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AI and ML: Your Ticket to Achieving Net-Zero Emissions

The Intergovernmental Panel on Climate Change (IPCC) has raised code red. Its recent report demands oil and gas companies take urgent action for limiting the rising global temperatures to under 1.5°C by 2050 . While some communities call for revoking O&G companies’ operating licenses, others accept that it’s not rational. After all, oil and gas are required for various purposes, beyond energy production.

So, O&G executives are viewing net-zero targets as a business opportunity to not only pivot to lowcarbon operations, but also enhance the value chain by leveraging advanced technologies. Data analytics, machine learning (ML), and artificial intelligence (AI) have emerged as the key technologies, with the potential to play critical roles in making traditional fuels acceptable in our low-carbon future.

Riding the technology wave to chase away emissions

The O&G industry’s current priorities – which are supplying affordable and reliable energy while minimising greenhouse gas (GHG) emissions – necessitate companies to develop strategies that involve:

  • Improving operational efficiency by enabling process transparency and automation
  • Increasing asset/equipment reliability through monitoring and proactive maintenance
  • Minimising emission risks due to methane leaks, oil spills or natural gas flaring
  • Decarbonising energy through carbon capture utilisation and storage
  • Investing in cleaner and less fossil-fuel dependent energy solutions, such as solar and wind

To successfully implement these strategies, companies have to accurately identify and measure all parameters that impact performance and cause emissions. The first step constitutes asset integration, data collection and analysis. In the next step, companies must gain actionable insights by applying advanced analytics, AI and ML, along with complementary technologies, such as cloud, edge analytics, and automation.

The imperative is to ensure AI is used on all the key stages to achieving net-zero targets, as follows:

  • Monitoring – Leveraging AI, companies can holistically track their emissions throughout their value chain – including materials, component suppliers, logistics, corporate activities, equipment, products, and by-products. In case there is any missing data, AI helps to make near accurate estimations by creating historical data patterns.
  • Predicting – Along with enabling predictive maintenance, AI can facilitate emission forecasting by analysing the company’s current carbon-minimising efforts with potential reduction innovations, and future hydrocarbon demands. These insights help companies to set and adjust the targets with greater accuracy.
  • Reducing – The detailed insights from monitoring and predicting emissions can help companies develop strategies for achieving net zero targets. Optimisation using the prescriptive AI approach can help take control over even the scope 3 emissions, which constitute the difficult-to-remove 75 to 80 percent of lifecycle emissions

Applying AI for Net Zero Emissions

In the past few years, companies have accelerated their digital transformation and many have even embarked on their AI journeys. A European Commission’s report suggests that by 2020, 57% percent of O&G companies had adopted one or two AI technologies and another 6 percent were planning to implement the technology in the near future . While this is a good step forward, companies will need to consider the broader applicability of AI and ML to successfully achieve their net-zero targets. After all, the technologies can not only alleviate bottlenecks for addressing emissions but also enhance operational efficiencies.

AI & ML - Net Zero Emissions-page-003.jpg12

Figure 1: Mapping ML applications impacting GHG emissions

The figure above indicates how ML can reduce emissions (orange) as well as accelerate carbon production through emission-intensive activities (purple).

Here’s how AI and ML radically impact your carbon footprint:

  • Policy design, monitoring, and enforcement – ML algorithms analyse the data captured from the IIoT sensors, satellite imagery, and text documents to facilitate planning systems, developing net-zero target achieving policies, and RD&D. Key use cases include GHG emissions tracking, mapping and closely monitoring the infrastructure, and capturing insights for improving efficiencies.
  • R&D for low-carbon technologies – ML is extensively used in accelerating scientific discovery. In the case of developing low-carbon technologies, the algorithms enable engineers quickly search for experimental parameters that help them design new and improved batteries. Key use cases include photovoltaics and fuel and EV batteries.
  • Planning and design of relevant systems – Leveraging its learning capabilities from time series, ML forecasts production requirements and near-future demands, thereby reducing energy wastage. Key use cases include carbon trading and urban infrastructure
  • System operation and efficiency – ML implemented into a fully integrated system allows enhanced control over the complex assets, helping the company save energy and resources. Key use cases include industrial cooling and heating systems, and the electrical grid.

ML also has the capabilities to accelerate time intensive physics-based simulations, which enable climate modelling. AI-powered predictive maintenance applied to low-carbon systems additionally improves asset life and efficiency.

Apart from these, O&G companies are using ML for oil and gas exploration to reduce production costs and increase reserves. This increases fossil fuels in the environment, thereby accelerating the rate of emissions. So, the very technology that can reduce emissions also has the potential to increase them.

Given that ML shares a diverse relationship with emissions, companies will need to carefully develop informed strategies that enable them to achieve their net-zero emission targets. At Xebia, we strive to extract meaningful information from millions of electronic touchpoints and evaluate the data maturity to develop a roadmap for your business. Xebia is equipped to design and develop AI solutions that can help O&G companies achieve their net-zero emissions. We have extensive domain expertise and experience in implementing data analytics, ML, and AI. Our offerings include data engineering, BI assessments, developing data roadmap, maturity scanning, training, and deploying ML algorithms

The clock is ticking and the challenge of removing net carbon emissions is no more a nice-to-achieve, but a mandate that impacts every living being today. So, every company, country, and community needs to resolve the issue together.

Join hands and let’s get started.

To find more information about Xebia’s AI and ML solutions, write to us at .


1. IPCC Report on Oil and Gas - Energy Voice

2. Reducing Emissions in the Oil and Gas Industry - World Economic Forum

3. European Parliament Study on Emissions - Europarl

4. Research Document on Emissions - HAL Archives

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