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Companies across the energy sector understand that new digital technologies will be crucial to reducing emissions and thriving amid the coming energy transition. Increasingly, they are turning to AI (including GenAI) to improve their ability to reduce, manage, report, and model their emissions profiles and associated environmental impacts. AI has the potential to manage emissions more accurately, with greater granularity, and in real time. Most importantly, AI can quickly turn raw data into actionable knowledge and insights that can be used to drive operational changes and optimizations.
For midstream energy companies, the emissions sources of highest concern are emissions from vehicles and equipment, flaring and venting, and fugitive emissions. They are aiming to accelerate carbon capture and storage, leak detection and reduction, and natural gas replacement as they map out the broader transition to renewables. By combining AI with operational changes, midstream companies will find themselves better positioned to control fugitive emissions, largely eliminate non-emergency flaring, increase the deployment of carbon capture technologies, and expand the use of low-emissions hydrogen fuel sources. By advancing these strategic imperatives, they will pave the way for affordable decarbonization.
The Next Phase of AI Implementation
Many midstream companies have made important initial strides in adopting AI, primarily in the areas of identifying and quantifying emissions sources; using edge intelligence and analytics for pipeline risk assessment, pipeline maintenance, and storage optimization; and automating emissions reporting to adhere to regulatory compliance requirements. However, quite a few promising AI use cases remain to be explored.
The following use cases, in particular, have potentially high business impact in the midstream space yet remain under-explored:
- Monitoring and alert systems: AI-powered monitoring and alert systems can detect changes in emissions levels and send alerts to operators in real time, along with recommended actions to prevent further emissions. Monitoring systems can provide real-time data on emissions, allowing companies to quickly identify and respond to changes in emissions levels. Intelligent visual monitoring will cost-effectively manage visual sensors at scale, analyzing massive volumes of visual data with AI and inputs from other systems (such as portable gas analyzers) and distributing personalized, actionable information to end users.
- Scenario analysis: Similarly, AI can be used to conduct scenario analysis to assess the impact of different emissions reduction strategies and identify the most effective ones. AI-driven simulations can model various scenarios and responses to spills or leaks, helping companies develop more effective emergency response plans and train their personnel accordingly. Importantly, AI-powered data analysis will also guide investments in renewable energy sources by predicting demand, optimizing production, and providing the real-time data that enables midstream companies to meet their emissions reduction targets while reliably serving the needs of customers. On a more tactical level, AI tools can analyze data from compressors, pumps, and other equipment to optimize their performance and identify areas where energy efficiency can be improved.
- Regulatory compliance: AI is already beginning to aid in risk assessment and management, which is crucial for regulatory compliance. AI algorithms can analyze historical data on safety incidents, environmental breaches, and operational disruptions to predict and prevent potential compliance risks. AI tools can ensure compliance with emissions limits, reporting requirements, and environmental permits. AI tools can also mine both regulatory documents and internal datasets, extracting relevant information and highlighting areas that need attention to meet regulatory requirements. Regular monitoring and reporting of emissions will help companies avoid penalties and maintain their social license to operate.
AI Proofs of Concept in the Energy Industry
Energy companies have already begun to leverage cutting-edge AI applications to drive measurable business benefits.
One of Wipro’s petrochemical clients in the Middle East, for example, worked with us to roll out an energy optimization and sustainability platform that collects real-time GHG monitoring data, translates it to emissions, and reports on KPIs related to energy intensity, material intensity, waste recycling, and emissions intensity, while delivering insights on key trends across processes and equipment at manufacturing sites.
In another project, we worked with a Singapore-based energy utility to deliver an AI solution that improved wind turbine productivity by 2% through accurate alignment of the turbine dynamically to the wind direction.
The potential of similar projects to target emissions in the midstream space is exciting. The midstream energy industry faces pressure on several fronts. AI, along with machine learning and big data analytics, can enable major changes in operational efficiency and provide greater insight and control. These tools can help midstream operators identify the fastest and most effective steps towards the path of emissions management and decarbonization and provide guidance for executing on those steps. Fundamentally, AI tools and techniques will empower the midstream segment to turn emissions management into a competitive strategy.
