Managing Pipeline Risks and Material defects through PIMS

7th Pipeline Integrity Management Forum - Berlin

Managing Pipeline Risks and Material defects through PIMSPaul Roovers

What is PIMS?


Pipeline Integrity Management System- Management System

> Policy> Organisation> Plan - Do – Check - Adjust> Audit

- Pipeline Integrity> All actions a TSO takes to keep the

“container” closed

Procedures / documented

Position of PIMS within the different management systems(Extract from EN16348 – 2013)

When does PIMS start ?


PIMS starts when the first gas molecule enters the pipeline

ASME B31.8 ASME B31.8S

EN1594 prEN 16348

Feedback loops

1966197519852013

Integrity Challenges

Once a pipeline is in service, it’s safety level is fixed, but:- a TSO has no full control on the pipelines environment.

- coating will age.

- technology improves → recent pipelines will always be “better” than previous ones.


Growth from the FLX grid over the period 1966 - 2013

What are the hazards we need to cope with?

A pipeline is during it’s operational life exposed at different hazards.

They can be due to design, manufacturing, construction or third party interference

EGIG statistics give a good idea of the type of hazards leading to leak or rupture of a gas transmission pipeline.


(extract from the 8th EGIG report)

Distribution of incidents per cause

Monitoring, detection and discrimination of pipeline defects

In order to deal with the hazards referred to in EGIG integrity management programs are established:- Third party interference:

> Patrolling> Daily helico photo reporting.> Shock detection systems.

- Corrosion> Cathodic protection measurements.> Internal Line Inspection (ILI).

- Construction & Manufacturing defects> Should have been dealt with during material production or during

construction (NDT) but can be reported by ILI or leak detection.

Need to have a holistic approach towards integrity management.


Detection T1(Signal data) d=(D-

Δt*C)/2

14.3 km

Δt = t2-t1 (sec)

C = Acoustic velocity (m/s)

D = Distance between 2 sensors

Sensor 1 Sensor 2

Detection T2(Signal data)

4.7 km

Detection data sent to the RM&D Software

16 “ diameter pipeline - Natural gas – 60 bars

Hydrophone CPA28 Beacon box Communication Network

Remote Monitoring & Diagnosis Center

Shock detection system principle


Qualification of the system

From 2008 to 2011 FLX tested the system by performing impact tests on a reinforced shell installed on the pipeline.

These were performed on various part on the grid in difficult conditions- Branched sections

- Near compressor stations

Some issues during the first tests in 2008- System was blind during first tests as a result of strong variating background

noise.

- ThreatScan: detected shocks of minimum 700J.

- Issues: pirate noises, strong variation of background noises were blinding the system.

The detection algo’s were enhanced (Dynamic Detection Algorithms). As a result during tests in 2011 and 2012 Threatscan system detected all

shocks with an energy equivalent to at least 1681 J (80° pendulum angle). No false alerts were generated. Hammering/ ROW activities were not detected.


Fluxys calibrated impacts (blind tests)

Impact generation system (Pendulum 146 kg)

Protective mechanical interface to the pipeline

Weight Strike Position Angle (°) Energy (J)146kg 3:00 10 31146kg 3:00 20 123146kg 3:00 30 272146kg 3:00 40 426146kg 3:00 50 727146kg 3:00 60 1017146kg 3:00 70 1338146kg 3:00 80 1681146kg 3:00 90 2034


Fluxys calibrated impacts (blind tests)


Shock detection systems From tests performed with an excavator simulator at

Centro Sviluppo Materiali in Rome (33 ton) we know that large diameter pipeline (≥36”) resist to quite large damages.

From the 39 damages (36” 10 - 15mm, API X60, 66 bar) performed at MOP none ruptured, only 1 (the thinnest 10.5 mm leaked).

If these damages are not reported or not found during patrolling they can lead to time delayed.

Therefore FLX decided to place 39 Threatscan beacons for two large diameter pipelines going trough densely populated areas.


Generated impact energy varies between 12 – 26 kJ


Approach with respect to anomalies

The management of anomalies varies in function of how these are detected.

Taking into account the difference in monitoring techniques we can consider two groups of hazards:

- those more likely to be detected by ILI> Corrosion.> Manufacturing defects.

- those detected by other surveillance techniques

> Mechanical damage.> Construction defects.> Weather outside force.

Apart of this a global analysis of the PIMS data via specific software allows to pin point high risk areas for the different type of hazards.

Fluxys ILI policy

In a nutshell: all piggable pipelines shall be pigged.

Re-inspection interval:- 1st inspection:

> after 20 years.

- 2nd inspection: > If no Metal Loss (ML) at 1st run → re-inspection after 15 ans.> ML present at 1st run, re-inspection interval: 7 (to 10) years.

- 3rd inspection:> RunCom® analysis and application of the real corrosion growth rate.> Re-inspection interval taking it into account:

» Maximum interval : 15 years.» Minimum interval: 7 years.


0.00

0.50

1.00

1.50

2.00

2.50

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Number

Years

Estimated number of ERF≥1Runcom growth

• Taking into account a 3 year buffer re-inspection would be performed 12-14 years from now.

• Making two dig ups extends the interval to 15 year.

Immediate action after ILI

In function of the type of defect detected different actions are taken:


Anomaly Criterion Action Assessment model

Metal Loss ASME B31G Bell hole inspection RStreng Effective Area

Dent with metal loss Bell hole inspection

CSA Z662.1• Dent depth max. 6%OD• ML max. 40 wt + length acceptable conf. ASME B31G

Dents between 9:00-15:00Check for coating damage (ECDA)

Bell hole inspectionEPRG recommendations (smooth/ max 7%OD)

Gouge Bell hole inspection + milling

Battelle formula/ RStreng Effective Area

Manufacturing defects Depth ≥ 70% wt Bell hole inspection Pipe Specification

Some ILI issues

External damage should be reported as such e.g.- Run 1: ILI 2004

> 5 Internal ML of which biggest: depth 35%, length 6 mm (ERF = 0.91)

- Run 2: ILI 2011> 3 Dents> 4 External ML of which biggest: depth 39%, length 35 mm (ERF = 0.98)> 2 Dents + ML

- Pearson test was performed and a coating defect was detected.


Important to add to the POF that screening for external damage has to be performed.

Some ILI issues

Repeatability of the reporting e.g. consecutive runs


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 20 40 60 80 100 120 140 160 180

Peak Depth

Lengte

External Metal Loss

2003

2010

What you would expect to get

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0 20 40 60 80 100 120 140 160

Peak Depth

Lengte

External Metal Loss

2000

2008

What you usually get

0

5

10

15

20

25

30

35

40

45

DENT EXT MFG EXT ML INT MFG INT ML

Comparison of anomalies

2000

2008

Global analysis of PIMS data

The ILI data can be completed with information that comes from other integrity management programs like surveillance and incident reports.

This data can also be used to assess non piggable pipelines.

In Fluxys a semi-quantitative risk approach is used to estimate the likelihood of an hazard and its possible consequences.

Choice has been to implement a specific integrity software based upon an industry standard database model (ISAT) developed by GRI.


Example of used risk matrix

Consequences

Like

liho

od


Integrity Data Management

Data generated by the different operational and maintenance processes are regularly assessed to check the need for preventive or mitigative actions.

Large amount of data to be assessed → 127 parameters.

Asset data(GIS-G/Tech)

Internal data sources External data sources

Surveillance data(SAP)

CP reports(SAP)

ILI data(Excel)

Mailings/ visits(Excel)

ISAT(PipeView Integrity)

Population density(NGI)

Soil/ water data(GIS Vl./ Reg. DB)

Infrastructures(Teleatlas)

Land destination(Regional DB)

Habitat VEN regions(Regional DB)

.xml .xml

.csv

Asset data(GIS-G/Tech)

Internal data sources External data sources

Surveillance data(SAP)

CP reports(SAP)

ILI data(Excel)

Mailings/ visits(Excel)

ISAT(PipeView Integrity)

Population density(NGI)

Soil/ water data(GIS Vl./ Reg. DB)

Infrastructures(Teleatlas)

Land destination(Regional DB)

Habitat VEN regions(Regional DB)

.xml .xml

.csv


Assessing the integrity level

f(threat) to be assessed : different parameters were defined as well as their corresponding weight coefficient.

Coating Age; 20%

Wall thickness; 5%

Operating stress; 10%

Cathodic Protection; 30%

Coating system; 20%

Soil corrosivity; 15%

External Corrosionexcl. Fcond

Operating stress; 10%

Cycling; 15%

Type of seam; 20%

Age; 20%

Pipe/ material; 15%

Hydrotest level; 20%

Manufacturing defects

Proximity10%

Crossings10%

Land use20%

Encroach./One Call5%

Mechanical protection25%

Surveillance20%

WallThickness10%

External damage

Soil stability; 30%

Flooding; 30%

Joint; 25%

Crossings; 15%

Construction Defects

External Corrosion Mechanical damage

Manufacturing Defects Construction Defects


Dynamic Segmentation

By using the software the likelihood and consequence of a hazard are calculated for each pipeline segment

The segments are created by a dynamic segmentation process that splits the pipe in function of the parameters defined by the integrity engineer.

1 2 3 4 5 6 7 8 9 10 11 12

Wall th.

Coat Age

CP

…

Segments

Op stress

Dynamic segmentation processIntegrity Parameter

Each segment has homogeneous pipeline characteristics.


Integrity scoring

For each of these parameters a scoring table is generated. These scorings are defined on basis of:

- existing legislation> e.g. RD’s concerning gas transmission, VLAREM

- available technical reports> e.g. from EPRG, GERG, Marcogaz, EGIG

- available literature/ manuals- or if none of the above exist, on in house experience


Likelihood of a threat

After the pipeline has been segmented and each parameter scored the likelihood of a threat per segment is calculated as follows.

Calculation on basis of:- Factors that tend to cause the threat

↑- Factors that tend to resist the threat

↓- Results of previous assessments

- Any previous assessments conducted- History of leak and/or rupture

max 1

max 1max 0.4

Proximity; 10%

Crossings; 10%

Land use; 20%

Encroach./One Call; 5%Mechanical protection; 25%

Surveillance; 20%

WallThickness; 10%

External damage

Lik

elih

oo

d o

f ex

tern

al c

orr

osi

on

(%

)

Distance along pipeline (m)

Determining Integrity thresholds

The calculation results for the whole grid need to be classified

Analysis performed by Prof. J. Van Dyck (University of Leuven)

The approach uses 4 levels from Extreme, High, Medium to Low

The number of E, H, M and L categories per integrity threat is function on basis of their relative importance in the incident statistics


The number of categories chosen is function of the available resources for integrity management.

Expected frequency over 10 years is derived from EGIG data

Thresholds for mechanical damage


• Classification in 4 types of categories (E, H, M and L) approaching target numbers as well as possible.

• Categories primarily suggested by histogram shape

• Same approach used for the other threats

Distribution of the likelihood scores for external damage (LMEC) for all segments in the ISAT database


Consequence of a threat

The impact on human life has the most significant weight. The affected area considered due to a leak/ rupture → PIPESAFE.

- Life safety criteria:> worst case scenario → jet fire → threshold for heat radiation.

- Environmental criteria:> same scenario → thermal dose → piloted House Burning Distance

Consequences of a leak/ rupture are independent of the type of hazard.- the consequence score of a segment can be characterized by a single number.

> The so called z-score gives how many standard deviations the consequence score for a specific hazard and segment is distant from the overall average (=0)

Ranking of pipelines

On basis of the results on individual segments an approach was worked out to rank pipelines

This ranking system is based on:- The relative length of the segment- The height of the scoring achieved per hazard of the segment.

> Extreme scores have higher impact than High and so on

- The number of segments with high scores within the pipeline- The consequences in case of leak or rupture in combination with the previous.

The highest ranking are than assessed individually, some practical examples are given in the next slides.


Pearson shows coating defect = dig up

Dents (orthophoto)

Case of high likelihood of mechanical damage

E-scores as a result of presence of dents (dected by ILI) or possible unsufficient depth of cover.


Depth of cover (orthophoto)

Case of high likelihood of external corrosion

H scores for external corrosion

Presence of trains, industry, water and isolating canal barriers.

Request to perform intensive OFF-potential measurements.


Typical results of a year PIMS global analysis


During 2013 about 800 km (20% of the grid) was assessed.

40 reports were edited and 103 preventive measures have been defined.

An action plan was drawn up and is being monitored by a working group.

32%

6%

9%26%

1%

26%

Preventive Measures

Check depth of cover

Extra Markers

CP measurements

Coating check

Map adjustment

None

Measures Check depth of cover 33Extra Markers 6CP measurements 9Coating check 27Map adjustment 1None 27

Conclusions

Pipeline risks and material defects are managed via holistic approach via a PIMS.

Different integrity monitoring programs are in place to manage the typical hazards for pipelines.

Due to their pipeline resistance some large diameter lines in sensitive areas are monitored by a shock detection system.

ILI allows to assess different anomalies, but correct characterization and reporting is a point of attention.

Collecting all PIMS data and using adequate software allows to extend the integrity analysis beyond the assessment of reported features by ILI.


Thank you for your attention!


Loenhout Weelde

Genk

Berneau

Sinsin

Jumet

Jemappes

Brussels

AnderlechtZuun

Wetteren

Zelzate

Zeebrugge

IZTF

Brugge

Winksele

Merksem

Grâce-Hollogne

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Managing Pipeline Risks and Material defects through PIMS