Harnessing AI for Operational Efficiency in the Energy Industry — Part 1
The Evolution of Prescriptive Maintenance
In the rapidly evolving energy industry, achieving operational efficiency is essential. You could say this has been a challenge for energy companies since they started drilling, producing oil and gas, refining, and moving their products from point A to point B. As the demand for energy continues to rise, companies are under increasing pressure to optimize their operations, reduce costs and ensure reliability. Efficiencies driven by intelligent automation alone have been projected to save the energy sector between $237 billion and $813 billion.
These sentiments are further underscored in our resent research, From Hype to Impact: How Enterprises Can Unlock Real Business Value with AI. Focusing on the energy industry specifically, over 32% of respondents identified operational efficiency as a top priority of artificial intelligence (AI) adoption, followed by productivity improvements (31%) and profit margin improvement (26%). Priorities such as efficiency, productivity and cost savings reflect an industry grappling with costly legacy systems, volatile markets and streamlining processes, while at the same time meeting sustainability goals.
One of the most promising advancements in achieving operational efficiency is the use of AI, particularly for prescriptive maintenance. Over the past 80+ years, the industry has seen significant advancement in equipment maintenance strategies. In this article, we will be focusing on how AI can drive operational efficiency by carrying out prescriptive maintenance.
A Brief History of Equipment Maintenance
Equipment maintenance has come a long way. Initially, the approach was reactive, where equipment was repaired only after a failure occurred, leading to costly downtime and unexpected disruptions. The need for a more proactive approach led to preventive maintenance, where regular inspections and scheduled maintenance were conducted to prevent failures, which helped reduce equipment downtime. The next iteration was condition-based maintenance, which required a technician to record what they saw, with a reliability engineer calculating failure rates based on the collected data.
Predictive maintenance followed, leveraging data from sensors and historical records to predict when equipment might fail, a better solution that still required significant resource time.
The Development of Prescriptive Maintenance
While predictive maintenance was a significant improvement, it still had limitations. It can predict failures but does not provide specific recommendations on how to prevent them. This is where prescriptive maintenance comes in — going beyond prediction by analyzing many sources of data and recommending, or prescribing, specific actions to prevent equipment failures. This is only enabled when disparate groups of systems are tied together, such as IoT data coming from equipment and sensors, historical maintenance records, strategies in the ERP system, and so on.
AI plays a central role here. By processing immense amounts of data, it identifies patterns, detects anomalies and determines the best course of action. Machine learning (ML) models can then predict the likelihood of equipment failures and suggest the best course of action to mitigate these risks. For example, in an oil refinery, AI can monitor pumps, compressors and other critical components. If it detects a deviation from normal operating conditions, it can alert maintenance teams and provide recommendations such as adjusting operational parameters, scheduling inspections, or replacing parts before a failure occurs. This proactive approach not only prevents costly downtime but also extends the lifespan of equipment.
Leveraging AI in the Energy Industry
Our team has extensive experience working with energy companies across a wide range of areas, from exploration and production to refining and mining. As early as 2015, we’ve partnered with several leading energy companies to build predictive maintenance and advanced analytical models for anomaly detection, with early warning systems to prevent incidents and production shutdowns. Over time, these engagements have helped hone our methodologies, harnessing the potential of cutting-edge technologies like AI and ML.
Considering that more than 80% of all AI projects fail, success requires a clear strategy. Our team employs four key repeatable patterns for AI success: understanding what product concepts already exist at the client, discovering the status of client platforms and data, gaining internal SME buy-in, and lastly, planning to sustain the capability at the client in the long run.
Our Approach to Delivering AI-Driven Prescriptive Maintenance
Our approach starts with integrating AI into existing, often siloed, systems. By unifying data sources such as IoT sensors and maintenance systems, we enable end-to-end insights that unlock the value of a company’s data.
To be clear, AI doesn't replace existing systems, such as an ERP or platforms for failure prediction or sensor information; instead, it acts as an orchestration layer, combining existing knowledge bases and then providing a robust analytics platform.
To get to those insights more quickly, it’s necessary to layer generative AI (GenAI) on top of existing AI systems. This layer elevates the system by transforming raw intelligence into actionable, explainable and human-friendly decisions. This is valuable because it can offer maintenance teams relevant historical information, such as 10 years of work-order history for a specific piece of equipment, and integrate relevant content from existing knowledge bases and manuals.
Using AI and GenAI, we focus on three critical elements of prescriptive maintenance:
- Lifetime-Based Maintenance — Optimizing equipment lifecycles to maximize value.
- Incident & Failure Prediction — Proactively addressing risks to avoid costly disruptions.
- Anomaly Detection — Identifying subtle deviations in performance before they become issues.
All these capabilities combined provide valuable insights to the operators in the field, head office personnel and staff in operations centers.
The Path Forward
As companies continue to embrace AI, and they will continue to increase spend at a rate of 14% annually. By adopting prescriptive maintenance and other AI-driven solutions, they can tackle complex operational challenges while improving efficiency, achieving sustainability goals and building resilience.
Our ability to deliver tailored AI solutions further enhances the industry's ability to achieve these goals, paving the way for a more efficient and reliable future.
