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Using Completion Reliability For Improved Sand Control Selections DEVEX , 10 May 2012 Christian Capderou

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Sand Control Completion Selection

Short list of alternatives

Expected Productivity

Deviation in reservoir

Rock Mechanics

Sand Retention Criteria

Fines Migration

Reliability

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Why is sand face completion reliability important ?

Higher Well Uptime

Lower Work Over Frequency

Well Integrity

Higher Cumulative Production

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History of Reliability Engineering applied to well completions

1950 : Reliability engineering emerges from the US Air Force to improve reliability of electronic equipment.

1990 : Molnes publishes his work on SCSSV Failure Mode and reliability

1991-1993 : Patterson and Upchurch use exponential distribution for ESP run life prediction

1993 : Molnes introduces exponential distribution and constant failure rate for upper completion components and systems

1997 : Oliveira proves that weibull provides a better fit for a particular ESP data set.

2003 : G. King publishes the first sand control reliability data base base on exponential distribution

2004 : Frota established that Weibull distributions are suitable for reliability analysis of sand control completions.

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The bath tub curve

Wear Out Infant Failures

t

Haz

ard

rate

h(t)

Useable Life

Constant Failure Rate

Increasing Failure Rate

Decreasing Failure Rate

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Analysis steps

Data Mining • Definition of

failure • Full Field • Sand control

related only

Ranking • Right Censored

Data • Reverse Ranking

(1/k)

Life Data Analysis • h(t) = f(t)/R(t) • R(t) = exp(-H(t)) • Curve Fitting

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Data mining

Scope – Open Hole Gravel Pack

– Expandable Sand Screen

– Cased Hole Oriented Perforating ( requires external data)

Key information used

Failure defined as either sand control failure or production curtailment due to sand production.

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Open Hole Gravel Pack Extended Data Set

0%

2%

4%

6%

8%

10%

12%

0%

25%

50%

75%

100%

0 2 4 6 8 10 12

Haza

rd ra

te , h

(t)

Relia

bilit

y R(t)

Life t, yearR(t) Weibull 2P Exponential h(t) Poly. (h(t))

Internal Data Set – 147wells – 721 well .years

– 31 failures – Platform /Subsea wells only – Both oil and gas producers

Distribution Exponential

MTTF years 23

Failure rate (λ) 0.043

Distribution Weibull 2 P

Scale Factor (years) 44

Shape Factor 0.65

MTTF Years 60

0.97

1.14

Weibull 2P Exponential

Correlation coefficient

26.86

27.53

Weibull 2P Exponential

Least Square Difference

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Expandable Sand Screens Life Data Analysis

Internal Data Set (partial) – 22 wells – 62 well .years – 6 failures – Platform wells only – Both oil and gas producers

Distribution Exponential

MTTF years 10

Failure rate (λ) 0.1

Distribution Weibull All

Scale Factor (years) 5.7

Shape Factor 1.67

MTTF (years) 6.1

0%

25%

50%

75%

100%

0%10%20%30%40%50%60%70%80%90%

100%

0 1 2 3 4 5 6 7

Harz

ard

Rate

h(t

)

Relia

bilit

y R(

t)

time t, years

Weibull 3P All R(t) Exponential h(t)

0.94

0.64

Weibull 3P All Exponential

Correlation Coefficient

0.02

0.11

Weibull 3P All Exponential

Least Square Difference

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Comparison to published data

Expandable screens Chevron SPE 84262 Failure Rate 1/year 0.097 0.016* MTTF exponential (years)

10.3 62.5*

MTTF Weibull (years) 6.1 n/a

OHGP Chevron SPE 84262 Failure Rate 1/year 0.043 0.020* MTTF Exponential (years)

23 50*

MTTF Weibull (years) 60 n/a

*Infant failures are excluded from the figures quoted in SPE 84262

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Conclusions

The use of Life Data Analysis techniques provides a fact based assessment of completion reliability

The use of the Weibull distribution allows to account for infant failure.

In the fields evaluated OHGP appears to be significantly more reliable than ESS.

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Q & A

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Sand Face Completion Reliability

From SPE 101511 (2004)

Sand Face Reliability can result in heated debates over the validity of the data and analysis….

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SPE 84262 Results