How do we figure out when to stop digging and when to run the

next metal loss in-line inspection.

Using statistical methods to help quantify “DONE”

R. Turley - MAPL

6/1/00 2

Let’s start with a small dose of reality • We DON’T normally excavate everything an in-line

inspection tool identifies (we leave stuff).• We typically excavate only 5-10% of the metal loss

indications an ILI tool identifies.• If you can excavate all anomalies the tool identifies,

consider yourself lucky but maybe not as smart as you think.

• Everyone defines “DONE” differently• You need different tools with older, pre-CP systems

(195,000 anomalies in 110 miles is a lot)

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Distribution of Predicted Metal Loss

8-inch PipelinePreliminary ILI Results

1512 External Anomalies Identified

638

505

258

989 1 0 0

0

100200

300400

500600

700

<20% 20 -29%

30 -39%

40 -49%

50 -59%

70 -79%

>80% 60 -69%

% Wall Loss

Num

ber o

f A

nom

alie

s

External Metal LossInternal Metal Loss

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The Challenge

• Can we define “done” in a consistent manner so that everyone understands “where” we stopped on a particular line and the level of risk we accept when we do stop?

• Can we find a way to quantify our level of “comfort” regarding what we didn’t dig up?

• Can we come up with something more justified than a “one-size fits-all” interval?

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What information does a metal loss in-line inspection tool provide?

• Predicted length of the anomaly• Predicted depth of the anomaly

– from this information we can calculate the following for each anomaly:• a Predicted Burst Pressure (Pburst)• a Calculated Allowable Operating Pressure

(CAOP)• We can then look at the number of excavations it will

take to reach certain criteria.

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Calculated Allowable Operating Pressure

(psiPredicted

RPR1172 1.05 78% 57% 79%1172 1.02 78% 54% 57%1172 1.01 77% 51% 56%1172 1.05 77% 50% 55%1172 1.02 76% 47% 52%1172 1.02 75% 44% 51%1172 1.02 74% 41% 51%1172 1.00 74% 41% 51%1172 1.03 73% 41% 50%1172 1.05 72% 40% 49%1172 1.00 71% 38% 49%1172 1.02 71% 36% 49%1172 1.05 70% 35% 48%1172 1.05 70% 34% 48%1172 1.03 70% 34% 48%1172 1.04 68% 34% 48%1172 1.05 68% 33% 48%1172 1.00 68% 33% 48%1172 1.03 67% 33% 48%1172 1.04 67% 32% 47%1172 1.05 67% 32% 47%1172 1.00 67% 32% 47%1172 1.04 66% 31% 47%

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Predicted Burst Pressures / CAOP versus

Potential Excavations

500

700

900

1100

1300

1500

1700

1900

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Excavations

Pres

sure

(psi

) Predicted CAOP

Predicted Burst Pressure

100% SMYS

72% SMYS (MAOP)

MOP

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The Dilemma

• In the past, we picked a criteria and dug to it. Refer to previous graphs.

• BUT, an in-line inspection tool isn’t perfect.– Typical stated tolerance (for ERW pipe):

• +/- 10%, 80% of the time• +/- 15%, 95% of the time• It’s worse for Seamless (+/- 20%, 80% of the time)

• So, the question becomes, “if the tool isn’t perfect, how confident are we that we didn’t leave something behind that is a problem.

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Depth Unity GraphEXAMPLE : NOT SO ACCURATE IN-LINE INSPECTION

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Actual Depth (mils)

Calle

d De

pth

(mils

)

Undercalled

Overcalled

Criteria Lines are set to 15% of wall. If change is necessary, click button to enter a new percent

Change

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CAOP Unity GraphEXAMPLE : IN-LINE INSPECTIONS PREDICTIONS TEND TO BE CONSERVATIVE

600

800

1000

1200

1400

1600

1800

600 700 800 900 1000 1100 1200 1300 1400 1500

Actual CAOP (PSI)

Pred

icte

d CA

OP

(psi

)

Undercalled

OvercalledCriteria Lines are set to 15% of wall. If change is necessary, click button to enter a new percent

Change

6/1/00 11

Using statistics to assist in our decision making

• Based on either the tool vendors stated accuracy or our excavation data, we can develop statistical relationships to provide a quantitative way to measure our confidence in a pig’s predicted value.

• The “tool” we utilize is a technique known as “Probability of Exceedance Analsysis” or “POE”.

• Working on the utilization of this technique for almost three years.

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Probability of Exceedance Analysis

• Is just a different look at the same data (we just are trying to allow for the tool’s tolerances).

• Allows the prioritization of anomalies or groups of anomalies with the greatest probability of causing a release by either a rupture or a leak.

• Allows the pipeline mileage to be prioritized by likelihood of a rupture/leak

• Demonstrates the impact of a dig program to reduce the likelihood of a corrosion release via either a rupture or a leak.

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Probability of Exceedance Analysis

• Helps with designing a multiyear dig program and planning reinspection intervals

• Allows the potential for adding consequence information and calculating “risk of a leak/rupture due to metal loss.

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How do we take into account a tool's inaccuracy level?We statistically model the in-line inspection predicted values.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

0 10 20 30 40 50 60 70 80 90 100

Percent Depth

Perc

enta

ge o

f the

Pop

ulat

ion

that

will

Occ

ur a

t Eac

h De

pth

This graph shows the relationship between a predicted in-line inspection data point and what we might find in the field. We use vendor specifications or our correlation digs to develop these graphs for each anomaly.

So what? How can you use this info?

The probability that an in-line inspection call of 50% is really 70% or deeper is numerically equal to the area under the curve to the right of 70%.

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As our correlation activities result in different statistical correlations, the "tightness" of the distribution changes

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

0 10 20 30 40 50 60 70 80 90 100

Accurate correlation (15%) Reasonable correlation (20%) Not-so-good correlation (25%)

Therefore depending on the correlation, the chance that you exceed a certain value (area under the curve) changes.

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Ok, what does it all mean?• Now, for each anomaly, we can calculate the potential for

the actual value of an un-excavated anomaly to “exceed” a threshold that we identify.

• The thresholds we are typically interested in are:– the anomaly is actually deeper than 80% (these

anomalies, if they failed, would fail as a leak)– the anomaly has a predicted burst pressure less than the

abnormal operating pressure (these anomalies, if they failed, would fail as a rupture)

– the anomaly has a CAOP less than MOP

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For a given in-line inspection project, we can graphically portray the relationship between Depth and the Probability of Something not meeting our threshold (in this case 80% of depth)

1.00E-12

1.00E-11

1.00E-10

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+000 10 20 30 40 50 60 70 80 90 100

Percent Depth

Prob

abili

ty o

f an

Anom

aly

Exce

edin

g 80

% (D

epth

PO

E)

+/- 20% tool accuracy

The Probability that a called X% anomaly will in fact be an anomaly deeper than 80% of depth is what this graph shows.

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Yeah, so what?

• Now, we can quantify the chance or probability that what we didn’t dig could actually be “un-acceptable”

• We can correlate our “gut-based” criteria of the past with a quantitative value.

• Note, it isn’t truly the chance we are going to have a leak or a rupture, just that our designated threshold is exceeded. It’s the chance the plane has a missing bolt, not that the missing bolt will bring down the plane.

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1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+000 50 100 150 200 250 300 350

Number of Excavations

Prob

abili

ty o

f a m

etal

loss

ano

mal

y ha

ving

an

actu

al B

urst

Pr

essu

re <

the

Abno

rmal

Ope

ratin

g P

ress

ure*

OR

a de

pth

80

% w

.t.

Pressure POE Depth POE

* See Maint-003 for definition

MAOP = 1172 psiHigh Pressure Shutdown Setpoints = 1050 psiDischarge Pressure Control Setpoints = 1000 psi

1x10E-1 Threshold: All anomalies with predicted depth 65% of the w.t.5x10E-2 Threshold: DEPTH TARGET for this

system (using actual correlation data). All anomalies with predicted depth > 60% of the w.t.

1x10E-2 Threshold: All anomalies with predicted depth 52% of the w.t.

1x10E-3 Threshold: All anomalies with predicted depth 42% of the w.t. All anomalies with predicted CAOP = MAOP = 1172 psi, RPR = 1.017x10E-4 Threshold: PRESSURE

TARGET for this system (using actual correlation data).

6/1/00 20

• We can now quantify “done” and communicate the relative likelihood of a problem being un-excavated.

• We can also treat the risk of a potential burst/rupture failure different than the risk of a potential leak and excavate to different criteria.

• We can show the relative reduction in the likelihood of a theoretical leak/rupture with additional excavations.

• We can also look at all of our pipeline systems at one time and utilize the information to rank them on a relative basis.

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1.00E-16

1.00E-15

1.00E-14

1.00E-13

1.00E-12

1.00E-11

1.00E-10

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+000 20 40 60 80 100 120 140 160 180 200

Number of Depth Probabilities Addressed

Dep

th P

roba

bilit

ies

Arrows Denote the Remaining Maximum Depth Probabilities

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1.00E-15

1.00E-14

1.00E-13

1.00E-12

1.00E-11

1.00E-10

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+000 20 40 60 80 100 120 140 160 180 200

Number of Pressure Anomalies Addressed

Pres

sure

Pro

babi

lity

Arrows Denote the Remaining Maximum Pressure Probabilities

6/1/00 23

Now what about re-inspection intervals?

• Once we can quantify where we stopped, for those anomalies that we leave un-excavated, we model the anomaly with a corrosion growth rate.

• When the anomaly grows (in the future) to a certain threshold that we deem in-appropriate (probability of a problem), it’s time to re-inspect.

• Just trying to determine broad band justification of the pigging intervals (3-5 yrs, 6-9 years, 10-15 years).

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Now the bad news

• This only applies to corrosion anomalies (not to 3rd party damage, appurtenances, dents, etc.).

• Need different criteria and “gut” reasoning to identify other types of anomalies meriting investigation.

6/1/00 25

But the good news is….

• All this work confirmed our “gut” feel.• We are doing a much better job of defining and,

more importantly, communicating “done”.