Harnessing AI for Operational Efficiency in the Energy Industry
Part 2: The Role of AI in Process Automation
In the energy industry, one of the most transformative technologies driving operational efficiency is artificial intelligence (AI). In Part 1 of this blog, we focused on the use of AI to drive prescriptive maintenance on equipment to help achieve operational efficiency. In Part 2, we‘re exploring process automation using AI and why it makes sense for companies in the energy industry to take it seriously, now.
In our AI research report, From Hype to Impact: How Enterprises Can Unlock Real Business Value with AI, the findings for the energy industry tell us that over 32% of respondents identified operational efficiency as a top priority of AI adoption, followed by productivity improvements (31%) and profit margin improvement (26%). These priorities reflect an industry grappling with costly legacy systems, volatile markets and processes that need to be streamlined while at the same time meeting sustainability goals.
Being able to run processes automatically is not a new concept in the energy industry — oil and gas companies have managed unmanned installations and offshore platforms producing oil and gas autonomously for several years. Most of the processes at these facilities are automated with remote monitoring and operators periodically going onsite to assess the facility. This blog article focuses on just that: Process automation using AI and machine learning (ML) to perform routine tasks and processes while also providing insights based on adaptable parameters.
Understanding Process Automation & Process Optimization
AI and ML enhance process automation by enabling systems to learn from data, adapt to new information such as changing market conditions, and make decisions using specified parameters. This combination of automation and AI leads to smarter, more efficient operations, while also improving accuracy and scalability.
Applied Process Automation
There are several valuable use-cases leveraging AI to automate segments of processes in the energy industry, such as:
- Failure remediation recommendations and mapping in asset strategy
- Predictive and risk-based maintenance recommendations
- Methods to streamline production
- The levelling of spares and materials inventory
- Recommendations on production inputs, such as feed gas composition
All these processes run automatically and in real-time to help the operator optimize production quality and quantity and make decisions that significantly improve production and profitability outcomes.
Baker Hughes’ Leucipa
Our work with Baker Hughes developing Leucipa is an excellent example of how energy companies are implementing process automation. Leucipa is an automated field production solution designed to optimize production, reduce unplanned downtime, and improve efficiency and emissions. The solution leverages AI-powered automation software to integrate production data, tools and workflows into a single platform. Our partnership aims to deploy AI-powered automation workflows across global assets, enhancing operational capabilities and efficiency while reducing emissions.
Real World Challenges & Considerations
What many current automated processes don't account for is the impact of the increased usage of machinery and equipment in the pursuit of process optimization. Questions come to mind, such as: How is it going to impact OpEx spend to keep these machines running? Will increased production rates reduce the lifespan of expensive equipment? When automating processes, a common pitfall is optimizing solely for production. How can companies optimize and grow production without sacrificing profit?
We’ve found that the economic model of process automation needs to be well thought out. ROI can change when you look at input factors, such as the changing cost of oil, the lifespan of equipment, the cost of electricity, profitability, the cost of replacement, the cost of maintenance activities, etc. When operating levels are determined, these types of multivariate factors need to be considered. The reality is, if the price of oil goes down significantly, you might want to slow down production to extend the lifespan of your equipment.
Using generative AI (GenAI), you can optimize for market conditions by implementing a platform that serves up prescriptive insights. It gives operators much more control over the use of their equipment and processes to ensure they do it profitably in the long term.
Future Trends in AI & Process Automation in Energy
The future of AI in process automation is getting to processes that are completely autonomous, such as autonomous drilling rigs, offshore platforms and manufacturing facilities — all run by AI. No one out there is doing this yet, as we still need a human in the loop, but we see it on the horizon and some people are starting to think about it as a reality.
As AI technology continues to evolve, several trends are likely to shape the future of process automation:
- Enhanced Human-AI Collaboration: The future will see a greater emphasis on collaboration between humans and AI systems. Rather than replacing human workers, AI will augment their capabilities, enabling them to make better decisions and perform tasks more efficiently.
- Advanced Predictive Analytics: AI will continue improving its ability to predict future events and trends. This will enable companies to anticipate changes in demand, identify potential issues before they arise and make proactive decisions.
- IoT & AI Integration: The integration of IoT with AI will create more intelligent and interconnected systems. IoT devices will generate vast amounts of data, which AI algorithms can analyze to optimize processes in real-time.
- Increased Focus on Sustainability: As environmental concerns become more pressing, AI-driven process automation will play a key role in promoting sustainability by optimizing energy usage, minimizing waste and improving resource efficiency.
Conclusion
Process automation using AI and ML cannot be done in a vacuum. Parameters must be set to allow for adaptability and learning, with humans still in the loop.
AI-driven process automation is redefining the energy sector by enhancing operational efficiency, reducing costs and improving accuracy. While there are challenges to overcome, the potential benefits make it a worthwhile investment for companies looking to stay competitive in a rapidly changing landscape. By understanding and leveraging the power of AI, businesses can unlock new levels of efficiency and drive sustainable growth.
