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Decarbonization in the Oil & Gas Industry and How AI Can Help

A report by the National Oceanic and Atmospheric Administration shows that the burning of fossil fuels has caused CO2 levels to rise to the highest level in 3 million years. This means for oil and gas companies to improve sustainability, they must find ways to reduce their carbon footprint and make efforts towards cultivating greener practices.

As such, many oil and gas companies are incorporating short-term climate goals like minimizing the intensity of its greenhouse gas for upstream operations by 15-20%. Other global companies are under pressure to abide by the Paris Agreement to reduce their carbon emissions below 2 degrees.

The good news is, the advancement of artificial intelligence (AI) has made its way into the industrial sector. Companies can leverage AI technology to analyze large volumes of data to develop diverse solutions to lower carbon emissions and increase overall global productivity and sustainability. We’ll take a look at how AI can revolutionize decarbonization and how companies can get involved today.

How AI can help with decarbonization

Companies involved in the production of gas and oil can leverage AI to lower their carbon footprints in several ways:

Use performative predictive analysis

Performative predictive analysis is used to estimate the impact of emissions inventory on the industry to help state regulators make effective carbon reduction strategies. Because oil and gas production from wells tends to decrease over time, this means a more significant effort is required to pump out these reserves, increasing carbon emissions.

AI can produce decline curves for each individual well, telling company regulators about the amount of oil present in each reserve and the amount that can be potentially pumped out. Performative predictive analysis using machine learning can also be used to research a potential oil field to predict the number of oil reserves, decreasing the number of wells that need to be drilled.

Conduct predictive maintenance of equipment

The volatility in oil prices leads to companies rapidly incorporating predictive maintenance technologies to help minimize costs by predicting gas intervals and optimizing maintenance schedules. The cost of gasoline is also one of the biggest expenses that goes into owning a car, so keeping prices down is important just as much for the everyday consumer as it is for major oil companies.

For example, Petrobras has applied AI technologies that can separate carbon dioxide from natural gas in pre-salt areas. Instead of venting out the gas, it can be injected into the reservoir in deep waters.

Predictive maintenance technologies also take a proactive approach in identifying equipment that requires greater surveillance or servicing. AI does this by diagnosing a potential problem with the equipment and suggesting solutions to fix it.

This reduces the time and labor required to test equipment manually. It also enables companies to manage machinery essentials, such as fluid components, efficiently. And industry-specific equipment is far from the only systems that can benefit from automation of maintenance processes - you can incorporate AI into other areas as well, such as maintenance of backend and software systems for your company.

Monitoring emissions

AI-powered data can also be used in data engineering to track carbon footprints in emissions. For example, companies can collect and organize data from corporate activities, including supply chains, components suppliers, transporters, and even product users. AI can then use this to generate approximations of missing data and estimate the accuracy of results. Using Big Data and AI, companies can gain an extensive, real-time view of all their production processes across the company.

The downfall of AI in the oil and gas industries

Unplanned downtime

Offshore gas and oil companies are always looking for ways to maximize production, owing to global rising energy demand. However, offshore platforms run at 77% maximum production only, which means the remaining 23% goes to waste. This also means a downplay of about 10 million barrels per day – a loss that is quite substantial for an oil and gas rig to bear.

These losses are due to inefficient equipment maintenance, catastrophic asset failures, or a malfunction in the automated AI system.

Advanced cyber threats

Wherever there's a digital system, there is always a threat of malicious attacks, and no industry is safe from these attacks, particularly essential ones like oil and gas. As data becomes more important to gas and oil industries, there is a substantial threat of innovative hacking capabilities to exploit company databases, potentially resulting in a loss of millions of dollars. And as AI continues to advance, so will the threat of these cyber threats.

Companies should adopt endpoint encryption and anti-malware software that can prevent even the most advanced attacks to keep malicious attackers from hacking into the customer's database and prevent significant downtime that may be hard to bounce back from.

Incorporating an AI strategy into your business

Companies should collect and analyze the emissions caused by AI-related technologies and compare the efficiencies between traditional and modern sustainable production methods.

The best way to do that is to adopt a three-pronged approach:

Start small: When designing your AI strategy, use pilots and prototypes and conduct comprehensive test experiments to further learning and development. Companies can also use MVP – Minimum Viable Product, to design an AI system for production and integrate a feedback loop for consistent improvements.

Aim High: Even if your carbon emissions are through AI technologies, monitoring these emissions across the value chain is still important. Identify where you need to apply the technology to reduce footprints and how AI can further minimize carbon emissions and costs.

Scale Fast: Transform your organization by building and developing technologies that can be painlessly incorporated across all departments in your company. Enable tech platforms to scale rapidly when defining new systems and parameters. You can also include AI strategies in your business models and align these with your company vision to help employees strategize accordingly.

Conclusion

So far, we have seen what benefits AI can provide when implemented in the exploration, research, and production departments. However, companies should also incorporate AI into day-to-day operations to remain competitive in this dynamic landscape. Whether it's to prevent an asset failure at the rig, minimize labor costs, or reduce carbon emissions, AI is set to revolutionize the oil and gas industry.

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