and the average PSA for building fragility, AvgPSA, that contribute most
to the mean Local Personal Risk metric over gas-year 2020/2021 under the base case production scenario (GTSraming 2020 for the average temperature weather and Operational Strategy 1). Results are shown for three
structural systems: (a) URM1F_B, (b) URM3L, and (c) URM4L, as previously shown in Figure 7.15.

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7.6.1 Sensitivity to epistemic uncertainties
We quantify epistemic uncertainty within our Probabilistic Seismic Hazard and Risk Analysis using the full
factorial logic tree shown in Figure 7.18. This logic tree includes six key factors:
1.
2.
3.
4.
5.
6.

the earthquake magnitude-frequency model,
the maximum possible earthquake magnitude model,
the ground motion median- model,
the ground motion -model,
the building fragility model, and
the consequence model.

Each factor contains from 2 to 7 levels designed to characterise the distribution of mutually-independent
alternative models that collectively span the range of epistemic uncertainties. To better understand the
relative contributions of each logic tree factor to the risk prediction intervals we disaggregate the logic
tree to obtain their marginal distribution for a given summary risk metric. For instance, the upper level of
contributions from the magnitude-frequency model is measured as the weighted mean of all logic tree
branches that include the upper level of the magnitude-frequency model re-weighted by the relative
weights of these selected branches. The lower level of contributions from the magnitude-frequency model
is likewise computed from all logic-tree branches that include the lower level of this factor. Repeating this
procedure for all factors and levels yields a tornado plot of ranked sensitivities to epistemic uncertainties
for a given risk metric. Summary risk metrics utilized for this purpose include the mean LPR for all exposed
people, and the number of buildings with a mean LPR exceeding a given threshold, such as 10-5/year. Figure
7.19a shows results for the mean LPR metric indicating the epistemic uncertainties recognized in the
maximum possible earthquake magnitude, Mmax, are the most influential, followed by the nature of
stress dependence in the frequency-magnitude model, and then the fragility model. The ranking of all 6
risk factors remains essentially unchanged for 3 other alternative risk metrics (Figure 7.19b, Figure 7.20).
This indicates the most effective means of reducing the influence of epistemic uncertainties on the risk
analysis is for any further data acquisition and studies to target the top-ranking risk factors shown in Figure
7.19 and Figure 7.20.

Figure 7.18

The structure and weights of the full-factorial logic tree for Probabilistic Seismic Hazard Analysis (factors 1-4) and
Probabilistic Seismic Risk Analysis (factors 1-6).

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(a)

(b)
Figure 7.19

Sensitivity of seismic risk to epistemic uncertainties as measured by (a) the mean local personal risk (LPR) per
exposed individual, and (b) the total number of buildings with a mean LPR exceeding 10 -5/year. Grey bars denote
risk intervals spanned by consecutive levels of a risk factor. The vertical black line denotes the mean of the entire
logic tree. Data is shown for gas-year 2020/2021 under the base case production scenario (GTS-raming 2020 for
the average temperature weather and Operational Strategy 1).

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(a)

(b)
Figure 7.20

Sensitivity of seismic risk to epistemic uncertainties as measured by (a) the mean P90 local personal risk (LPR) per
exposed individual, and (b) the total number of buildings with a P90 LPR exceeding 10 -5/year. Data is shown for
gas-year 2020/2021 under the base case production scenario (GTS-raming 2020 for the average temperature
weather and Operational Strategy 1).

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7.7 Group Risk and Maatschappelijk veiligheidsrisico
The Maatschappelijk Veiligheidsrisico has been calculated for seven communities and for the area of the
Groningen field (Fig. 7.21).

Buurten Bedum, Loppersum, Middelstum & Ten Boer (PDOK CBS buurten 2015)
Wijken Appingedam & Delfzijl (PDOK CBS wijken 2015)
Gemeente Groningen (PDOK CBS gemeenten 2015)

-> Appingedam & Delfzijl bestaan uit meerdere buurten; vandaar gekozen voor
wijken (eventueel Appingedam inzoomen op groene 3 wijken)
-> Groningen bestaat uit meerdere wijken; vandaar gekozen voor de
gemeente/stad

Figure 7.21

Extract from the advice by Ministry of Economic Affairs detailing the seven communities selected for calculation of
Maatschappelijk Veiligheidsrisico.

This selection was based on the CBS buurtenkaart (Ref. 71). Table 7.5 shows that the seven selected
communities are composed of several neighbourhoods in the CBS buurtenkaart.

Table 7.5

The seven identified communities consist of one of more neighbourhoods as defines in wijk- en buurten register by
CBS. Community “gemeente Groningen” consists of 10 neighbourhoods.

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Reliable assessment of the contribution of the collapse of groups of buildings to Maatschappelijk
Veiligheidsrisico requires detailed representation of the spatial correlations in the earthquake ground
motions. In the current assessment, spatial correlation is represented as strong within each near-surface
amplification zone up to a distance of 3 to 5 km with no correlation beyond this distance.
The Maatschappelijk Veiligheidsrisico assessments for all seven communities are shown in figure 7.22.

Figure 7.22

Maatschappelijk Veiligheidsrisico based on the mean of the logic tree, for the seven communities (in alphabetical
order) for Operational Strategy 1, gas-year 2020/2021, average temperature production profile. Numbers after
each community name denote the average day-night inside total population for that community.

Figure 7.23

Maatschappelijk Veiligheidsrisico based on the mean of the logic tree, for the Groningen field area for Operational
Strategy 1, gas-year 2020/2021, average temperature production profile.

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For smaller number of fatalities (N) the Maatschappelijk Veiligheidsrisico is negative. The curves in fig.
7.22 show for the seven selected communities, that for N larger than about ten, the Maatschappelijk
Veiligheidsrisico is about equivalent to Group Risk. The population (mean inside day-night population) is
shown for each community. The Maatschappelijk Veiligheidsrisico clearly depends on population size. The
Maatschappelijk Veiligheidsrisico for the Groningen Field area as a whole is presented in fig. 7.23.

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8 Damage Assessment
8.1 Classification of Building Damage; Building Damage States
8.1.1 European Seismological Commission, EMS-1998
The EMS-98, European Seismological Commission, 1998 (Ref. 76) document provides guidelines for
estimation of the intensity of an earthquake based on the damage assessment of buildings.
Damage of buildings is assessed on the basis of a damage classification. This is provided for two main
categories: unreinforced masonry buildings (URM) and reinforced concrete (RC) buildings. Figure 8.2
describes the 5 distinguished damage grades for both main categories. The description of the damage
states in this figure are purely qualitative. For instance, “negligible to slight damage” is termed DS1,
“moderate damage” DS2, “substantial to heavy damage" DS3”. The EMS scale relates DS1 to “hairline
cracks in very few walls”, DS2 to “cracks in many walls” and DS3 to “large and extensive cracks in most
walls”. The qualitative descriptions of the building damage states form a very useful, practical and
generally accepted and applied classification system for building damage.

Figure 8.1

Cover of the “European Macroseismic Scale 1998, EMS-98”by the European Seismological Commission (G.
Grünthal), 1998.

154

Figure 8.2

Classification of damage to masonry buildings (left) and classification of damage to reinforced concrete buildings (right). Illustration taken from EMS-98, European
Seismological Commission, 1998 (Ref. 76).

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8.2 Forecast for Damage State 1 (DS1) aesthetic damage
The report “Methodology Prognosis of Building Damage and Study and Data Acquisition Plan for
Building Damage” (Ref. 77), issued February 2017, describes the studies program into building damage
and the methodology for forecasting building damage. The building damage assessment of November
2017 (Ref. 15) contains an introduction into the classification of damage states and into the Monte
Carlo method used for forecasting building damage and fatality risk.
This section presents the forecast of building damage level DS1 resulting from production profile “GTSraming 2020” and is similar to the forecast provided last year for “GTS-raming 2019”. The higher
damage states DS4 and DS5 are relevant for risk and have been addressed in the previous sections of
this report. For the assessment of DS1 building damage, empirical methods based on analysis of
historical damage data are used.
The approach to forecast DS1 based on observed damage from historical earthquakes is described in
section 8 of the report “Induced Seismicity in Groningen, Assessment of Hazard, Building Damage and
Risk – November 2017” (Ref. 15, pages 168-173). An update of that work has been prepared In June
2018 (Ref. 17, pages 9-11).
This section describes a further update and incorporates the latest information and knowledge
available in the following areas:





Updated empirical GMPE, following recalibration of KNMI G0 accelerometer stations (Ref. 78).
Recalibrated DS1 fragility curves based on the updated empirical GMPE.
Production from the Groningen field “GTS-raming 2020”
Exposure database V7 (EDB)

8.3 Earthquake catalogue of events
For the forecast, a range of possible future realizations is needed that adequately represent the
anticipated earthquake distribution, both in terms of magnitude and location in the field. These have
been generated stochastically, using the hazard tool for the Operational Strategy 1 and Operational
Strategy 2 based on the average temperature demand profile. These are the same profiles as used for
the full Hazard and Risk Assessment. In the Monte Carlo simulation process, repeated random
sampling of a set of input distributions is used to create a probabilistic distribution output. So-called
‘synthetic earthquake catalogues’ (i.e. event locations and magnitudes for the period gas-year
2020/2021 to 2029/2030) are generated from the input probability distributions of total seismic
moment, number of events and event epicentres. This forecast uses events between ML = 1.8 and 4.0.

8.4 Exposure model
The exposure database (EDB V7 Ref. 79) is an extract of a project database and consists mainly of the
building typology classifications and several other building related attributes, including the population,
arranged per building. In addition to its use as input into the Hazard and Risk Modelling, the EDB
deliverable also provides the necessary information to assign the TNO typologies to all 263,399
Buildings (“Basisregistratie adressen en gebouwen (BAG)” from the Kadaster) in the area considered
for damage forecast. The area of interest is the same for the Hazard and Risk Assessment and is based
on the Groningen gas field outline. The extract boundary for the EDB V7 is a 5 km buffer around the
gas field outline.

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Table 8.1 shows how the different type of buildings present in the Groningen building stock have been
assigned to the typologies used by TNO.

Table 8.1

Assignment of EDB V7 typologies to the typologies used in the TNO Kalibratiestudie.

Although it has been recognized that secondary buildings representing ca. 40% of all buildings mainly
consisting of sheds, garages and other small normally unoccupied buildings could also incur damage,
they have been excluded from the forecast because damage data/reports are unavailable for such
structures. A previous sensitivity analysis, with assumed fragility function like Low Rise buildings after
1940, shows that secondary buildings may perhaps add up to ca. 60% additional damage cases.
Due to the absence of damage observations in the earlier TNO studies, fragility function for Low Rise
buildings after 1940 have also been assigned to all High-Rise buildings. This is believed to be a
conservative assumption.

8.5 Results
Figure 8.3 shows results of the DS1 damage forecast in the form of an annual F/N curve for the
Groningen field area, one per gas-year, shown for the period 2020-2030. The median forecast (P50 or
50%) is indicated together with the 80% confidence interval (10% to 90%). Each building in the
exposure area was assigned with a relevant typology. It was assumed that any resulting building
damage is repaired after the event and before the next one (instant repair). The figure shows that in
gas-year 2020-2021 a forty percent chance that more than 100 buildings will be damaged with
aesthetic damage (DS1) (due to all earthquakes in that year smaller than ML=4). In gas-year 2028-2029
there is a fifty percent chance that more than 10 buildings will be damaged with aesthetic damage.
Figure 8.4 shows the Mean and P50 for the DS1 damage forecast per gas-year for the period 20202030. Due to the skewed distribution of building damage the mean number of damaged buildings is
considerably higher than the P50. The DS1 damage forecasts for both operational strategies are very
similar, with a maximum difference of a few damaged buildings per year.

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Operational Strategy 1

Operational Strategy 2

Figure 8.3

DS1 Forecast per gas-year for period 2020-2030 based on the middle branch of the Mmax distribution, shown
for “GTS-raming 2020” Operational Strategy 1 and 2 average temperature demand profile.

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Operational Strategy 1 and 2 Compared

Figure 8.4

Mean and P50 DS1 Forecast per gas-year for period 2020-2030 based on the middle branch of the Mmax
distribution, shown for “GTS-raming 2020” Operational Strategy 1 and 2 average temperature demand profile.

8.6 Forecast for Damage State 2 (DS2) and Damage State 3 (DS3)
Fragility functions for DS2 and DS3 have been developed for each structural system identified in the
exposure model using the extensive analytical modelling and experimental test campaign described in
(Ref. 44). F/N curves have been calculated with the Monte Carlo risk engine which show the annual
frequency of exceedance (F) of different numbers of groups of buildings (N) which simultaneously
reach DS2 or DS3. Figure 8.5 shows the F/N curve for the whole field for each of the gas-years in the
period 2020/2021 to 2029/2030. Figure 8.5 shows that in 2020, the annual frequency of exceedance
of having anywhere 100 buildings simultaneously damaged to DS2 in a given earthquake is around 1%
and the chance that 10 buildings are simultaneously damaged to DS3 in a given earthquake is a bit
lower.
Figure 8.7 shows the exceedance damage count for the occurrence of the given damage state (DS).
For instance, in 2019, the chance of 10 or more buildings reaching a DS2 damage state is about 11%.
The chance that 100 buildings or more reach damage state DS3 is less than 2%. In Figure 8.8 DS2 and
DS3 damage is compared for the two operational strategies. Differences are less than 1%.

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Figure 8.5

2020/2021

2021/2022

2022/2023

2023/2024

2024/2025

2025/2026

2026/2027

2027/2028

2028/2029

2029/2030

Maatschappelijk risico for building damage DS2 and DS3 (MR(S)) for the whole field for the gas-years 2020/2021 to 2029/2030. The production is based on an average temperature
year for Operational Strategy 1.

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Average Temperature Production Profile

Cold Temperature Production Profile

Figure 8.7a

Maatschappelijk risico for building damage DS2 and DS3 (MR(S)) for the whole field for the gas-years
2020/2021 to the gas-years 2029/2030 for operational strategy 1. Top figure is for an average temperature
production profile and the bottom figure for a cold temperature production profile.

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for the Groningen field - March 2020

Warm Temperature Production Profile

Figure 8.7b

Maatschappelijk risico for building damage DS2 and DS3 (MR(S)) for the whole field for the gas-years
2020/2021 to the gas-years 2029/2030 for operational strategy 1 for a warm temperature production profile

Figure 8.8

Maatschappelijk risico for building damage DS2 and DS3 (MR(S)) for the whole field for the gas-years
2020/2021 to the gas-years 2029/2030. The results for both operational strategy 1 and 2 are shown.

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9 References
All reports referenced in this section prepared by NAM can be downloaded from the webpage
“onderzoeksrapporten” on www.nam.nl.
Ref. 1

Het Winningsplan Groningen 2013, NAM, Dec 2013.

Ref. 2

Technical Addendum to the Winningsplan Groningen 2013 - Subsidence, Induced Earthquakes and Seismic
Hazard Analysis in the Groningen Field and Supplementary Information, Jan van Elk, Dirk Doornhof, Stephen
Bourne, Steve Oates, Julian Bommer, Clemens Visser, Rob van Eijs and Peter van den Bogert, NAM, Shell and
Imperial College, Dec 2013.

Ref. 3

Hazard Assessment for the Eemskanaal area of the Groningen field, Jan van Elk and Dirk Doornhof, NAM, Nov
2014.

Ref. 4

Addendum to: Hazard Assessment for the Eemskanaal area of the Groningen field, Jan van Elk and Dirk
Doornhof, NAM, Nov 2014.

Ref. 5

Dreigings- en risicoanalyse voor geïnduceerde seismiciteit Groningen - Onderzoek 1 dreigingsanalyse, Jan van
Elk and Dirk Doornhof, NAM, May 2015.

Ref. 6

Dreigings- en risicoanalyse voor geïnduceerde seismiciteit Groningen - Onderzoek 2 risicoanalyse, Jan van Elk
and Dirk Doornhof, NAM, May 2015.

Ref. 7

Hazard and Risk Assessment for Induced Seismicity in Groningen, Jan van Elk and Dirk Doornhof, NAM, Nov
2015.

Ref. 8

Winningsplan Groningen gasveld - 2016, NAM, April 2016. EP201604259068

Ref. 9

Technical Addendum to the Winningsplan Groningen 2016 (chapter 1-5), Jan van Elk and Dirk Doornhof, NAM,
Apr 2016.

Ref. 10

Technical Addendum to the Winningsplan Groningen 2016 (chapter 6), Jan van Elk and Dirk Doornhof, NAM,
Apr 2016.

Ref. 11

Technical Addendum to the Winningsplan Groningen 2016 (chapter 7), Jan van Elk and Dirk Doornhof, NAM,
Apr 2016.

Ref. 12

Technical Addendum to the Winningsplan Groningen 2016 (chapter 8), Jan van Elk and Dirk Doornhof, NAM,
Apr 2016.

Ref. 13

Technical Addendum to the Winningsplan Groningen 2016 (chapter 9), Jan van Elk and Dirk Doornhof, NAM,
Apr 2016.

Ref. 14

Supplement to the Winningsplan Groningen 2016, Jan van Elk and Dirk Doornhof, NAM, Apr 2016.

Ref. 15

Assessment of Hazard Building Damage and Risk for Induced Seismicity in Groningen - 2017, Jan van Elk, Dirk
Doornhof, NAM, Nov 2017.

Ref. 16

Seismic risk assessment for a selection of seismic risk production scenarios for the Groningen field, Jan van Elk,
Assaf Mar-Or, Leendert Geurtsen, Per Valvatne, Eddy Kuperus and Dirk Doornhof, NAM, Apr 2018.

Ref. 17

Assessment of Hazard, Building Damage and Risk based on Production Scenario Basispad Kabinet for the
Groningen field, Jeroen Uilenreef, Jan van Elk and Assaf Mar-Or, NAM, July 2018.

Ref. 18

Assessment of Subsidence based on Production Scenario “Basispad Kabinet”, Dirk Doornhof and Onno van der
Wal, NAM, July 2018. EP201806209337

Ref. 19

Hazard and Risk Assessment - GTS raming 2019, Jan van Elk, Assaf Mar-Or, Leendert Geurtsen, Jeroen Uilenreef
and Dirk Doornhof, NAM, Mar 2019.

Ref. 20

Letter to NAM: Mijnbouwwet instemmingsbesluit winningsplan Groningenveld; aanvullingsverzoek, Directeur
Energy Markt Ministry of Economic Affairs, 20 December 2013.

163

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for the Groningen field - March 2020
Ref. 21

Verwachtingenbrief – Winningsplan 2016

Ref. 22

Wijzigingsbesluit 24th May 2017

Ref. 23

Letter to Parliament “Gaswinning Groningen”, 29th March 2018, Minister of Economic Affairs and Climate
Policy.

Ref. 24

Letter to Parliament “Betreft Voortgang maatregelen gaswinningsbrief”, 6 juni 2018, Minister of Economic
Affairs and Climate Policy.

Ref. 25

Letter “Verwachtingenbrief aanvulling winningsplan Groningenveld 2016” to NAM, 2nd May 2018, Minister
of Economic Affairs and Climate Policy.

Ref. 26

Verwachtingenbrief “verzoek tot voorspellen van twee operationele strategieen voor het gas jaar 2019-2020”,
Ministerie van Economische Zaken en Klimaat met 13 Februari 2019 met bijlage “raming benodigd
Groningenvolume en capaciteit gasjaar 2019/2020 en verder”, Gasunie Transport Services, Februari 2019.

Ref. 27

Instemmingsbesluit Winningsplan Groningenveld, Ministry of Economic Affairs, 30th September 2016

Ref. 28

Eerste advies Adviescommissie ‘Omgaan met risico’s van geïnduceerde aardbevingen’ 23rd June 2015,

Ref. 29

Tweede advies Omgaan met hazard- en risicoberekeningen in het belang van handelingsperspectief voor
Groningen Adviescommissie ‘Omgaan met risico’s van geïnduceerde aardbevingen’ 29th October 2015

Ref. 30

Eindadvies Handelingsperspectief voor Groningen Adviescommissie ‘Omgaan met risico’s van geïnduceerde
aardbevingen’ (Commissie-Meijdam), 14th December 2015

Ref. 31

Verwachtingenbrief – GTS Raming 2020

Ref. 32

Letter Minister EZK (Wiebes) to the Voorzitter van de Tweede Kamer: Raming Gaswinning Groningen
2020/2021 en verder, 21 februari 2020 (Ons kenmerk DGKE-PGG / 20027392)

Ref. 33

Voorlopige beoordeling van de actualisatie van de HRA modellen, bijlage 1, SodM, 1 februari 2020

Ref. 34

Groningen Dynamic Model Update 2018, Quint de Zeeuw and Leendert Geurtsen, NAM, Jun 2018.

Ref. 35

Groningen Dynamic Model Update 2018 – V6, Quint de Zeeuw and Leendert Geurtsen, NAM, Oct 2018.
EP201809202872.

Ref. 36

Investigation of gas presence in the aquifer of the Groningen field, Gulfiia Ishmukhametova, NAM, Sept 2018.

Ref. 37

Groningen Basin Model 2018, Hemmo Boscher, NAM, June 2018.

Ref. 38

V6 Ground-Motion Model (GMM) for Induced Seismicity in the Groningen Field - With Assurance Letter, Julian
J Bommer, Benjamin Edwards, Pauline P Kruiver, Adrian Rodriguez-Marek, Peter J Stafford, Bernard Dost,
Michail Ntinalexis, Elmer Ruigrok and Jesper Spetzler, , Dec 2019.

Ref. 39

Coefficient files for V6 Ground-Motion Model (GMM) for Induced Seismicity in the Groningen Field, Julian J
Bommer, Benjamin Edwards, Pauline P Kruiver, Adrian Rodriguez-Marek, Peter J Stafford, Bernard Dost,
Michail Ntinalexis, Elmer Ruigrok and Jesper Spetzler, , Dec 2019.

Ref. 40

Reader for the Spatial and Temporal development of induced seismicity in the Groningen field, S.J. Bourne, S.J.
Oates, NAM, Nov 2017.

Ref. 41

Gaussian Process Optimisation of the Elastic Thin-Sheet Seismological Model for Depletion Induced Seismicity
in the Groningen Gas Field, G. Joosten, J. Limbeck, G. Kaleta, S. Bourne, K. Nevenzeel, Shell Research and IBM
Services, Feb 2019.

Ref. 42

Evolution of induced earthquake magnitude distributions with increasing stress in the Groningen gas field, S.J.
Bourne, S.J. Oates, NAM, Dec 2019.

Ref. 43

Assurance of the report: Evolution of induced earthquake magnitude distributions with increasing stress in the
Groningen gas field by Stephen Bourne and Steve Oates, S.J. Bourne, S.J. Oates, NAM, Jan 2020.

164

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Ref. 44

Assurance Check on Exposure, Fragility and Fatality Models for the Groningen Building Stock, Assurance Panel
Building Fragility, Typology and Exposure, Assurance Panel Building Fragility, Typology and Exposure, Dec
2019.

Ref. 45

Exposure Database (EDB) voor het gebied van het Groningen veld - Stand van Zaken juni 2018, NAM, June
2018

Ref. 46

Exposure Database (EDB) voor het gebied van het Groningen veld - Stand van Zaken september 2018, NAM,
September 2018

Ref. 47

Exposure Database V6 - Data Documentation, Technical Report & Postprocessing to produce the v6 Exposure
Model, NAM, July 2019

Ref. 48

Pruiksma, J.P. & Breunese, J.N. & Thienen-Visser, Karin & De Waal, Hans. (2015). Isotach formulation of the
rate type compaction model for sandstone. International Journal of Rock Mechanics and Mining Sciences. 78.
127-132. 10.1016/j.ijrmms.2015.06.002.

Ref. 49

NAM (2017) Ensemble Based Subsidence application to the Ameland gas field – long term subsidence study
part two (LTS-II) continued study.

Ref. 50

Van Eijs, R., & Van der Wal, O. (2017). Field-wide reservoir compressibility estimation through inversion of
subsidence data above the Groningen gas field. Netherlands Journal of Geosciences, 96(5), S117-S129.

Ref. 51

Kole, P., Cannon, M., Tomic, J., Bierman, S. (2020) Analysis of first four years of in-situ strain data in Zeerijp3A. EP201908210907 (Draft)

Ref. 52

Geertsma, J. and van Opstal, G. (1973). A Numerical Technique for Predicting Subsidence Above Compacting
Reservoirs, Based on the Nucleus of Strain Concept. Verh. Kon. Ned. Geol. Mijnbouwk. Gen., 28, pp. 63-78.

Ref. 53

NAM (2019) Stabiliteitsanalyse van waterpaspeilmerken 2019 – Toelichting methodiek en toepassing op de
meetregisters “Noord Nederland, Groningen en Waddenzee 2018” en “Zuid-Holland 2017”. Nederlandse
Aardolie Maatschappij, 12 juli 2019, EP20190621048

Ref. 54

Advies vaststellingsbesluit Groningen gasveld 2019/2020 of 7 May 2019, TNO

Ref. 55

Report on the v7 Fragility and Consequence Models for the Groningen Field, Helen Crowley and Rui Pinho,
March, 2020

Ref. 56

Production Optimisation 2018, Per Valvatne, Leendert Geurtsen, Assaf Mar-Or, Gosia Kaleta, Jan Limbeck and
Gerard Joosten, NAM, October 2018.

Ref. 57

Letter to Ministry of Economic Affairs: NAM Productie Optimalisatie Studie: Beoordeling en advies,
T.F.Kockelkoren, SodM, 16 October 2018.

Ref. 58

Bouwstenen voor Operationele Strategie Groningenveld 2018/2019, NAM, 15/6/2018.

Ref. 59

Instemmingsbesluit Groningen gasveld 2018-2019, Minister of Economic Affairs and Climate Policy,
14/11/2018.

Ref. 60

Definitief vaststellingsbesluit Groningen gasveld 2019-2020, Minister of Economic Affairs and Climate Policy,
10/09/2019.

Ref. 61

SBR, Trillingsrichtlijn deel A – Schade aan Gebouwen, Stichting Bouw Research, Rotterdam, 52 pp., 2017.

Ref. 62

Brief Minister of Economic Affairs, Vooraankondiging veiligheidsnorm, 21 October 2015, kenmerk DGETM-EO
/ 15146924

Ref. 63

Eerste advies panel van hoogleraren over risicobeleid en veiligheidsmaatregelen geïnduceerd
aardbevingsrisico, panel van hoogleraren, bestaande uit prof. dr. Eric Cator (Applied stochastics, Radboud
Universiteit Nijmegen), prof. dr. Ira Helsloot (Besturen van veiligheid, Radboud Universiteit Nijmegen) en prof.
dr. ir. Jan Rots (Structural mechanics, Technische Universiteit Delft), 27-6-2018.

Ref. 64

Eerste deel van aanvullend advies over veiligheidsrisico’s van infrastructuur en cumulatie-effecten van
geïnduceerde aardbevingen, panel van hoogleraren, bestaande uit prof. dr. Eric Cator (Applied stochastics,

165

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020
Radboud Universiteit Nijmegen), prof. dr. Ira Helsloot (Besturen van veiligheid, Radboud Universiteit
Nijmegen) en prof. dr. ir. Jan Rots (Structural mechanics, Technische Universiteit Delft), 16 oktober 2018
Ref. 65

Addendum op advies panel van hoogleraren over risicobeleid en veiligheidsmaatregelen geïnduceerd
aardbevingsrisico panel van hoogleraren, bestaande uit prof. dr. Eric Cator (Applied stochastics, Radboud
Universiteit Nijmegen), prof. dr. Ira Helsloot (Besturen van veiligheid, Radboud Universiteit Nijmegen) en prof.
dr. ir. Jan Rots (Structural mechanics, Technische Universiteit Delft)8 november 2018

Ref. 66

Briefadvies panel van hoogleraren over de berekening van de gemiddelde verblijfsduur in verschillende type
bouwwerken, Panel van Hoogleraren (Prof. dr Eric Cator, prof. dr. Ira Helsloot en prof. dr. Jan Rots), 2 nd
February 2019.

Ref. 67

Kamerbrief “Advies hooglerarenpanel over berekening aardbevingsrisico”, Ministerie van Economische Zaken
en Klimaat, 7th maart 2019.

Ref. 68

Met het oog op de tijd - Een blik op de tijdsbesteding van Nederlanders, Sociaal en Cultureel Plan Bureau,
Mariëlle
Cloïn
(red.),
November
2013,
https://www.scp.nl/Publicaties/Alle_publicaties/Publicaties_2013/Met_het_oog_op_de_tijd

Ref. 69

Beoordeling advies “berekening gemiddelde verblijfsduur” van het panel van hoogleraren, SodM, 11 th
February 2019.

Ref. 70

Guidelines for Developing Quantitative Safety Risk Criteria, Center for Chemical Process Safety, ISBN 978-0470-26140-8

Ref. 71

Toelichting Wijk- en Buurtkaart 2013, 2014 en 2015 Respectievelijk Versie 3, 2 en 1, CBS, December 2015.

Ref. 72

Advies van 11 februari 2019 aan de minister van EZK over te gebruiken belasting bij de beoordelingen i.h.k.v.
NPR:2018, SodM.

Ref. 73

Voortgangsverslag van de versterking huizen en afbouw van de gaswinning in Groningen, SodM, 26 June 2019.

Ref. 74

Advies van 10 oktober 2019 aan de Minister van EZK over te gebruiken tijdvak bij de beoordeling i.h.k.v.
NPR:2018, SodM, 10 October 2019.

Ref. 75

Advies van 19 december 2019 inzake NPR 9998 en te gebruiken dreigingskaart, SodM 19 December 2019.

Ref. 76

European Seismological Commission (G. Grünthal), European Macroseismic Scale 1998, EMS-98, 1998

Ref. 77

Methodology Prognosis of Building Damage and Study and Data Acquisition Plan for Building Damage,
Nederlandse Aardolie Maatschappij BV (Jan van Elk, Jeroen Uilenreef and Dirk Doornhof), 30 January 2017

Ref. 78

Updated Empirical GMPEs for PGV from Groningen Earthquakes – March 2019, Julian J Bommer, Peter J
Stafford and Michail Ntinalexis, March 2019

Ref. 79

Exposure Database V7 - Data Documentation, Technical Report & Postprocessing to produce the v7 Exposure
Model, NAM, March 2020

Ref. 80

Report on the v6 fragility and Consequence Models for the Groningen field, Helen Crowley, Rui Pinho and
Francesco Cavalieri, March 2019

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Appendix A – Relevant Correspondence
Expectation Letter (Verwachtingenbrief)

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Voorlopige beoordeling van de actualisatie van de HRA-modellen.

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Appendix B – Glossary of Terms
This appendix contains a glossary of terms used in this report and the documents references in this
report. More extensive glossaries for terms used in earthquake studies have been compiled by the
USGS:
https://earthquake.usgs.gov/learn/glossary/?alpha=ALL
and at the web-site www.earthquake-report.com.
https://earthquake-report.com/2011/02/15/glossary-of-earthquake-terms-2/

Term
Acceleration

Accelerogram
Building Code

Cold temperature year

Earthquake

Epicenter
Eurocode 8

Disaggregation

Fault
Fault Plane

Explanation
The rate of change of velocity of a reference point. Commonly expressed
as a fraction or percentage of the acceleration due to gravity (g) where g
= 980 cm/s2. - (USGS)
The recording of the acceleration of the ground during an earthquake.
A building code, or building control, is a set of rules that specify the
minimum acceptable level of safety for constructed objects such as
buildings and non-building structures. The main purpose of building
codes are to protect public health, safety and general welfare as they
relate to the construction and occupancy of buildings and structures. A
seismic code, refers to a building code which uses earthquake-resistant
design principles. (USGS - CEDIM)
The demand for gas is dependent on the weather and especially on
whether the winter will be cold. This is also reflected in the production
required from the Groningen gas field to ensure security of supply.
Therefore a sensitivity is performed in this hazard and risk assessment
where the target year (gas-year 2020/2021) has a cold winter.
The expected (or probable) life loss, injury, or building damage that will
happen, given the probability that some earthquake hazard occurs.
Earthquake risk and earthquake hazard are occasionally used
interchangeably. (USGS)
The point on the Earth's surface vertically above the point (focus or
hypocenter) in the crust where a seismic rupture nucleates. (EQCanada)
The Eurocodes are the current technical standards for structural design
in Europe, and it is now compulsory for the 28 countries in the Eurocode
zone to adopt these. Eurocode 8 specifically deals with earthquakeresistant design of structures (CEN, 2006). Each country adopting
Eurocode 8 must develop a National Annex to indicate how the code is
implemented; the National Annex for the Netherlands is being
developed. Eurocode 8 uses a standard practice to represent seismic
hazard via PGA maps associated with ground motions having a 10%
probability of exceedance during 50 years, equivalent to 0.2%/year for a
stationary process, or a return period of 475-years.
Disaggregation of a Monte-Carlo simulation result is a technique to
determine the range of the factors with the largest impact on the
simulation result.
A fracture along which there has been significant displacement of the two
sides relative to each other parallel to the fracture. (USGS)
The surface on which the earthquake movement takes place. (CEDIM)
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Gas-year

Geophone

Ground
(Shaking)

Motion

GTS-raming

Gutenberg-Richter

Hazard

Hypocenter
Individual
Risk

Earthquake

Inside Local Personal
Risk (ILPR)
Liquefaction

Local
(LPR)

Personal

Risk

Local site conditions

Nationale
Praktijk
Richtlijn: NPR 9998

Magnitude

Gas-years are the 12-month period starting at 1st October. The gas-year
2020/2021 is the period from 1st October 2020 up to and including 30st
September 2021. Gas-years are used to avoid the high gas demand winter
period to be split over two reporting periods.
A device that converts ground movement (velocity) into voltage, which
may be recorded at a recording station. The deviation of this measured
voltage from the base line is called the seismic response and is analysed
for structure of the earth.
General term referring to the qualitative or quantitative aspects of
movement of the Earth's surface from earthquakes or explosions. Ground
motion is produced by waves that are generated by sudden slip on a fault
or sudden pressure at the explosive source and travel through the Earth
and along its surface. (USGS)
The hazard and risk assessment is based on a production profile for the
Groningen field. This is based on a prognosis of demand for Groningen
gas prepared by GTS. GTS raming 2020 as made in January 2020.
Earthquakes appear to follow a pattern through time in terms of no. of
earthquakes vs. magnitude. This is called the Gutenberg-Richter criterion.
(CEDIM)
Any physical phenomenon associated with an earthquake that may
produce adverse effects on human activities. This includes surface
faulting, ground shaking, landslides, liquefaction, tectonic deformation,
tsunami, and seiche and their effects on land use, manmade structures,
and socioeconomic systems. A commonly used restricted definition of
earthquake hazard is the probability of occurrence of a specified level of
ground shaking in a specified period of time. (USGS)
The point within the Earth where an earthquake rupture initiates. Also
commonly termed the focus. (USGS)
The individual earthquake risk is the annual risk that an individual is
exposed to in the various structures in or near which this individual is
present (See also table 7.1).
The probability of death of a fictional unprotected person who is
permanently present in a building (See also table 7.1).
seismology, it refers to the loss of soil strength as a result of an increase
in pore pressure due to ground motion. This effect can be caused by
earthquake shaking. (IASPEI)The transformation of a granular material
from a solid state into a liquefied state as a consequence of increased
pore water pressures and reduced effective stress. In engineering
The probability of death of a fictional unprotected person who is
permanently present in or near a building. This person is thought to be
inside the building 99% of the time and outside near the building 1 % of
the time (See also table 7.1).
A qualitative or quantitative description of the topography, geology, and
soil profile at a site that affect ground motions during an earthquake.
(IASPEI).
This document describes the structural safety of a building in case of
earthquake loads. Constructors can use this guideline to calculate how
strong a building must be in order to comply with the seismic safety
standard for buildings used in the Netherlands.
A number that characterizes the relative size of an earthquake.
Magnitude is based on measurement of the maximum motion recorded
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by a seismograph(sometimes for earthquake waves of a particular
frequency), corrected for attenuation to a standardized distance. Several
scales have been defined, but the most commonly used are (1) local
magnitude (ML), commonly referred to as Richter magnitude, (2) surfacewave magnitude (Ms), (3) body-wave magnitude (Mb), and (4) moment
magnitude (Mw). ML, Ms and Mb have limited range and applicability and
do not satisfactorily measure the size of the largest earthquakes. The
moment magnitude (Mw) scale, based on the concept of seismic
moment, is uniformly applicable to all sizes of earthquakes but is more
difficult to compute than the other types. In principal, all magnitude
scales could be cross calibrated to yield the same value for any given
earthquake, but this expectation has proven to be only approximately
true, thus the need to specify the magnitude type as well as its value. An
increase of one unit of magnitude (for example, from 4.6 to 5.6)
represents a 10-fold increase in wave amplitude on a seismogram or
approximately a 30-fold increase in the energy released. In other words,
a magnitude 6.7 earthquake releases over 900 times (30 times 30) the
energy of a 4.7 earthquake - or it takes about 900 magnitude 4.7
earthquakes to equal the energy released in a single 6.7 earthquake!
There is no beginning nor end to this scale. However, rock mechanics
seem to preclude earthquakes smaller than about -1 or larger than about
9.5. A magnitude -1.0 event releases about 900 times less energy than a
magnitude 1.0 quake. Except in special circumstances, earthquakes
below magnitude 2.5 are not generally not felt by humans. (USGS-IASPEI)
Mijnraad 1500 Lijst
These are the ca 1,500 buildings that in the advice of the Mijnraad of mid2018 had a mean LPR > 10-5/year.
Monte-Carlo Simulation The Monte Carlo simulation is a simulation technique whereby a physical
process is simulated not once but many times, each time with different
starting conditions. The result of this collection of simulations is a
distribution function that displays the entire area of possible outcomes.
Object-related
The Objectgebonden individual earthquake risk is the risk that an
Individual Risk
individual dies in a year due to collapse or falling objects (as a result of an
earthquake) of a building in which or in the direct vicinity of which this
person is present. The residence time in/around that building is therefore
taken into consideration (See also table 7.1).
Outside Local Personal The probability of death of a fictional unprotected person who is
Risk
permanently present near a building (See also table 7.1).
P Wave
A seismic body wave that involves particle motion (alternating
compression and extension) in the direction of propagation. (USGS)
P wave
A P wave, or compressional wave, is a seismic body wave that shakes the
ground back and forth in the same direction and the opposite direction
as the direction the wave is moving.
Peak Acceleration
The highest acceleration in terms of value. (USGS)
PGA
The maximum acceleration amplitude measured or expected in a strongmotion accelerogram of an earthquake. (IASPEI)
Primary Wave
See P Wave (CEDIM)
Risk
The probabilistic determination of the damages a certain hazard can
cause given the existing vulnerability, location and time. (UN )
Risk Assessment
Definition: A methodology to determine the nature and extent of risk by
analyzing potential hazards and evaluating existing conditions of
vulnerability that together could potentially harm exposed people,
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Rupture

S-wave
S Wave Velocity
Secondary Wave

Seismic hazard

Seismic Risk

Seismic Station
Seismic Waves

Seismicity

Seismogram

Seismometer

property, services, livelihoods and the environment on which they
depend.Comment: Risk assessments (and associated risk mapping)
include: a review of the technical characteristics of hazards such as their
location, intensity, frequency and probability; the analysis of exposure
and vulnerability including the physical social, health, economic and
environmental dimensions; and the evaluation of the effectiveness of
prevailing and alternative coping capacities in respect to likely risk
scenarios. This series of activities is sometimes known as a risk analysis
process. (UN/ISDR)
The instantaneous boundary between the slipping and locked parts of a
fault during an earthquake. Rupture in one direction on the fault is
referred to as unilateral. Rupture may radiate outward in a circular
manner or it may radiate toward the two ends of the fault from an interior
point, referred to as bilateral. (USGS)
An S wave, or shear wave, is a seismic body wave that shakes the ground
back and forth perpendicular to the direction the wave is moving.
The velocity of a secondary or S wave. Generally measured in m/s.
(CEDIM)
A seismic body wave that involves a shearing motion in a direction
perpendicular to the direction of propagation. When it is resolved into
two orthogonal components in the plane perpendicular to the direction
of propagation, SH denotes the horizontal component and SV denotes the
orthogonal component. Also known as S waves and shear waves. (PDC)
Risk of a certain ground motion occurring at a location (this can be
defined by scenario modeling via stochastic catalogues, DSHA, PSHA or
other such methods, and can include different types of earthquake
effects) (CEDIM)
See earthquake risk, also the probabilistic risk is the odds of an
earthquake occurring and causing damage within a given time interval
and region. (EQCanada)
A ground position at which a geophysical instrument is located for an
observation. (U-Milwaukee)
An elastic wave generated by an impulse such as an earthquake or an
explosion. Seismic waves may propagate either along or near the Earth's
surface (for example, Rayleigh and Love waves) or through the Earth's
interior (P and S waves). (USGS)
1) The geographic and historical distribution of earthquakes. 2) A term
introduced by Gutenberg and Richter to describe quantitatively the
space, time, and magnitude distribution of earthquake occurrences.
Seismicity within a specific source zone or region is usually quantified in
terms of a Gutenberg-Richter relationship. (ICWGroup/IASPEI)
A record written by a seismograph in response to ground motions
produced by an earthquake, explosion, or other ground-motion sources.
(ICW Group)
A seismometer is a damped oscillating mass, such as a damped massspring system, used to detect seismic-wave energy. The motion of the
mass is commonly transformed into an electrical voltage. The electrical
voltage is recorded on paper, magnetic tape, or another recording
medium. This record is proportional to the motion of the seismometer
mass relative to the Earth, but it can be mathematically converted to a

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Velocity

record of the absolute motion of the ground. Seismograph is a term that
refers to the seismometer and its recording device as a single unit. (NASA)
In reference to earthquake shaking, velocity is the time rate of change of
ground displacement of a reference point during the passage of
earthquake seismic waves commonly expressed in centimeters per
second. (USGS)

181

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Appendix C – List of Abbreviations
AHN
ALLEA
AGE
ALARP
ARUP
ACVG
BAG
Bcm
BGS
BOA
BZK
CBS
CEA
CEDIM
CMI
CMOC
CPT
CVW
DAS
DEEP.nl
DC
DIC
DS
DSS
DTS
DvhN
EBN
EDB
EMS
EPOS
ERIC
EVS
EZ
EZK
FDSN
Frl
GBB
GEM
GMPE
GMM
GNSS
GPS
GR
GWC

Actueel Hoogtebestand Nederland
All European Academies
TNO - adviesgroep economische zaken
As Low As Reasonably Practicable
Engineering Company named after founder: Ove Arup
Adviescollege Veiligheid Groningen
Basisregistratie Adressen en Gebouwen
N.Bcm refers to a volume of a billion normal cubic meters. Normal means the volume is
measured at a standard temperature (0 degree C) and pressure (1 bar)
British Geological Survey
Begeleidingscommissie Onderzoek Aardbevingen
Ministry of Internal Affairs (Ministerie van Binnenlandse Zaken)
Centraal Bureau Statistiek
China Earthquake Administration
Center for Disaster Management and Risk Reduction Technology
Compaction Monitoring Instrument
Crack Mouth Opening Displacement
Cone Penetration Test
Centrum Veilig Wonen
Distributed Acoustic Sensing
Research program led by NWO
Carboniferous Formation
Digital Image Correlation
Damage State
Distributed Strain Sensing
Distributed Temperature Sensing
Dagblad van het Noorden (regional newspaper)
Energy Beheer Nederland
Exposure Database
European Macroseismic Scale
European Plate Observatory System
European Research Infrastructure Consortium
Extended Visual Screening
Ministerie van Economische Zaken (in English MEA)
Ministerie van Economische Zaken en Klimaat (in English MEAC)
Federation of Digital Seismograph Networks
Friesland
Groninger Bodembeweging
Global Earthquake Model
Ground Motion Prediction Equations
Ground Motion Model
Global Navigation Satellite System
Global Positioning System
Group Risk
Gas water contact
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HRA
HRBE
ILPR
I&M
InSAR
IR
IVO
IU
KEM
KNAW

Hazard and Risk Assessment
High Risk Building Element
Inside Local Personal Risk
Ministerie van Infrastructuur en Milieu
Interferometric Synthetic Aperture Radar
Individual Risk
Instituut versterkingsopgave
Interrogation Unit
Kenninsprogramma Effecten Mijnbouw (Knowledge program Effects of Mining)
Koninklijk Nederlands Academie van Wetenschappen (Royal Netherlands Academy of Arts
and Sciences)
KNGMG
Koninklijk Nederlands Geologisch Mijnbouwkundig Genootschap
KNMI
Koninklijk Nederlands Meteorologisch Institute
KU Leuven Katholieke Universiteit Leuven (Catholic University Leuven)
LE
Latest Estimate
LIDAR
Laser Imaging Detection And Ranging
LPR
Local Personal Risk
LNEC
Laboratorio Nacional de Engenharia Civil (Lisbon)
M
Earthquake Magnitude
MEA
Ministry of Economic Affairs (prior to 2017)
MEAC
Ministry of Economic Affairs and Climate Policy (from 2017 onwards)
MR
Maatschappelijk Risico
MASW
Multichannel Analysis of Surface Waves
MIT
Massachusetts Institute of Technology
NAM
Nederlandse Aardolie Maatschappij B.V.
NARS
Network of Autonomously Recording Seismographs
NCG
Nationaal Coordinator Groningen
NFU
Netherlands Federation of University Medical Centres
NGO
Non-governmental Organisation
NORSAR
Norwegian Seismic Array (Norwegian independent, not-for-profit, research foundation
within the field of geo-science)
NPR
Nationale Practijkrichtlijn
NTNU
Norges teknisk-naturvitenskapelige universitet (Norwegian University of Science and
Technology in Trondheim)
NWO
Nederlands Organisatie voor Wetenschappelijk Onderzoek (Dutch National Science
Foundation)
OECD
Organisation of Economic Cooperation and Development
OGP
Onafhankelijk Geologen Platform
OIA
Objectgebonden Individueel Aardbevingsrisico (Object related individual earthquake risk)
OIR
Object-related individual risk (same as OIA)
OVV
Onderzoeksraad voor Veiligheid (Safety Board)
PGA
Peak Ground Acceleration
PGK
Petroleum Geologie Kring
PGV
Peak Ground Velocity
PNL
Pulsed Neutron log
PRBE
Potential High Risk Building Elements
QRM
Quantitative Reservoir Management
RFT
Repeat Formation Tester
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RGD
RMSE
RUG
RVS
RvS
RWS
SAC
SCAL
SED
SINTEF
SMS
SodM
SPE
SPG
SPTG
SSHAC
Tcbb
TCMG
TIVO
TK
TNO
TNO-AGE
TO2
TPA
TU Delft
TU/e
UU
UvA
URM
USGS
USNRC
VoVo
VSNU
Vs800

Rijksgeologische Dienst (later also TNO-NIGT)
Roor Mean Square Estimate
Rijksuniversiteit Groningen
Rapid Visual Screening
Raad van State
Rijkswaterstaat
Scientific Advisory Committee
Special Core Analysis Laboratory
Schweizerischer Erdbebendienst (Swiss Seismological Survey)
Stiftelsen for industriell og teknisk forskning (Foundation for Scientific and Industrial
Research)
Samenwerking Mijnbouw Schade
Staatstoezicht op de Mijnen (also SSM State Supervision of Mines)
Society of Petroleum Engineers
Static Pressure Measurement
Static Pressure and Temperature Measurement
Senior Seismic Hazard Analysis Committee
Technische commissie bodembeweging
Tijdelijk Commissie Mijnbouwschade Groningen
Tijdelijke Instituut versterkingsopgave
Tweede Kamer (Dutch equivalent of House of Commons)
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek,
Netherlands Organisation for Applied Scientific Research
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek – Advies
Groep Economische Zaken
Toegepast Onderzoek Organisaties (Federation of Applied Research Institutes); Deltares,
MARIN, NLR, TNO and WR
Technische Platform Aardbevingen
Technische Universiteit Delft
Technische Universiteit Eindhoven
Universiteit Utrecht
Universiteit van Amsterdam
Un-reinforced Masonry
United States Geological Survey
United States Nuclear Regulatory Commission
Voorlopige voorziening
Vereniging samenwerkende universiteiten in Nederland (Association of Universities in the
Netherlands)
Shear wave velocity up to a depth of 800 m

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Appendix D – Reconciliation of Request for proposal of the
operational strategy for gas-year 2020/2021
The letter from the expectation letter (verwachtingenbief) from the Minister contained a list of
deliverables to be included in this report. This Appendix contains a quick reference guide linking these
requests to the tables and figures in this report.

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Rapportage
Rapportage
Een tabel of figuur waarmee de inzet van de clusters als functie van de dagvraag eenduidig wordt
vastgelegd.
Een overzicht van de kans op zwaardere bevingen (M>3,5; 4,0; 4,5) per gasjaar, voor het gasjaar 20212022 en de 10 volgende gasjaren.
Berekeningen van het Plaatsgebonden Persoonlijk Risico (LPR) (hazardkaarten en LPR-curves) voor het
gasjaar 2020-2021 en een overzicht in een tabel van het aantal gebouwen dat niet voldoet aan de
veiligheidsnorm (berekend met de verwachtingswaarde van het risico per gebouw, met P90 in een
bijlage) per gasjaar, voor het gasjaar 2020-2021 en de 10 volgende gasjaren.

Een doorkijk van de voorgestelde clusterafbouw (inclusief de productielocaties) bij de geraamde
afbouw van de productie in de jaren na komend gasjaar bij voortzetting van de clusterinzetvolgorde op
basis van de door GTS opgegeven dataset met volumeraming. Een overzicht van benodigde clusters en
een duiding van de benodigde opstarttijd indien een cluster alleen nog nodig is voor de door GTS
geadviseerde capaciteit en er dus planmatig geen volume meer nodig is uit het Groningenveld.
Ontwikkeling van de seismische activiteit tot 10 jaar na het gasjaar 2020-2021, weergegeven in
gasjaren.

De ontwikkeling van de bodemdaling inclusief de verwachting moet weergegeven worden in:

Kaarten van de ruimtelijke ontwikkeling van de bodemdaling in 2018 (laatste waterpassing) tot
2030.

Figuren met de tijdsafhankelijke ontwikkeling van de bodemdaling op meerdere
waterpaslocaties verdeeld over het gasveld.

Table, Map or Figure
Operational Strategy and Summary (Samenvatting) of this
report
Main text of the HRA Report
Chapter Event Rate
Tables 5.2a to b.
Main text of the HRA Report
Chapter Hazard Assessment
Hazard Maps: Fig. 6.4a to c, 6.6, 6.7 and 6.8.
Chapter Risk Assessment
LPR-Curves: Figure 7.1 a to c and Figure 7.2
Building Tables: Tables 7.3 a to f, Figure 7.4 and Figure 7.5
Maps: Figure 7.7, 7.8, 7.9, 7.10a to c and Fig. 11a to c.
Appendix E - Additional tables for buildings above the
Meijdam-norm for calendar years.
Figures e.1a to f
Operational Strategy
Chapter Reservoir and Pressure Modelling
Cluster reduction based on volume forecast (GTS -raming):
Fig. 3.3. and 3.4.
Main text of the HRA Report
Chapter Event Rate
Figure 5.1, 5.2, 5.3 and 5.4.
Appendix G – Seismic Event Rates
Fig. g.1a and g.1b.
Main text of the HRA Report
Chapter Subsidence

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Bijlage
Bijlage
Kaarten per operationele strategie van de drukontwikkeling tot 30 jaar na het gasjaar 20202021.
Ontwikkeling van de seismische activiteit per operationele strategie tot 30 jaar na het gasjaar
2020-2021, weergegeven in stappen van 5 jaar.

Verschilkaarten van de drukverdeling tussen de verschillende operationele strategieën, per
gasjaar.
Verschilkaarten van de ruimtelijke verdeling van de seismische activiteit (mean) in de
verschillende operationele strategieën, inclusief aanduiding hoeveel bevingen er meer/minder
in een gebied zijn opgetreden, per gasjaar.
Verschilkaarten van de seismische dreiging in de verschillende operationele strategieën.

Een overzicht per operationele strategie van het aantal gebouwen dat niet voldoet aan de
veiligheidsnorm (berekend met P90) per gasjaar, voor het gasjaar 2020-2021 en de 10 volgende
gasjaren.

Daarnaast een zelfde overzicht met het aantal gebouwen berekend met zowel de
verwachtingswaarde als P90 voor de kalenderjaren 2020 tot en met 2030. Deze dient uiterlijk
12 april in mijn bezit te zijn.

Table, Map or Figure
Appendix F – Pressure Differences
Maps: Fig. f1.a to k.
Main text of the HRA Report
Chapter Event Rate
Figure 5.1 en 5.2 for gas-years up to 2034/2035
Additional years in Appendix G
Main text of the HRA Report
Chapter Reservoir and Pressure Modelling
Figure 3.7 and 3.7.
Main text of the HRA Report
Chapter Seismic Event Rate
Figure 5.5.
Main text of the HRA Report
Chapter Hazard Assessment
Figure 6.7
Main text of the HRA Report
Chapter Risk Assessment
LPR-Curves: Figure 7.1 a to c and Figure 7.2
Building Tables: Tables 7.3 a to f, Figure 7.4 and Figure 7.5
Maps: Figure 7.7, 7.8, 7.9, 7.10a to c and 7.11a to c. .
Appendix E - Additional tables for buildings above the Meijdamnorm for calendar years.
Figures e.1a to f

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Bijlage

Table, Map or Figure

Kaarten per operationele strategie met de locaties en het type van de gebouwen boven Main text of the HRA Report
de norm op basis van de verwachtingswaarde en P90 voor het gas-jaar 2020-2021.
Chapter Risk Assessment
Maps: Figure 7.6, 7.7 and 7.8.
Verschilkaarten van de ruimtelijke verdeling van de gebouwen boven de norm voor de Main text of the HRA Report
verwachtingswaarde en P90 voor het gasjaar 2020-2021.
Chapter Risk Assessment
Maps: Figure 7.10a to c and 7.11a to c.
Bij het overzicht van het aantal gebouwen dat niet aan de veiligheidsnorm voldoet voor Main text of the HRA Report
het gasjaar 2020-2021 (berekend met de verwachtingswaarde en P90) een overzicht Chapter Risk Assessment
welk percentage van deze gebouwen in het huidige gasjaar (volgens HRA 2019) Table: 7.1a to f.
eveneens niet aan de norm voldeed, uitgesplitst in verwachtingswaarde, P90 en Reconciliation: 7.12 7.13 and 7.14.
conform norm in de HRA2019.

188

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Appendix E – Additional tables for buildings above the Meijdamnorm for calendar years.
In the main text of this report the hazard, building damage and risk assessment for production profile
GTS-raming 2020 was presented for gas-years. The choice of gas-years for the document is because the
Ministerial Decision this report informs will also be based on gas-years.
However, the plan of approach (Plan van Aanpak) for the structural upgrading program prepared by the
NCG is based on calendar years. In this appendix the numbers of buildings above the Meijdam-norm are
provided in calendar years.

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Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

137
141
67
7
5
3
0
0
0
0

0
0
0
0
0
0
0
0
0
0

35
20
7
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1a

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and average temperature
year. See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020”, operational
strategy 1 for an average weather year.

Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

144
220
64
43
16
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

35
71
4
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1b

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and cold temperature year.
See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020” operational strategy
1 for a cold weather year.

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Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

159
94
32
16
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

43
12
4
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1c

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 1 and warm temperature year.
See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020” operational strategy
1 for a warm weather year.

Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

127
126
28
6
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

20
22
4
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1d

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and average temperature
year. See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020” operational
strategy 2 for an average weather year.

191

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for the Groningen field - March 2020

Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

116
214
61
24
5
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

13
67
0
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1e

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and cold temperature year.
See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020” operational strategy
2 for a cold weather year.

Calendar
year

Mean LPR
10-4/year

Mean LPR
10-5/year

Mean OIA
10-4/year

Mean OIA
10-5/year

P90 LPR
10-4/year

P90 LPR
10-5/year

P90 OIA
10-4/year

P90 OIA
10-5/year

2020

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

108
22
31
7
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0

10
0
4
0
0
0
0
0
0
0

2021
2022
2023
2024
2025
2026
2027
2028
2029
Table e.1f

Number of buildings with mean LPR exceeding 10-5/year and 10-4/year, for production profile GTS-raming 2020, Operational Strategy 2 and warm temperature year.
See main text for further explanation. These are shown for each year of the period 2020 to 2029 for the production profile “GTS-raming 2020” operational strategy
2 for a warm weather year.

192

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

193

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Figure e.1

Graphs show the Local Personal Risk associated with the production profile “GTS-raming 2020” for average,
cold weather and warm weather calendar-years for the period 2020 to 2029.
Right graphs:
number of buildings exceeding the norm LPR larger than 10-5/year
Left graphs:
number of buildings exceeding the norm LPR larger than 10-4/year
Top graphs:
number of buildings exceeding the norm for mean LPR
Bottom graphs:
number of buildings exceeding the norm for P90 LPR

194

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for the Groningen field - March 2020

Appendix F – Pressure Differences
Pressure (bar)

Pressure
difference (bar)

195

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

196

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

197

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

198

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

199

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

200

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

201

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

202

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

203

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Pressure (bar)

Pressure
difference (bar)

204

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020
Pressure (bar)

Pressure
difference (bar)

Figure f.1

Pressure and pressure difference maps for average temperature scenario for gas-years until gas-year 2050/2051.

205

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Appendix G – Seismic Event Rate
The reservoir model has been history matched to reservoir pressure, water encroachment, gravity and
compaction data stretching back to the start of production in 1963. Dynamic reservoir models have
been used for many decades in the oil industry and the modelling software used to prognose future
pressure development is used all over the world. Confidence in long-term pressure forecasts is
therefore relatively high.
The HRA-model is bespoke for the induced seismicity in the Groningen field. The model has been
developed based on the studies carried out as part of the Study and Data Acquisition Plan and data
acquired in the Groningen field. It has been calibrated to the seismic catalogue of earthquakes with a
magnitude larger than M=1.5, maintained by KNMI. While confidence in the seismic prognoses
prepared by the HRA-model is high for the next 10 to 15 years, caution is required when using the
model to forecast for longer time scales. Especially, more than 15 years in the future when seismic
event rates are by comparison low, processes not incorporated in the model might play a role.
Research on the Zeerijp core has so far shown that these effects (if any) are for compaction most likely
small. However, the influence of such effects cannot be excluded.

Figure g.1a

Seismic Activity Rate of earthquakes for the period gas-years 2012/2013 to 2050/2051 based on production
profile GTS-raming 2020 for the average weather temperature gas-year and production profile for operational
strategy 1.

206

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Figure g.1b

Seismic Activity Rate of earthquakes for the period gas-years 2012/2013 to 2050/2051 based on production
profile GTS-raming 2020 for the average weather temperature gas-year and production profile for operational
strategy 2.

207

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Appendix H - Exceedance Probabilities Calendar-year
The probability of an earthquake with a magnitude exceeding a given magnitude can be assessed using
the seismological model (Ref. 403 to Ref. 436). In table 1a and 1b the annual probability of an
earthquake occurring with a magnitude exceeding the specified magnitude is given. For instance, the
probability of an earthquake occurring in calendar-year 2020 with a magnitude exceeding ML=3.6 (the
magnitude of the Huizinge earthquake) is equal to 5.16%. In the HRA for GTS-raming 2019, this
probability was close to 9.3%. The probability of an earthquake occurring in calendar-year 2020 with a
magnitude exceeding ML=3.6 has halved between these two assessments (HRA GTS-raming 2019 and
HRA GTS-raming 2020). For larger exceedance magnitudes the reduction between these two HRA
versions is even larger.
Calendar-year
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Table h.1a

P(M>=4.0)
1.31%
1.20%
0.95%
0.77%
0.69%
0.64%
0.60%
0.54%
0.51%
0.47%
1.31%

P(M>=4.5)
0.17%
0.16%
0.12%
0.10%
0.09%
0.08%
0.08%
0.07%
0.07%
0.06%
0.17%

P(M>=5.0)
0.02%
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%

Table with annual probabilities for occurrence of earthquakes exceeding a set magnitude. This table is for
production profile GTS-raming 2020, average temperature calendar-year and operational strategy 1.

Calendar-year
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Table h.1b

P(M>=3.6)
4.91%
4.51%
3.57%
2.97%
2.67%
2.41%
2.20%
2.02%
1.88%
1.74%
4.91%

P(M>=3.6)
4.91%
4.60%
3.49%
2.94%
2.67%
2.43%
2.23%
2.06%
1.86%
1.76%
4.91%

P(M>=4.0)
1.30%
1.23%
0.93%
0.77%
0.71%
0.63%
0.60%
0.57%
0.48%
0.49%
1.30%

P(M>=4.5)
0.17%
0.16%
0.12%
0.10%
0.09%
0.08%
0.08%
0.07%
0.06%
0.06%
0.17%

P(M>=5.0)
0.02%
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.02%

Table with annual probabilities for occurrence of earthquakes exceeding a set magnitude. This table is for
production profile GTS-raming 2020, average temperature calendar-year and operational strategy 2.

208

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020
Annual probabilities for occurrence of earthquakes exceeding a set magnitude
Gas-years
6%

5%

Probability / year

4%

3%

2%

1%

0%

2020/2021 2021/2022 2022/2023 2023/2024 2024/2025 2025/2026 2026/2027 2027/2028 2028/2029 2029/2030
(mag>=3.6)

(mag>=4.0)

(mag>=4.5)

(mag>=5.0)

OS2_gas (mag>=3.6)

OS2_gas (mag>=4.0)

OS2_gas (mag>=4.5)

OS2_gas (mag>=5.0)

Annual probabilities for occurrence of earthquakes exceeding a set magnitude
Calendar-years
6%

Probability / year

5%

4%

3%

2%

1%

0%

2020

Figure h.1

2021

2022

2023

2024

2025

2026

2027

2028

(mag>=3.6)

(mag>=4.0)

(mag>=4.5)

(mag>=5.0)

OS2_cal (mag>=3.6)

OS2_cal (mag>=4.0)

OS2_cal (mag>=4.5)

OS2_cal (mag>=5.0)

2029

Figure with annual probabilities for occurrence of earthquakes exceeding a set magnitude. This table is for
production profile GTS-raming 2020, average temperature gas-year (above) and calendar-year (below). The
probabilities for operational strategy 1 are solid lines and for strategy 2 are hatched lines.

The probabilities for gas-years appear in the graphs to be lower. However, this is due to the later start
date of the gas-year (1st October) versus the calendar year (1st January).

209

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

Appendix J – Tables for comparing the results of the risk
assessments
The reconciliation of buildings in different safety groups relative to the norm between HRA 2019 and
HRA 2020 is in the main text of the report shown in Sanky-diagrams. In this appendix we present the
reconciliation in the form of two tables.
Results for Operational Strategy 1 of (Previous) HRA 2019 compared to Operational Strategy 1 of (New)
HRA 2020.
Previous P90
Previous meanP
Previous others
New in EDB
Total
Table j.1a

New P90
60
72
30
0
162

New meanP
0
0
0
0
0

New others
3,205
359
148,613
5,617
157,794

No longer in EDB
6
4
348
0
358

Table comparing the buildings in the safety groups between HRA 2019 and HRA 2020 for operational
strategy 1.

Results for Operational Strategy 1 of (previous) HRA 2019 compared to Operational Strategy 2 of
(New) HRA 2020.
Previous P90
Previous meanP
Previous others
New in EDB
Total
Table j.1b

New P90
35
34
13
0
82

New meanP
0
0
0
0
0

New others
3,230
397
148,630
5,617
157,874

No longer in EDB
6
4
348
0
358

Table comparing the buildings in the safety groups between HRA 2019 and HRA 2020 for operational
strategy 2.

210

Seismic Hazard and Risk Assessment of Production Profile “GTS raming 2020”
for the Groningen field - March 2020

211