By Ripal Vyas, Founder and President, Softweb Solutions
The arrival of new technologies like artificial intelligence (AI) and machine learning (ML) is transforming the way industries have been operated from decades. These technologies are bringing revolutionary transformations that are impacting industries at large. The oil and gas industry faces a lot of challenges in its operational processes like unconnected environment, frequent machine downtime and maintenance issues for its truck engines, etc. In comparison to other sectors, O&G has invested less in incorporating AI/ML technologies in its processes. But since these technologies are helping companies to cut on operational costs and enhance efficiencies, the oil and gas industry is now ready to adopt these technologies.
Oil and gas companies get a huge amount of data from sensors and machines, but they are not able to take full advantage of it. AI application in oil and gas analyzes the historical data and provides more accurate predictions for future outcomes.
Here are some of the ways in which AI/ML can enhance the operational processes and drive business values:
Connected worker
You can stay connected with your workers through RFID tags and smart safety helmets. Let’s go through them quickly.
RFID tags
RFID tags help you to track the location of workers and get real-time triggers if a worker enters, crosses a restricted area or a designated location. You can customize it as per your factory rules and time to time requirements. You can also assign a work area and create rules for a set of employees and get alerts for the same.
Smart safety helmet (SSH)
Smart safety helmets are designed to reduce the risk of injury and increase worker safety. It can track the head gestures and brain activity of workers to recognize anomalous behavior. The sensors incorporated in the SSH collect data that can be used for computing risk of an accident in real-time. SSH includes hardware components like a helmet displacement sensor, collision sensor, air quality sensor, data processing unit, wireless transmission and an alternating unit. An alert is sent to the operator to turn off the specific equipment or process when computed risk level for fatigue, stress or errors reaches a threshold.
Truck engine maintenance
Truck engines in oil sands capture a huge amount of data for parameters like vibration, temperature, pressure and throughput. But the data mostly remains unused. With IoT and AI-enabled solutions, you can easily utilize this data to derive useful insights. These insights help you to enhance engine performance, reduce costs and implement effective preventative maintenance processes.
AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period.
– MarketsandMarkets
Capital planning
Several companies in this sector have merged because of the fluctuations in prices of oil and gas and various other reasons. Large O&G companies acquire small companies like:
-
Kinder Morgan, one of the largest energy infrastructure companies in North America, acquired El Paso Corporation, a provider of natural gas and related energy products in North America. -
Royal Dutch Shell PLC, commonly known as Shell, a British-Dutch oil and gas company acquired BG Group, a British multinational oil and gas company.
The executives of the acquiring companies evaluate portfolios of the companies to be acquired, for making critical sale and purchase decisions. AI and ML implementation provides the opportunities to assess the historical performance and key metrics of these companies, examine loopholes and opportunities and give useful suggestions for making such decisions.
Subsurface – Well data analysis and management
Management of oil wells requires integration of various disciplines like:
-
Reservoir engineering -
Geology -
Production technology -
Petro physics -
Operations -
Seismic interpretation
AI implementation in the oil and gas industry helps to create tools that enable asset teams to get a profound understanding of asset performance and examine opportunities to enhance operational efficiencies.
No doubt, AI systems support the concepts and implementations of digital oil field (DOF). There is still immense potential to find new ways for optimizing field development and production costs.
Chevron Corporation is currently using AI-based software to analyze its historical well performance data. This helps the company to drill in better locations and raise its production by 30%.
Environment, health and safety (EHS)
Safety is one of the most crucial aspects of the success of everyday operations in O&G companies. Workers in the O&G refineries are generally exposed to agents like toxic and corrosive substances, manual handling activities, isolated sites, extreme temperature, etc. that cause health hazards.
Immense data is captured to understand the reasons for occurrence of safety issues, the root cause behind it and to figure out ways to prevent them in future. AI and ML technologies have the potential to assess this data and learn from it over time. These technologies derive useful insights which help you in proactive and preventative decision-making.
NASA uses AI principles to create algorithms that analyze data from the industry to find issues. This helps NASA to prevent accidents before they occur.
Conclusion
The oil and gas industry is now aware of the potentials of AI. Early adopters are taking advantage by protecting their assets and employees and staying ahead of the competition. There is no doubt that the potential of AI is going to bring real changes in companies’ overall business strategies. And as the competition builds up in the industry, companies will have todeploy AI in oil and gas business operationsso that they are not left behind. Hence, let’s recall the saying ‘the sooner the better’ and seize the opportunities that these technologies hold for business advancement.
About the Author
Ripal Vyas is the Founder and President of Softweb Solutions Inc – An Avnet Company. He has been instrumental in bringing the latest technologies to the Midwest over the last 12 years with his firm in Chicago. Vyas is now raising awareness on the importance of IoT, deep learning, AI, advance data analytics, and digital experiences across the U.S. via his new base in Dallas.
Check out our free e-newsletters
to read more great articles.
By Ripal Vyas, Founder and President, Softweb Solutions
The arrival of new technologies like artificial intelligence (AI) and machine learning (ML) is transforming the way industries have been operated from decades. These technologies are bringing revolutionary transformations that are impacting industries at large. The oil and gas industry faces a lot of challenges in its operational processes like unconnected environment, frequent machine downtime and maintenance issues for its truck engines, etc. In comparison to other sectors, O&G has invested less in incorporating AI/ML technologies in its processes. But since these technologies are helping companies to cut on operational costs and enhance efficiencies, the oil and gas industry is now ready to adopt these technologies.
Oil and gas companies get a huge amount of data from sensors and machines, but they are not able to take full advantage of it. AI application in oil and gas analyzes the historical data and provides more accurate predictions for future outcomes.
Here are some of the ways in which AI/ML can enhance the operational processes and drive business values:
Connected worker
You can stay connected with your workers through RFID tags and smart safety helmets. Let’s go through them quickly.
RFID tags
RFID tags help you to track the location of workers and get real-time triggers if a worker enters, crosses a restricted area or a designated location. You can customize it as per your factory rules and time to time requirements. You can also assign a work area and create rules for a set of employees and get alerts for the same.
Smart safety helmet (SSH)
Smart safety helmets are designed to reduce the risk of injury and increase worker safety. It can track the head gestures and brain activity of workers to recognize anomalous behavior. The sensors incorporated in the SSH collect data that can be used for computing risk of an accident in real-time. SSH includes hardware components like a helmet displacement sensor, collision sensor, air quality sensor, data processing unit, wireless transmission and an alternating unit. An alert is sent to the operator to turn off the specific equipment or process when computed risk level for fatigue, stress or errors reaches a threshold.
Truck engine maintenance
Truck engines in oil sands capture a huge amount of data for parameters like vibration, temperature, pressure and throughput. But the data mostly remains unused. With IoT and AI-enabled solutions, you can easily utilize this data to derive useful insights. These insights help you to enhance engine performance, reduce costs and implement effective preventative maintenance processes.
AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period.
– MarketsandMarkets
Capital planning
Several companies in this sector have merged because of the fluctuations in prices of oil and gas and various other reasons. Large O&G companies acquire small companies like:
-
Kinder Morgan, one of the largest energy infrastructure companies in North America, acquired El Paso Corporation, a provider of natural gas and related energy products in North America. -
Royal Dutch Shell PLC, commonly known as Shell, a British-Dutch oil and gas company acquired BG Group, a British multinational oil and gas company.
The executives of the acquiring companies evaluate portfolios of the companies to be acquired, for making critical sale and purchase decisions. AI and ML implementation provides the opportunities to assess the historical performance and key metrics of these companies, examine loopholes and opportunities and give useful suggestions for making such decisions.
Subsurface – Well data analysis and management
Management of oil wells requires integration of various disciplines like:
-
Reservoir engineering -
Geology -
Production technology -
Petro physics -
Operations -
Seismic interpretation
AI implementation in the oil and gas industry helps to create tools that enable asset teams to get a profound understanding of asset performance and examine opportunities to enhance operational efficiencies.
No doubt, AI systems support the concepts and implementations of digital oil field (DOF). There is still immense potential to find new ways for optimizing field development and production costs.
Chevron Corporation is currently using AI-based software to analyze its historical well performance data. This helps the company to drill in better locations and raise its production by 30%.
Environment, health and safety (EHS)
Safety is one of the most crucial aspects of the success of everyday operations in O&G companies. Workers in the O&G refineries are generally exposed to agents like toxic and corrosive substances, manual handling activities, isolated sites, extreme temperature, etc. that cause health hazards.
Immense data is captured to understand the reasons for occurrence of safety issues, the root cause behind it and to figure out ways to prevent them in future. AI and ML technologies have the potential to assess this data and learn from it over time. These technologies derive useful insights which help you in proactive and preventative decision-making.
NASA uses AI principles to create algorithms that analyze data from the industry to find issues. This helps NASA to prevent accidents before they occur.
Conclusion
The oil and gas industry is now aware of the potentials of AI. Early adopters are taking advantage by protecting their assets and employees and staying ahead of the competition. There is no doubt that the potential of AI is going to bring real changes in companies’ overall business strategies. And as the competition builds up in the industry, companies will have todeploy AI in oil and gas business operationsso that they are not left behind. Hence, let’s recall the saying ‘the sooner the better’ and seize the opportunities that these technologies hold for business advancement.
About the Author
Ripal Vyas is the Founder and President of Softweb Solutions Inc – An Avnet Company. He has been instrumental in bringing the latest technologies to the Midwest over the last 12 years with his firm in Chicago. Vyas is now raising awareness on the importance of IoT, deep learning, AI, advance data analytics, and digital experiences across the U.S. via his new base in Dallas.
Check out our free e-newsletters
to read more great articles.
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