Probabilistic Database of Corrosion Rates Rate...Francois Ayello and Narasi Sridhar DNV GL. ... Survey shows links between parameters and degrees of confidence (slide 15-24) 29 Corrosion

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Probabilistic Database of Corrosion RatesTECHNOLOGY WEEK | 15 - 18 OCTOBER 2018

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Francois Ayello and Narasi Sridhar

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Corrosion data comes from various sources with

various degrees of reliability.

▪ Laboratory experiments

– performed within a specific set of conditions

– Performed for a purpose (e.g. study of the effect of…)

– some parameters are not reported

– anonymized published results difficult to use

▪ Field data

– real world complex conditions

– many parameters are not measured

▪ Corrosion models

– Empirical vs. mechanistic

– Do not have same list of inputs (next slide)

Corrosion Data

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Generating corrosion data from corrosion model is not easy

Corrosion Type - Bottom of the line corrosion - Bottom of the line corrosion - Bottom of the line corrosion - Bottom of the line corrosion - Bottom of the line corrosion

Steel Type - Low Carbon Steel - Low Carbon Steel - Low Carbon Steel - SS 304 - Low Carbon Steel

Flow Type - ? - Water Flow - Water Flow - complete agitation - Water Flow

Temperature ℃ ? ℃ 1 - 120 ℃ 1 - 120 - below dew point ℃ ?

Pressure Bar ? Bar 1 - 200 Bar 1 - 10000 - above dew point Bar ?

Internal Diameter m 0.0254 - 1 m 0.01 - 1 m pipe flow, multip flow m ?

Inclination deg -90 - 90

Pipe Roughness μm 0 - 1000000 μm multip flow

Pipe Thickness mm 0.5 - 100

Pipe Conductivity W/(m*K) 0.001 - 250

Input Type Velocity flow rate used for multiphase flow only

Velocity Input Type Mixture Input

Superficial Water Velocity m/s 0.000001 - 100 m/s 0.001 - 20 m/s pipe flow m/s ?

Water flow rate

Superficial Oil Velocity m/s 0.001 - 100

Oil Flow rate

Liquid flow rate m3/s multip flow

Superficial Gas Velocity m/s ? m/s 0.01 - 100

Water Cut % 0.1 - 99.9 % multip flow

Gas Flow Rate Sm^3/day 500 - 6000000 m3/s Standard or at input T&P ?multip flow

Mixture Flow Rate m^3/day 0.01 - 10000

Shear Stress Pa shear stress

Water Density kg/m^3 1 - 1500 calculated

Water Viscosity Pa. s 0.0005 - 1.5 calculated

Oil Density kg/m^3 1 - 1200 calculated

Oil Viscosity cP 0.001 - 1.5 cP multip flow

Interfacial Tension N/m 0.001 - 2

Gas Properties Input Input

Gas Density kg/m^3 0.1 - 600 calculated

Gas Viscosity Ps.S 0.000001 - 1 calculated

Gas-Liquid Surface Tension N/m 0.001 - 100

CO2 bar ? bar 0 - 215 bar 0 - 10000 mol 0 - inf bar ?

H2S bar 0 - 5 bar mol 0 - inf bar ?

N2 %Mol Calculate mol 0 - inf

HAc %Mol Calculate mol 0 - inf

H2O %Mol Calculate mol 0 - inf

CH4 bar 0 - 215 mol 0 - inf

C2H6 bar 0 - 215 mol 0 - inf

C3H8 bar 0 - 215 mol 0 - inf

C4H10 bar 0 - 215 mol 0 - inf

C5H12 bar 0 - 215 mol 0 - inf

C6H14 bar 0 - 215 mol 0 - inf

C7H16 bar 0 - 215 mol 0 - inf

C8H18 bar 0 - 215 mol 0 - inf

O2 mol 0 - inf

pH - ? - 2 - 9 - 3 - 7 calculated

O2 ppb(w) 0 - 10000 calculated

Total HAc+Ac ppm(w) 0 - 1000000 %Mol 0 - 1000 mol

Fe2+ ppm(w) 0 - 1000 ppm(w) 0 - 100 calculated mg/L ?

Na+ ppm(w) 0 - 1000000 mg/L ?

Ca2+ ppm(w) 0 - 1000000 mg/L ?

Ba2+ ppm(w) 0 - 1000000 mg/L ?

K+ ppm(w) 0 - 1000000 mg/L ?

Mg2+ ppm(w) 0 - 1000000 mg/L ?

Sr2+ ppm(w) 0 - 1000000 mg/L ?

Cl- ppm(w) 0 - 1000000 mg/L ?

SO42- ppm(w) 0 - 1000000 mg/L ?

Bicarbonate calculated mg/L ?

Acetate mg/L ?

Alkalinity MeOH %(w) ? M -0.01 - 20

MEG %(w) ?

Inhibitor type

% inhibition % 0 - 100

corrosion potential V (SHE) calculated

repassivation potential V (SHE) calculated

Scaling tendencies - calculated

Time hours 0.1 - 1.8E308 hours 24 only hours 0h

Passivation / Scaling

Time

Unit

CASSENDRA 93

Range

DeWaard & Milliams

Range

Input

Range Value RangeInput

Pipe Flow

Line Properties

Flow Properties

Water Properties

General Input

Inhibition

MULTICORP 5 FreeCorp 1 OLI 2018

Category Unit Unit Unit Unit

Oil Properties

Gas Properties

Composition

Aqueous Species

Gas Content

Types

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Why create corrosion rate database?

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Corrosion Database

Why make a database of corrosion rates?

1. Corrosion models are expensive and proprietary. We mostly use one model

2. Corrosion models are difficult to link to other softwares (e.g. flow, risk…)

3. Corrosion models are not the only source of corrosion database

The proposed corrosion rate database would aggregate information from

– Multiple corrosion models

– Laboratory experiments

– Literature

– Field data

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Corrosion Database Proof of Concept

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Visualisation – Histogramshows corrosion rate probability

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Corrosion Rate Distribution

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3D Visualisationshows relationship between parameters

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Corrosion Database Proof of Concept

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Why a probabilistic corrosion rate database?

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Database as a knowledge integration platform

Corrosion Models

Field Data

Laboratory experiments

Corrosion rates in the literature

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Probabilistic Database of Corrosion Rates

Why make a probabilistic database of corrosion rates?

1. Corrosion models do not agree (one input leads to multiple

results, histogram slide 9 to 13)

2. Field data do not record all input parameters all the time

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Database benefits

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How to use the data

1. Corrosion rate visualisation

(understanding where corrosion

rates are uncertain, slides 9-13)

2. Survey shows links between

parameters and degrees of

confidence (slide 15-24)

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Corrosion Rate

Corr

osio

n R

ate

Pro

bability

Condition A

Condition B

Temperature

Corr

osio

n R

ate

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How to use the data

3. Tailor corrosion model to specific input and operating conditions

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database Machine learning corrosion model

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How to use the data

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3. Tailor corrosion model to specific input and operating conditions

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How to use the database?

4. Inference, with enough data we can back calculate corrosion inputs.

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Conclusion

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Where are we?

▪ FreeCorp (10,000,000 corrosion simulations)

▪ MultiCorp

– license

– code to automate calculations

– run MultiCorp

▪ OLI

– license

– code to automate calculations

– run OLI

▪ Select other corrosion models (members decide)

▪ Software tools to automate literature search

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Automated Information Retrieval

▪ Text mining tools to parse databases of conference papers, reports and

peer-reviewed articles

▪ Automated image and table extraction

▪ Requires some manual analysis to determine context and

environmental/materials conditions

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Next Step: create a JIP to gather more corrosion data

Project Goal

This JIP aims to produce a corrosion rate database. This JIP will gather corrosion

rates from various sources such as corrosion models, field data, lab experiments

and published corrosion rates. Data will be anonymized. The data collected will

enable users to make informed decisions regarding operations, maintenance and

life extension of their assets.

How is this useful?

The database will help answer these questions:

1. When two corrosion models provide different rates, which one should be

trusted?

2. When corrosion rates from the field do not agree with predicted rates, what

additional information is needed?

3. How to predict corrosion rates when some data is missing or unavailable?

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For more information

Contact

Francois Ayello

Principal Engineer, Risk Management

DNV GL USA, Inc.

[email protected]

Direct 614-440-6056

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SAFER, SMARTER, GREENER

www.dnvgl.com

The trademarks DNV GL®, DNV®, the Horizon Graphic and Det Norske Veritas®

are the properties of companies in the Det Norske Veritas group. All rights reserved.

Probabilistic Database of Corrosion Rates

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Francois Ayello

[email protected]

Documents

Probabilistic Database of Corrosion Rates Rate...Francois Ayello and Narasi Sridhar DNV GL. ... Survey shows links between parameters and degrees of confidence (slide 15-24) 29 Corrosion