AI/ML in Oil & Gas Refining: Part 1 Maintenance Optimization
𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
The oil & gas refining industry is under increasing pressure to improve efficiency, reduce costs, and enhance safety. Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful tools to achieve these goals, offering refiners new ways to optimize operations, predict failures, and make smarter decisions. While AI was once considered a high-cost, complex investment, advancements in open-source tools and cloud computing have made these technologies more accessible than ever.
This article is the first in an 8-part series by Fidelis Associates, where we explore how refineries can integrate AI/ML across various operational areas without requiring costly enterprise software. Each installment will focus on a key area where AI is delivering measurable improvements:
𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗶𝘀 𝗔𝗿𝘁𝗶𝗰𝗹𝗲): How AI enhances predictive maintenance, root-cause analysis, and reliability-centered maintenance to minimize downtime and reduce costs.
𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: AI-driven process optimization, energy consumption reduction, and throughput enhancement in refining operations.
𝗛𝗲𝗮𝗹𝘁𝗵, 𝗦𝗮𝗳𝗲𝘁𝘆, 𝗮𝗻𝗱 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 (𝗛𝗦𝗘) 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Using AI to improve worker safety, emissions monitoring, leak detection, and regulatory compliance.
𝗔𝘀𝘀𝗲𝘁 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 & 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲: AI-powered risk-based inspections, corrosion monitoring, and failure prediction for critical refinery infrastructure.
𝗦𝗵𝘂𝘁𝗱𝗼𝘄𝗻 & 𝗧𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Leveraging AI to optimize scheduling, predict delays, and improve resource allocation during planned maintenance events.
𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜/𝗠𝗟 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗥𝗲𝗳𝗶𝗻𝗲𝗿𝗶𝗲𝘀: A deep dive into cost-effective, open-source AI/ML solutions for refineries.
𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗜𝗻-𝗛𝗼𝘂𝘀𝗲 𝗔𝗜 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: Understanding the computing power, sensor networks, and infrastructure needed to build AI solutions in-house.
𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 & 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻: A practical roadmap for AI adoption, overcoming barriers, demonstrating ROI, and scaling AI initiatives.
Each article will provide real-world case studies, best practices, and actionable strategies to help refiners harness AI for better decision-making and operational efficiency.
This first installment focuses on 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻, one of the highest-impact, lowest-cost applications of AI in refining. Let's explore how AI can revolutionize maintenance strategies, reduce downtime, and improve asset reliability.
𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 𝗪𝗵𝘆 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗙𝗮𝗹𝗹𝘀 𝗦𝗵𝗼𝗿𝘁
Historically, refineries have relied on two primary maintenance strategies:
𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲: Fixing equipment after it fails, leading to costly emergency repairs and production losses.
𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲: Servicing equipment on a fixed schedule, which can result in unnecessary downtime and wasted resources.
Neither approach optimally balances cost and reliability. Equipment often fails between scheduled maintenance checks, and unnecessary servicing leads to wasted labor and parts. 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 solves this problem by analyzing equipment data in real-time to forecast failures before they occur.
𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲: 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀
AI-powered maintenance relies on sensor data, advanced analytics, and machine learning models to detect anomalies and predict failures. The process involves:
𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Sensors monitor temperature, vibration, pressure, and other key indicators in real time.
𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: AI algorithms analyze data patterns, identifying deviations that signal potential failures.
𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: ML models forecast when and how equipment is likely to fail, enabling proactive repairs.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴: AI optimizes maintenance timing, aligning work with production schedules to minimize disruption.
These AI-driven insights 𝗿𝗲𝗱𝘂𝗰𝗲 𝘂𝗻𝗽𝗹𝗮𝗻𝗻𝗲𝗱 𝗱𝗼𝘄𝗻𝘁𝗶𝗺𝗲, 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗲𝘅𝘁𝗲𝗻𝗱 𝗮𝘀𝘀𝗲𝘁 𝗹𝗶𝗳𝗲, 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 𝗹𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝘄𝗵𝗶𝗹𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝘀𝗮𝗳𝗲𝘁𝘆.
𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲
Here are three practical examples of how AI-driven predictive maintenance can be applied to specific refinery equipment and systems:
𝗛𝗲𝗮𝘁 𝗘𝘅𝗰𝗵𝗮𝗻𝗴𝗲𝗿𝘀
𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Sensors track temperature differentials, flow rates, and pressure drops in real time.
𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: AI identifies inefficiencies or early fouling by detecting deviations in heat transfer performance.
𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Machine learning predicts when fouling will reach a critical level, allowing for proactive cleaning before energy efficiency is compromised.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴: AI schedules cleaning during optimal production windows, minimizing disruption and maximizing efficiency.
𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Improved energy efficiency, reduced unexpected downtime, and enhanced safety by preventing pressure-related failures.
𝗣𝘂𝗺𝗽𝘀 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗼𝗿𝘀
𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Vibration, sound frequency, and pressure sensors monitor equipment health continuously.
𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: AI detects irregular vibrations, cavitation, or fluctuations in operating pressure that indicate early wear.
𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: ML forecasts failure timelines based on historical data, preventing catastrophic breakdowns.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴: AI aligns maintenance tasks with production demands, ensuring repairs occur before major failures while maintaining output.
𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Increased equipment lifespan, reduced repair costs, and enhanced worker safety by preventing sudden equipment failures.
𝗙𝗹𝗮𝗿𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Sensors monitor gas flow rates, temperature, and emissions levels.
𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: AI detects irregular combustion patterns or unexpected increases in flare activity, which may signal leaks or process inefficiencies.
𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: ML models anticipate potential failures in relief valves, flare headers, or pilot burners.
𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴: AI triggers maintenance alerts before excessive emissions occur, ensuring regulatory compliance and operational safety.
𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Improved regulatory compliance reduced environmental impact, and increased system reliability by preventing excessive emissions and hazardous conditions.
These examples demonstrate how AI-driven predictive maintenance enhances refinery reliability, prevents costly failures, and improves efficiency.
𝗣𝗿𝗼𝘃𝗲𝗻 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗥𝗲𝗳𝗶𝗻𝗲𝗿𝘆 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲
Industry leaders have documented substantial gains from AI-driven maintenance optimization:
𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗨𝗻𝗽𝗹𝗮𝗻𝗻𝗲𝗱 𝗗𝗼𝘄𝗻𝘁𝗶𝗺𝗲: AI-based predictive maintenance has been shown to decrease unexpected equipment failures by 𝟯𝟬–𝟱𝟬%, directly improving refinery uptime.
𝗟𝗼𝘄𝗲𝗿 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗖𝗼𝘀𝘁𝘀: Studies indicate AI-driven strategies can reduce maintenance expenses by 𝟭𝟬–𝟰𝟬%, saving millions annually.
𝗘𝘅𝘁𝗲𝗻𝗱𝗲𝗱 𝗘𝗾𝘂𝗶𝗽𝗺𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝘀𝗽𝗮𝗻: Predictive maintenance helps prevent excessive wear, extending machinery lifespan by 𝟮𝟬–𝟮𝟱%.
𝗛𝗶𝗴𝗵𝗲𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁: Avoiding failures means refineries can maintain higher throughput, leading to 𝟭𝟱–𝟮𝟬% gains in efficiency.
𝗛𝗶𝗴𝗵 𝗥𝗢𝗜: Companies implementing AI maintenance strategies report returns of 𝟱:𝟭 𝘁𝗼 𝟭𝟬:𝟭, often achieving full payback within a year.
One notable case study is Shell, which deployed predictive maintenance across over 10,000 pieces of equipment, reducing downtime by 𝟮𝟬% and saving an estimated $𝟮 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆. Similar results have been reported by BP, Chevron, and ExxonMobil, proving AI’s tangible impact on refinery operations.
𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗶𝗻𝗴 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀
Despite the clear benefits, AI adoption in refinery maintenance faces several hurdles:
𝗗𝗮𝘁𝗮 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: AI models require high-quality, real-time sensor data, but many refineries struggle with data silos and outdated infrastructure.
𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗦𝗸𝗲𝗽𝘁𝗶𝗰𝗶𝘀𝗺: Maintenance teams may initially distrust AI-driven recommendations. Training and change management programs help bridge this gap.
𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗟𝗲𝗴𝗮𝗰𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: AI solutions must seamlessly connect with existing Computerized Maintenance Management Systems (CMMS) to drive actionable results.
𝗖𝗼𝘀𝘁 & 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽𝘀: While AI delivers high ROI, initial investments in sensors, software, and talent can be substantial. Strategic pilot programs help mitigate financial risks.
To successfully deploy AI-driven maintenance, refiners must 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗱𝗮𝘁𝗮 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻, 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗽𝗶𝗹𝗼𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗥𝗢𝗜 to gain executive buy-in.
𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗥𝗲𝗳𝗶𝗻𝗲𝗿𝘆 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲
The transition from reactive to AI-powered predictive maintenance is well underway. As sensor networks, cloud computing, and machine learning models continue to evolve, AI adoption in maintenance will become the industry standard.
Leading refiners are already seeing the benefits—𝗳𝗲𝘄𝗲𝗿 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀, 𝗹𝗼𝘄𝗲𝗿 𝗰𝗼𝘀𝘁𝘀, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆. Those who embrace AI-driven maintenance now will be best positioned to drive higher profitability and reliability in the years ahead.
𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 & 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀
Implementing AI-driven maintenance optimization may seem like a complex task, but it doesn’t have to be. At Fidelis Associates, we understand that every refinery has unique operational challenges, and a one-size-fits-all approach doesn’t work. That’s why we partner with refineries to develop 𝘁𝗮𝗶𝗹𝗼𝗿𝗲𝗱 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 that align with specific business needs, infrastructure, and objectives.
Whether you’re just beginning to explore AI applications or looking to scale an existing program, our team can help you navigate the process—𝗳𝗿𝗼𝗺 𝗶𝗻𝗶𝘁𝗶𝗮𝗹 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗽𝗶𝗹𝗼𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝘁𝗼 𝗳𝘂𝗹𝗹-𝘀𝗰𝗮𝗹𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗢𝗜 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁. Our experts bring deep industry knowledge and technical expertise to ensure AI adoption is both seamless and impactful.
If you found this content valuable, consider 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗙𝗶𝗱𝗲𝗹𝗶𝘀 𝗔𝘀𝘀𝗼𝗰𝗶𝗮𝘁𝗲𝘀 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 for more insights on AI/ML in oil & gas refining. Stay connected with us as we continue to explore innovative AI-driven solutions that enhance refinery operations.
In the next installment of this series, we’ll explore how 𝗔𝗜 𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁, from optimizing process controls to reducing energy consumption.
𝗔𝗯𝗼𝘂𝘁 𝗙𝗶𝗱𝗲𝗹𝗶𝘀 𝗔𝘀𝘀𝗼𝗰𝗶𝗮𝘁𝗲𝘀: Fidelis Associates provides expert consulting and solutions at the intersection of energy, technology, and industrial operations. Our team specializes in helping businesses harness AI/ML, data analytics, and process optimization to drive efficiency, reduce costs, and enhance decision-making.
About the Author
Tim Weber is Vice President of Business Development & Project Management at Fidelis Associates, a professional services firm serving clients in the oil & gas industry. With over two decades of leadership experience in operations, lending, and consulting, Tim brings a strategic mindset and deep curiosity to the evolving intersection of industrial operations and emerging technologies. He is passionate about helping refineries and process plants adopt practical AI/ML solutions that drive efficiency, reduce risk, and create measurable value.
