Pipeline Integrity Monitoring and Realtime Analytics

Intelligent Monitoring Safety solutions

The industrial growth that began in the United States in the early 1800’s continued even after the civil war. However, after that, American industry changed dramatically. Machines took over most of the human labor which resulted in fast production and increased competition. To keep up with the rapidly growing demands of the industry advanced machineries were brought in, which resulted in increased fuel consumption. Also, with time increased the population of the country which raised fuel demands. Now,to cover wide geographic area of a country like US some efficient transmission technique was needed. This was accomplished through metallic pipes.

A large proportion of America’s pipelines run next to factories, farmlands, schools, public areas and nearly two-thirds of Americans live within 600 fit of a pipeline carrying natural gas or crude oil. Factors such as nature of environment and extreme temperatures affect the state of these pipelines, resulting in corrosion. Pipeline integrity is about using intelligent and efficient tools using Machine Learning models and AI tools to detect and locate pipeline defects. Techniques such as Magnetic Flux Leakage signals and tools such as PIG (Pipeline Inspection Gauge) use AI to inspect pipelines for the purpose of preventing leaks, which can be explosive to the environment.

Data Science Methodology

There are various types of direct and indirect costs involved in pipeline spills.

  • Cost due to leakage.
  • Cost of adverse effects of external environment.
  • Hazards which can affect human lives

Case study

One such incident happened in Jan 2018, where a company applied Machine Learning and AI solutions to detect pipeline failures and ended up identifying a severe point of failure. The key point in this case study is that this pipeline ran through an almond grove where each tree is valued at more than $100,000. The Pipeline and Hazardous Materials Safety Administration (PHMSA) reported that significant pipeline incidents grew 26.8% from 2006 to 2015. These incidences also involved death, serious injury, property damage and fire explosion. Pipeline spills also result in some indirect costs such as millions of hours of manpower in efforts to insure pipeline integrity.

How SDS will help:

What improvements do Machine learning models bring to current pipeline integrity systems?

SDS methodology

  • We will study market scenario to understand existing methodologies
  • Our team of expert Data Scientists will develop a solution according to company’s data provided
  • We understand that the pipeline problem could be solved by dealing with continuous sensor data coming from remote site.
  • Our Data Science stack services will also be used to analyze the hidden patterns among real time signal data.
  • We will first do root cause analysis by fining correlation between various parameters and their effects on pipeline health.
  • Then the old data for non accidental situations will be used as a label ‘success’ and the accident instances will be labeled as ‘damage’
  • The problem will be divided into a binary classification problem to identify and avoid accidents well before time.

Are these Features for You?

  • Project Governance, Phases and Milestones with our Statement of Work
  • Full Service Consulting and Code Implementation for Executives
  • For IT Professionals and Leaders continuing the Data Science Methodology
  • Modeling customized to fit to your business data and cost value proposition

Machine Learning for the win!

Superior Data Science LLC

Ready to contact us?

1 (214) 518-9248