Cognitive AI to boost sustainability for the oil and gas sector Cognitive AI to boost sustainability for the oil and gas sector
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Cognitive AI to boost sustainability for the oil and gas sector

Cognitive AI to boost sustainability for the oil and gas sector

AI and data analytics are already finding increasing adoption in the oil and gas sector, across a wide range of scenarios for upstream and downstream use cases


The oil and gas sector is facing growing pressure to become more ‘green’. While mitigating environmental impact has been on the energy sector’s agenda for a long time, recent years have seen increasing calls from both within and outside the industry to improve sustainability and minimise the sector’s footprint, especially in the face of demands for alternative energy and more accountability. For a growing number of companies, artificial intelligence (AI) is proving to be the key to reducing environmental impacts and meeting sustainability goals.

AI and data analytics are already finding increasing adoption in the oil and gas sector, across a wide range of scenarios for upstream and downstream use cases. Companies are using AI in areas as diverse as monitoring operations for plant efficiency and uptime improvements to analysing production data and trends and managing product formulations and output. AI is helping companies to make better, more informed decisions about resources and assets while enabling new sources of data from connected sensors that are opening up a huge number of potential uses. Unsurprisingly, AI is also being employed to tackle the challenge of sustainability across many different areas of the industry.

In upstream operations, advanced technology like reservoir modelling is well established as a way to extract the most from precious natural resources without wastage. Today, AI is taking oil field development to the next level. AI solutions are supporting oil field planning by improving and complementing traditional physics-based simulation models.

Typically, oil field planning requires geologists, reservoir engineers, petro-physicists and other experts, working for months or years to plan the optimal placement of wells – a hugely complex task where even a small reservoir could have as many as one hundred trillion possible well placement locations.

Now Cognitive AI software, which combines historical and real-time data with embedded human knowledge and human-like reasoning, is enhancing existing predictive methodologies and helping field planners to pinpoint drilling opportunities, reducing the time taken to process reservoir data from months to days.

Recent studies by NVIDIA and Beyond Limits, an industrial AI company, found that high-performance AI frameworks powered by GPUs can improve profitability by over 13 per cent in field planning scenarios compared to traditional methods while reducing the number of injector wells drilled by 58 per cent. The drastic reduction of injector wells reduces drilling costs by up to $5-10m per well while also minimising environmental hazards of drilling, including groundwater seepage and damage to geological stratifications. Better extraction methods also reduce the need for potentially dangerous oil recovery mechanisms and fracking.

Cognitive AI-powered solutions are supporting the longevity of wells so that exploration companies can get the best possible rate of extraction and ensure resources are utilised to their fullest extent. By combining historical data with real-time sensor data and human expertise captured by cognitive reasoning technology, AI enables companies to extend the productive life of their wells and increase productivity over time, maximising the resources that can be extracted and reducing the need to drill new wells.

Even once reservoirs are depleted, Cognitive AI solutions can be used to contribute to sustainability efforts. Deep learning AI frameworks are being implemented to identify depleted reservoirs and saline aquifers, which can be used for large-scale carbon capture and geological sequestration, which is quickly becoming a critical component for achieving carbon neutral and net-zero operating targets.

In the downstream arena, Cognitive AI software solutions are contributing to the sustainability of oil and gas operations in multiple areas. Refinery operations are typically complex and achieving operational targets in the sector can be a challenge. Cognitive AI solutions working with IoT sensors can monitor dynamic refinery conditions and unplanned events in real time – or even predict issues such as equipment breakdowns. This advanced intelligence gives plant operators more time to take corrective action, providing better insights for optimising operations.

These enhancements can help refinery operators to be more profitable. Some Cognitive AI solutions have optimised refineries in a short space of time, with up to 17 per cent improvement in operating to plan. Better plant management and maintenance increases uptime and extends asset life as well as reduces the risk to assets, personnel, and possible environmental contamination.

Cognitive AI is already helping companies codify and embed complex requirements into entire operations, such as compliance with environmental regulations so that refineries and other facilities can monitor compliance in real time. This means that incidents or operations that might risk breaching regulations or causing problems can be detected even before they happen, allowing for remedial action while compliance audits and paper trails are automated, reducing the burden of reporting.

The journey to creating a more sustainable oil and gas industry may still be in its early stages – but advanced solutions powered by Cognitive AI technology are already addressing some of the biggest challenges we face today.

As automation efforts continue to progress, AI-driven innovation will continue delivering new solutions that ensure the energy sector can become a positive contributor to preserving our environment and safeguarding our future.

Ken Habson is the general manager – EMEA at Beyond Limits

Read: Gartner identifies four trends driving near-term AI innovation

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