Predictive Maintenance in Oil and Gas – Current Applications

The International Energy Agency’s latest annual gas market report, Gas 2018, estimated that global gas demand could reach more than 4,100 billion cubic meters (bcm) in 2023. This is an increase from 3,740 bcm in 2017. Greater gas demands mean more oil rigs, and the machines on these rigs break down.

AI could help oil and gas companies predict when their machines and equipment require maintenance. Oil and gas companies can then repair these machines before their breakdowns result in long downtimes or employee injuries that could cost millions in legal fees and settlements.

The companies in this report all claim to help oil and gas, energy, and utility companies with at least one of the following:

  • Monitoring their machine assets
  • Predicting the probability of future machine failures
  • Making proactive maintenance decisions
  • And as a result, reducing operational costs arising from catastrophic machine failures

We begin our analysis of how energy companies can use AI to predict when their machines will break down with Uptake Technologies.

Uptake Technologies

Uptake Technologies offers its Asset Performance Management (APM) application, which it claims can help oil and gas companies monitor their machine assets, predict future machine failures, and make proactive maintenance decisions using machine learning.

Uptake claims that the APM is driven by the Asset Strategy Library (ASL), a dataset containing data about machinery and equipment types, their failure mechanisms, as well as fluids and inspection data, fault codes, and operating thresholds.

The company states the machine learning model behind the software was trained on more than 800 asset types used in the energy, chemical, manufacturing, and mining industries, 10 million components, and the 58,000 ways they can fail.

The application can be applied on the edge and in the cloud. The company states that oil and gas experts at the client company would need to determine where to install sensors on the cylinder.

These sensors would then collect telemetric data from those parts of the cylinder, such as pressure. This data would then be used as a baseline for a properly functioning cylinder.

The machine learning model behind the software would need to be trained on millions of these telemetric data points and data about when certain parts of the cylinder required maintenance, how long maintenance on those parts took, and possibly how long replacement parts took to arrive on site. The data would then be run through the software’s machine learning algorithm.

This would train the algorithm to discern which of all these data points correlate to properly functioning cylinder parts, the time at which the cylinder has needed maintenance in the past, and which of its parts needed repair.

The software would then be able to predict when certain parts of their cylinder are due for maintenance before they break down.

We could not find a demonstration video of this company’s software.

Uptake claims to have helped MidAmerican Energy Company increase its wind turbine availability. Within 48 hours after Uptake was deployed at the wind farm, the application found signs of failure in a main bearing in Tower 17, signs that were similar to previous conditions that lead to a gearbox malfunction.

Uptake alerted the client’s engineering and asset management team. Upon a physical inspection of the tower, the asset management team discovered the issue as predicted by the application.

This early detection allowed the team to fix the wind turbine at a cost of $5,000 and little downtime, saving MidAmerican $250,000 it would have spent if the gearbox had broken down.

MidAmerican also reports that within about three months of using Uptake’s software, the client had generated high-value information from 10% of its turbines.

Uptake Technologies also lists Caterpillar, Blanchard Cat, Ohio Cat, Magnetrol, BHE Renewables, and the US Army as some of its past clients.

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