Rewiring Energy with AI: From Prediction to Optimization

AI in energy: Attracting global attention

The global energy market is facing mounting challenges. Rapid economic growth continues to drive a sharp rise in energy demand, while escalating climate change concerns are pushing the sector to accelerate the transition toward cleaner and more sustainable energy sources. As a result, energy service providers are being forced to rethink their strategies: simultaneously optimizing existing production assets and investing in innovation in search for renewable and sustainable energy alternatives. While this dual challenge may appear daunting, AI is emerging as a powerful enabler in addressing these complexities.

Market signals indicate strong confidence in AI’s ability to reshape the energy sector. The market for AI applications in energy is growing rapidly, with projections to expand at an average rate of 36.9% annually, from roughly $8.9 billion in 2024 to more than $58 billion by 2030 [1]. On an enterprise level, AI adoption is gaining significant traction as energy companies seek to enhance operational efficiency, improve productivity, and meet environmental objectives through use cases such as demand forecasting and predictive maintenance. Indeed, a survey by IBM reveals that a whopping 74% of energy & utility companies have implemented or are actively exploring AI applications [2]. These growth projections and adoption rate have reflected strong investment and confidence in AI’s ability to deliver values throughout the energy value chain.

AI use cases in energy: Examples from real-world success stories

Cementing plays a critical role in ensuring well integrity, safety, and zonal isolation, protecting both the production sites and the surrounding areas from contamination. Indeed, cementing failures can have disastrous consequences, such as posing sustained casing pressure, threatening structural integrity of the wells, and potentially contaminating surrounding environment. Despite its importance, cementing remains highly challenging. Industry research estimates that as much as 15% of primary cementing jobs fail [3], often due to issues such as incorrect thickening time predictions and poor slurry design.

To resolve these issues, global energy companies are turning to AI as a viable solution. One leading oilfield services provider, who specializes in drilling, cementing, and reservoir optimization, has partnered with FPT to deploy an AI-powered cementing prediction system. The solution leveraged machine learning models, including FLAML and XGBoost, to predict cement thickening time as well as the time required to reach ultrasonic cement analyzer (UCA) strength thresholds of 100 psi, 500 psi, and 1,000 psi.

The results were substantial. Prediction accuracy improved from approximately 60% to 79% across key metrics, while median error was reduced to 19%. More accurate thickening time predictions ensured that the cement remained pumpable during placement and developed strength when needed, reducing risks associated with premature setting or excessive delays. Improved UCA milestone predictions further enhanced well integrity by validating cement strength within defined timeframes, supporting long-term zonal isolation. Beyond technical performance, the AI solution delivered significant operational efficiencies. By optimizing more than 17,000 laboratory tests per month that would otherwise be required for manual cement validation, the company substantially reduced experimentation costs and efforts. With an average runtime of just 1.6 seconds per prediction, the AI model has indeed accelerated the cement design workflow

AI is playing a critical role in optimizing oil well design and construction. During the planning stage, AI models analyze massive volumes of historical drilling data, geological information, and seismic interpretations to recommend optimal design, such as well trajectories and drilling parameters. Beyond planning, AI can also be applied during the oil well construction phase to analyze real-time data from sensors on the rig, providing predictive insights that allow engineers to dynamically adjust drilling parameters to optimize operations.

Global energy companies are already realizing the tangible benefits of this approach. BP, for example, has been actively leveraging AI to accelerate drilling operations by applying advanced analytics to its extensive seismic datasets in the Gulf of Mexico. Using AI, BP was able to complete seismic data analysis in just 8–12 weeks, which would traditionally take engineers up to 12 months to analyze [4].  Similar results are reported by other energy service providers. Devon Energy deployed machine learning to monitor oil rigs across the United States, achieving a 25% improvement in the productive life of its oil and gas wells [4]. In another case, a multinational energy company partnered with FPT to implement an AI-powered well construction solution, achieving a 25% reduction in well construction costs and an 80% decrease in engineering effort.

Machine learning algorithms can learn normal operating behavior for each asset and flag deviations that indicate corrosion, detecting early warning signs long before leaks occur. Trained with massive databases, AI is superior in picking up patterns and deviations that are often invisible to the human eyes, enabling earlier and more accurate detection of potential leak sources than traditional manual inspections. This proactive capability allows operators to promptly intervene, preventing resource waste, costly repair, and minimizing environmental risks.

A practical example is FPT’s Flezi Nergy Dronin™, an AI-powered automation solution for pipeline inspection. The platform leverages drone imagery and advanced analytics to accurately and rapidly detect corrosion across oil and gas assets, including pipelines, flanges, and valves. Compared to the traditional manual pipeline inspection approach, Flezi Nergy Dronin™ is 20% faster at predicting corrosion while achieving an accuracy rate of up to 95%.

Conventional Pipeline Inspection Solution

Flezi Nergy Dronin™ for Automated Inspection

Realizing the AI potential with speed and scale

As 2026 approaches, AI adoption has clearly moved beyond the experimental phase and is now under increasing pressure to deliver tangible business values. Indeed, a survey of 3,700 executives found that 61% of business leaders face greater pressure than last year to demonstrate a clear return on investment from their AI initiatives [5]. This means that global leaders in general and those at energy companies in particular, are required to have clear visions of AI and execute it quickly and at scale. Achieving this, however, is far from straightforward. The complexity of AI implementations, especially for energy-specific use cases, requires both advanced technical capabilities and deep domain expertise. As a result, partnering with an experienced technology provider has become a practical way to accelerate AI outcomes. An effective partner must not only excel in AI and data engineering but also understand energy operations and production processes.

With more than 25 years of experience delivering innovative technology solutions, FPT has established itself as a trusted partner for energy companies worldwide, working with leading organizations such as Halliburton, Petronas, RWE, and E.ON. To further support the sector, FPT offers Flezi Nergy—a dedicated suite of AI solutions for energy and utilities. Flezi Nergy delivers high-impact use cases, including drilling and cementing optimization, safety incident prediction, and well production optimization. Flezi Nergy is part of FPT’s flagship AI-first platform, FleziPT, which is designed to deliver AI solutions at speed and scale. By streamlining workflows with AI tools & accelerating deployment with FPT’s AI-augmented workforce, FleziPT can reduce development time by up to 60% and cut rework by more than 50%, enabling energy companies to move faster from AI ambition to measurable business results.

Learn more about FleziPT here: https://fptsoftware.com/flezipt