This special three-part series of webinars explores best practice in resilient Pipeline Integrity Management systems, developed working with, and seeing the proven performance of, operators worldwide.

  • June 16, 2020 - Part 1: Best Practice & COVID-19 – A time to reflect 
  • July 09, 2020 -  Part 2: PIMS in a time of crisis – Act in haste and repent at leisure 
  • July 16, 2020 -  Part 3: An integrated approach to crack management

This special six-part series of webinars explores digitally-enabled decision support in challenging times and how you can connect and collaborate with your consultant in the digital era.

Hosted by Florian Brüggemann

  • May 21, 2020 -  Part 1: Transforming Data Into Action
  • May 28, 2020 -  Part 2: Offshore Integrity
  • June 11, 2020 - Part 3: Inspect Now or Later?
  • June 18, 2020 - Part 4: In Times When We All Do Risk Assessments
  • June 25, 2020 - Part 5: Above-Ground Surveys
  • July 02, 2020  - Part 6: SCC Susceptibility 

In this course, you will find a series of presentations and reading materials that will introduce the concept of data science, explain how data is organised, and provide an overview of different analytical methods that can be used.

Presentations: Introduction to Data Science, Data Engineering, Descriptive Analytics, Predictive Analytics: Regression with Supervised Machine Learning 
Reading: The Bias Blind Spot, Thomas Bayes and Confirmation Bias, The Illusion of Causality

The oil and gas industry is experiencing unprecedented market forces, including the first 'negative' price for a barrel of oil, the decrease in energy demand and the oil and gas majors cutting costs. Every company and its workers in this industry will be affected both in the short and long term. How is the industry surviving in these difficult times and how do key players see the future? 

This webinar, hosted by Dr. Phil Hopkins, focuses on the pipeline business and asks pipeline operators and experts from around the world how they are managing their assets during the current crisis and how they see the future. The questions posed gives an insight into what the operators' priorities are, as well as the measures they are taking to ensure supply and security.

Presenters: Phil Hopkins, PHL; Terri Steeves, TCE; Marcelino Gomes, Petrobras; Tom Miesner, Pipeline Knowledge LLC; Polad Rustamov, TANAP; Jeroen Zanting, Gasunie 

While the world is turned upside down by the pandemic, oil and gas leaders must future-proof their companies simultaneously for unprecedented disruption and rising social risk.

Social risk is the combined political, policy and community risk that materially threaten construction and operation of projects. This keynote articulates how the pandemic is mobilizing new trends in disruption, accelerating some threats, and delaying other social risks. These changes are fundamentally reshaping the political and financial landscape for oil and gas companies. Rather than dismiss or fight these trends, Tisha Schuller challenges energy leaders to seize the opportunity presented by our current disruption and increasing opposition to oil and gas, turning risk into operational advantage.

Presenter: Tisha Schuller, Adamantine Energy

Мы живем в постоянно меняющихся условиях. Глобальные изменения ежедневно погружают нас в незнакомую обстановку, и мы опять должны искать решения для новых задач. Так как нам всем нужно адаптироваться к новой ситуации, мы считаем, что сейчас более чем когда-либо обмен знаниями приобрел решающее значение.

Поэтому в этой серии вебинаров наши эксперты рассмотрят различные темы, имеющие отношение к вопросам управления целостностью производственных объектов. Целью наших вебинаров является поддержка обеспечения непрерывной безопасной эксплуатации производственных объектов и помощь Операторам в преодолении сложившихся нестандартных обстоятельств.


Michael Smith
Introducción a la ciencia de datos • Ingeniería de datos • Analítica descriptiva • Analítica predictiva

Michael Smith
Introdução a ciência dos dados • 
Engenharia de dados • Análise descritiva • Análise preditiva