Case Study: Transforming Energy Operations with AI at ExxonMobil

Background

ExxonMobil is a multinational energy corporation and the largest investor-owned company in the world by revenue. According to its 2021 annual report, ExxonMobil generated $276.7 billion in revenue, with a net income of just over $23 billion. The company employs around 64,000 people and has a market cap of $420.1 billion. Traditionally known for its dominance in oil and gas, ExxonMobil has now stepped into the realm of artificial intelligence (AI) to enhance its operations. While exact details about its AI adoption are sparse, two significant AI applications have emerged: predictive maintenance and expediting well development.

Key Takeaways

  • ExxonMobil utilizes AI for predictive maintenance, significantly reducing unplanned downtime and labor costs.
  • The company uses AI to consolidate data silos, which expedites well development and provides faster ROI.
  • Challenges exist in terms of data integration and scalability.
  • ExxonMobil collaborated with external experts, like IBM, for the implementation of AI solutions.

Deep Dive: Transforming Energy Operations with AI at ExxonMobil

Approach

ExxonMobil focuses on two primary areas for AI applications: predictive maintenance and well development. In predictive maintenance, ExxonMobil uses machine learning algorithms in conjunction with proprietary laboratory software to provide insights into equipment conditions. In well development, the company aims to speed up the arduous process of seismic interpretation through AI and a better data infrastructure.

Implementation

Predictive Maintenance
ExxonMobil offers a “Mobil Serv Lubricant Analysis” service that leverages machine learning algorithms to analyze oil samples from drilling equipment. Users register for the program and submit samples, which are then analyzed within 24-48 hours. Reports are made available online, providing insights into equipment conditions and recommended actions. In a case study, this system led to a 66% reduction in sample collection time and an annual savings of $9,600 in labor costs for an alumina production company in Texas.

Expediting Well Development
ExxonMobil tackled the challenge of slow seismic interpretation by collaborating with IBM’s data science and AI team for a 12-month period. Together, they built a data management platform that consolidated multiple data sources, most likely using IBM’s Cloud Pak for Data. This AI-driven data consolidation has reportedly reduced drilling design planning by two months and decreased the time spent on data preparation by 40%.

Results

The AI initiatives have yielded compelling outcomes. Predictive maintenance has significantly reduced unplanned downtime, which according to the Journal of Petroleum Technology, can cost an average oil and gas company around $38 million in losses. For well development, ExxonMobil has shortened the drilling design planning timeline, offering a quicker return on investment.

Challenges and Barriers

One of the most prominent challenges faced by ExxonMobil in the implementation of AI is data integration. The data used for seismic interpretation was siloed in hundreds of applications, making it difficult to collect and organize. Moreover, scalability remains a concern, as stated by ExxonMobil’s Digital Transformation Lead, Dr. Xiaojung Huang. Building a robust data foundation was essential for scaling AI initiatives.

Future Outlook

Given the initial successes, ExxonMobil is likely to further integrate AI into its operational framework. Future applications might extend into optimizing supply chain logistics, automating administrative tasks, and enhancing safety measures. The collaboration with external experts like IBM suggests a willingness to partner for innovation, opening doors for more advanced AI solutions.

Conclusion

ExxonMobil has successfully incorporated AI to achieve efficiency gains and cost reductions. Through predictive maintenance and well development optimization, the company has realized tangible benefits, including reduced downtime and quicker project completions. Despite challenges in data integration and scalability, ExxonMobil’s AI journey serves as an inspiring example for the oil and gas industry. As ExxonMobil continues to advance its AI initiatives, it is poised to set new benchmarks for technological innovation within the energy sector.

Source:
Artificial Intelligence at ExxonMobil – Two Applications at the Largest Western Oil Company


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