MathWorks > Case Studies > Predictive Maintenance Software for Gas and Oil Extraction Equipment

Predictive Maintenance Software for Gas and Oil Extraction Equipment

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 Predictive Maintenance Software for Gas and Oil Extraction Equipment - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Enterprise Asset Management Systems (EAM)
  • Infrastructure as a Service (IaaS)
Applicable Industries
  • Oil & Gas
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Customer
About The Customer
In periods of peak demand, Baker Hughes crews work around the clock to tap oil and natural gas reservoirs. At a single well site, as many as 20 trucks may operate simultaneously, with positive displacement pumps injecting a mixture of water and sand at hi
The Challenge

If a truck at an active site has a pump failure, Baker Hughes must immediately replace the truck to ensure continuous operation. Sending spare trucks to each site costs the company tens of millions of dollars in revenue that those trucks could generate if they were in active use at another site. The inability to accurately predict when valves and pumps will require maintenance underpins other costs. Too-frequent maintenance wastes effort and results in parts being replaced when they are still usable, while too-infrequent maintenance risks damaging pumps beyond repair.

The Solution

Use MATLAB to analyze nearly one terabyte of data and create a neural network that can predict machine failures before they occur

Operational Impact
  • [Management Effectiveness - Data Analysis]

    Multiple types of data easily accessed.

Quantitative Benefit
  • Savings of more than $10 million projected.

  • Development time reduced tenfold. 

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