Introduction to Probability of Failure Analysis (CS044A)


You must first login to purchase this item.

Introduction to Probability of Failure Analysis


Th
is e-learn course provides you with an awareness of various pipelines which may lead to loss of containment and assessing the probability of failure from qualitative to quantitative.

This technical course has been authored by our industry expert and is designed to offer you with just-in-time knowledge related to pipeline integrity management. The courses can be studied as part of your learning journey, or used as a reference library.

To access this complimentary e-learn course you must create an account. Click here to begin.

Your course at a glance

Rosen-Calendar

Course Availability

Available to access online 24/7

Rosen-Clock

Duration

Estimated 60 minutes

Rosen-Laptop

Delivery

Study on-demand at your own pace

Rosen-Sealofapproval

Level

Awareness level

Rosen-Standards

Competency Standards

Satisfies the learning requirement for
 Probability Failure Analysis CS044A

Rosen-badge

What you will get

Completion E-badge, plus informal
Continued Professional Development hours




What you will learn


In this technical course you will gain insight into:

• Probability of Failure (PoF) assessment, including general lists of pipeline hazards, given in codes and standards and industry guidelines
• Pipelines segmentation due to pipeline design and different failure hazards for onshore and offshore pipelines
• Data requirements from probability of failure analysis
• Industry and operator historical failure statistics




Meet your Subject Matter Expert


Simon Slater

Ian Diggory

Ian Diggory is the Principal Consultant at ROSEN(UK). Over 40 years in the oil and gas industry, mainly working on risk models. Joined the industry as an acoustics consultant at British Gas’ Engineering Research Station, following 5 years research into environmental noise. Studied physics and mathematics at university, followed by postgraduate studies in nuclear structure and cosmic ray physics. Published papers on a range of topics from antiprotons in cosmic rays, to traffic noise through to machine learning.

  



Progress