Increasing Oil & Gas Production with Machine Learning & IoT

Increasing Oil & Gas Production

What is the best way for an oil & gas company to increase energy production?

A. More oil wells or pumping rigs?
B. More refineries?
C. More people & resources?
D. Better business intelligence?

If you answered D, you’re RIGHT!

While your organization may need to invest in more oil wells or more oilfield engineers, adding these resources adds a significant amount of ongoing expense. By contrast, business intelligence tools, like data mapping, machine learning and IoT (Internet of Things) sensors provide long-term added value with little upkeep.


“Internet of Things (IoT) in the Energy Market Worth
$22.34 Billion by 2020” - Systems & Systems

Oil & gas companies are investing in business intelligence solutions, particularly IoT sensors, at record rates. This exploding growth of IoT in the energy sector can be attributed to factors such as:

  • How inexpensive IoT sensors are, particularly when compared to the industrial oil & gas equipment being monitored
  • How difficult it can be to visualize operations in oilfields and refineries
  • The abundance of options for IoT sensor use throughout the energy industry supply chain – and how much value they can add

Data Warehousing for Oil & Gas

Many oil & gas companies still rely on after-the-fact business analytics. Information is collected from multiple business systems. Through data mapping and data modeling, the information is then stored in a data warehouse that provides a “single source of truth” across the organization.

The information in a data warehouse is designed to be highly accurate, and therefore, highly valuable. The only downside is that – unless you add additional business intelligence tools and steps – the information is always delivered after the fact.

While many oil & gas companies have improved insight by getting reports at the end of the day rather than end of the month, there’s still a costly lag time in traditional reporting.

There is a better way…

Adding Machine Learning & IoT Sensors for Real-Time Business Intelligence and Increased Production: 5 Business Use Cases

To extract and refine more natural resources per day, you want to be able to visualize and react in real-time to events happening in the oilfield, refinery, warehouse or elsewhere in supply chain.

We’ve seen companies use IoT data sent through Azure to a data lake to:

  1. Monitor wellhead & plant production volumes. Companies are starting to see real-time what’s happening in the fields, plants and refineries and create strategies to maximize production volume and improve forecasts.

  2. React to production bottlenecks. In an energy plant, business intelligence dashboards can provide red/yellow/green indicators that correlate with flow efficiency. Engineers are able to quickly find the source of the problem, whether it’s an absence of raw material or an equipment jam.

  3. Schedule maintenance. IoT sensors are designed to catch the early indicators of equipment failure – like excessive heat. The sooner you can get a truck on site with a fix, the less downtime you’ll have.

  4. Calibrate flow. When you’re moving from energy exploration to midstream operations, you need the right amount of natural resources flowing into pipelines and trucks to be processed efficiently. Machine learning can predict how much will be produced and recommend the right valve settings to optimize the flow.

  5. Improve resource allocation. With better real-time insight, companies can make better decisions about oilfield rental equipment, staffing requirements, maintenance schedules, spare inventory part thresholds, and supply chain visibility.

What do you want to know?

MCA Connect can help you find actionable insight and latent production opportunities. We have extensive experience in the energy industry such as oil exploration & production, oilfield services and midstream operations. When combined with our knowledge of technology, you can’t find a better partner for optimizing energy operations. Contact us for more information. 

Author: Mark Hatting, Analytics Practice Director

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