AI/ML in Oil & Gas Refining: Part 2 Operations Improvement
Continuing from the previous discussion on digital transformation in refineries, this part delves into how AI is driving improvements in refining operations. We explore six key areas: process optimization, advanced process control, energy efficiency, cost savings, real-world use cases, and implementation strategies. Each section highlights practical applications and benefits of AI in refining, backed by industry insights and research findings.
AI-Driven Process Optimization
AI techniques are being applied to refining process control to enhance yields and product quality. By analyzing vast process data in real-time, AI can fine-tune operating parameters far faster than human operators. This leads to more stable operations, higher throughput, and fewer off-spec products. Notably, AI algorithms (like neural networks and reinforcement learning) excel at handling complex, nonlinear refinery processes that traditional models struggle with.
Crude Distillation: In the crude unit, AI helps adjust cut points and heater settings dynamically to maximize distillate yields for varying feedstocks. Research shows that variability in crude oil composition causes substantial energy waste if not managed; AI models can adapt distillation settings on the fly to maintain efficiency. This results in more consistent output quality and reduced energy consumption per barrel distilled.
Fluid Catalytic Cracking (FCC): FCC units benefit from AI optimization by continuously tweaking catalyst circulation, regenerator air flow, and reactor conditions to boost gasoline and propylene yields. Closed-loop AI systems have demonstrated improved FCC high-value product yield and associated economic gains by continuously learning and adjusting to unit behavior. In practice, AI-driven control of FCC can respond to feed changes or catalyst aging more effectively than static models, sustaining optimal conversion and selectivity.
Hydroprocessing (Hydrotreating/Hydrocracking): AI models in hydroprocessing units help maintain product quality (like sulfur content) while minimizing hydrogen usage and energy. By predicting catalyst activity decline and feed impurities, AI can optimize reactor temperature and pressure to extend catalyst life and prevent quality giveaway. Even energy-intensive processes like hydrocracking see significant performance improvements (reports suggest up to 30% gains in efficiency or throughput with AI precision adjustments). This means more output of diesel or jet fuel at required specs, for the same or lower energy input.
In all these cases, AI-driven process optimization not only maximizes yield of high-value products but also tightens quality control. Advanced machine learning models can correlate subtle changes in sensor readings with product quality outcomes, enabling proactive tweaks to avoid off-spec production. As a result, refiners achieve higher yields, more consistent product quality, and improved unit stability, all of which enhance profitability.
Advanced Process Control (APC) with AI
Advanced Process Control has long been used in refineries to stabilize operations and push closer to constraints. Now, AI is taking APC to the next level through predictive and adaptive control strategies. AI-enhanced APC combines traditional model-predictive control with machine learning algorithms that update and improve the process model in real time. This allows for more accurate setpoint adjustments and better handling of disturbances (like feed changes or equipment fouling) before they impact the process.
AI-driven APC systems continuously analyze process data and learn optimal control moves, effectively creating a self-tuning loop. For example, an AI controller can anticipate how a change in one unit will affect downstream units and adjust accordingly, something that manual operators or static models may not catch in time. This predictive capability leads to real-time optimization across the refinery. Key benefits reported include:
Reduced Energy Consumption: AI controllers often find more efficient operating conditions. For instance, next-generation APC solutions have achieved about 10% reduction in energy usage in process units by optimizing heater duty, reflux ratios, and other energy-intensive inputs. By minimizing excess heat or pressure, they cut fuel consumption and associated costs.
Increased Throughput & Yield: Pushing constraints safely means more feed can be processed. AI-based APC has delivered throughput increases of ~2–5% and yield improvements on the order of a few percent. While these may seem modest, in a large refinery even a 2% throughput gain can translate to significant additional barrels processed per day. Similarly, a few percent higher product yield (e.g. more gasoline from the same feed) directly boosts margin.
Improved Stability & Quality: AI can reduce process variability by continually correcting deviations. Companies have seen over 50% reduction in product quality standard deviation on key specifications under AI-enhanced control. This means products consistently meet targets with minimal “giveaway” (e.g. octane or sulfur slightly better than spec), which avoids waste. Smoother operations also reduce wear on equipment.
Moreover, AI-enabled APC systems are better at multi-unit coordination. They can perform refinery-wide optimization, adjusting multiple units in concert for an overall economic objective (like maximizing refinery profit or minimizing cost). This holistic optimization in real time is often termed Real-Time Optimization (RTO), and AI greatly accelerates its speed and scope. By integrating AI with APC, refineries achieve closer to true optimum operation 24/7, beyond what conventional control could do.
Energy Efficiency & Sustainability
Energy efficiency and environmental performance are top priorities in modern refining, and AI is emerging as a powerful tool to advance sustainability goals. Refineries consume large amounts of fuel and power; even small percentage improvements in energy efficiency can yield big savings and emissions cuts. AI contributes to sustainability in several ways:
Emissions Reduction: AI systems help reduce both greenhouse gas (GHG) and criteria pollutant emissions. For example, AI-driven burner controls and combustion optimizers can minimize excess oxygen and improve fuel-air mix in furnaces, cutting CO₂ emissions and fuel use simultaneously. On a broader scale, adopting AI across energy systems could reduce global GHG emissions by around 4% in 2030 (equivalent to 2.4 gigatons of CO₂) according to a PwC analysis. In refineries, one practical application is using AI for flare management: smart algorithms predict flaring events and adjust operations to reduce flaring frequency and volume, thus curbing methane and CO₂ releases. Similarly, AI can optimize hydrogen production and other energy-intensive processes to shrink the refinery’s carbon footprint.
Fuel and Energy Optimization: Refineries often have their own power plants or steam systems, and AI can optimize these utility operations for peak efficiency. Machine learning models continuously analyze energy consumption patterns to identify inefficiencies and recommend adjustments. For instance, AI might suggest re-routing streams or changing pump schedules to avoid running too many pumps at partial load. By some accounts, AI techniques can improve energy efficiency in industrial operations by 10% to 40% through such optimizations. In practice, refineries have reported up to 20% reductions in fuel gas usage for process heaters after implementing AI optimizers that learn the best firing patterns. These savings not only cut cost but directly translate to lower emissions.
Carbon Footprint & Environmental Compliance: Compliance with emission regulations (SOx/NOx, CO₂ caps, etc.) is critical. AI-based predictive modeling is helping refineries stay within limits. Predictive emissions monitoring systems (PEMS) use AI to forecast emissions levels based on current operating conditions. This allows operators to take corrective action before an emission limit is breached. For example, an AI model might predict that running a certain crude blend will cause SO₂ emissions to approach the permit limit; the refinery can then adjust the sulfur removal process or blend in sweeter crudes proactively. Such foresight helps avoid regulatory violations and fines. ABB reported that model-driven AI systems enable plants to predict emissions in advance and adjust operations before any compliance limits are exceeded. Beyond compliance, AI is aiding carbon reduction initiatives like Carbon Capture, Utilization, and Storage (CCUS) by optimizing capture unit performance and evaluating the best operating windows for carbon capture when energy is cheapest or most abundant.
In summary, AI is becoming indispensable for energy management in refineries. It drives smarter decisions that reduce waste, whether that's fuel, power, or raw materials. This not only improves the refinery's environmental footprint (lower emissions per barrel) but also supports global sustainability efforts by significantly cutting the energy intensity of refining operations.
Cost Savings & Efficiency Gains
One of the most compelling aspects of AI in refining is the potential for major cost savings and efficiency gains. Refineries operate on thin margins, so improvements that boost output or reduce expenses can have a big financial impact. AI contributes to cost reduction in multiple dimensions:
Operational Cost (OPEX) Reductions: AI helps lower ongoing operating expenses by optimizing resource usage and minimizing losses. For example, by reducing energy consumption (as noted earlier), AI directly cuts fuel costs – often one of the largest OPEX items for a refinery. AI-driven predictive maintenance is another OPEX saver: By anticipating equipment failures, AI allows for maintenance to be done just-in-time rather than after a breakdown. This can reduce unplanned downtime by 30–50%, which not only avoids lost production but also prevents expensive emergency repairs. Fewer unexpected outages mean more stable operation and less money spent on fixing problems. According to industry experts, AI is now a key technology for optimizing operations, enhancing efficiency, and reducing costs.
Throughput and Yield Improvements: As discussed in previous sections, AI can push processes closer to their optimal performance. Even a small uptick in throughput (feed rate) or yield (output fraction of high-value products) translates into more saleable product for essentially the same fixed cost – improving the refinery’s unit economics. For instance, advanced AI control has been shown to deliver a 2–5% increase in throughput and ~3% higher yields on average in refinery units. These gains directly increase revenue without proportionally increasing costs, thus improving profit margins per barrel. In fact, some refineries using closed-loop AI optimization have reported margin boosts on the order of $0.50 per barrel processed. In an industry where margins might be only a few dollars per barrel, an extra $0.50 is significant.
Reduced Quality Giveaway and Rework: AI’s precision in maintaining product quality has cost benefits too. By keeping products right at spec, refineries avoid the cost of quality giveaway (extra processing or higher-quality inputs than necessary) and any reblending or reprocessing of off-spec batches. For example, if gasoline octane is consistently controlled to just meet the target rather than overshooting by 1-2 points, the refinery can save on expensive blending components. Over time, minimizing these inefficiencies yields notable savings.
Supply Chain and Scheduling Efficiency: Some refining operations are using AI for planning and scheduling (though this borders on refinery planning domain). AI can optimize crude purchasing and product distribution by analyzing market data, which reduces feedstock costs and improves supply chain efficiency. While our focus is on operations, it's worth noting these peripheral applications contribute to overall cost reduction as well.
In terms of industry benchmarks, the cumulative effect of AI-driven improvements can be substantial. A study by AspenTech on AI-infused APC reported benefits like those mentioned (energy minus 10%, yield plus 3%, etc.) which can equate to millions of dollars saved annually for a medium-sized. Similarly, real-world deployments (as highlighted below) have demonstrated multi-million dollar savings through a combination of higher throughput, lower energy use, and less downtime. These quantifiable benefits make a strong business case for AI in refining – often yielding a high return on investment (ROI) and a payback period of just months to a couple of years for AI projects.
Real-World Applications
AI in refining is not just theoretical; there are numerous real-world deployments showcasing its value. While specific company names can’t always be disclosed, the following examples illustrate how refineries are leveraging AI today:
Closed-Loop Optimization in FCC: One refinery implemented a closed-loop AI system on its FCC unit that directly controls operating settings in real time. The AI continuously learns from the process and adjusts parameters like reactor temperature and catalyst circulation. As a result, the refinery significantly improved its FCC gasoline yield and profitability. In one case, deploying such an AI optimization led to a notable increase in high-value product yield along with greater economic benefits for the unit. This example highlights how AI can unlock additional performance even in a unit that was already under advanced control.
Crude Distillation Adaptive Control: Another refinery faced challenges with highly variable crude feeds that upset the distillation process. They deployed an AI-driven advisory system for the Crude Distillation Unit (CDU). This AI model predicts how changes in crude quality (like API gravity, sulfur content) affect distillation and then recommends optimal adjustments to furnace firing and cut point settings. The system helped the CDU handle feed uncertainty with minimal energy loss or yield drop. Essentially, the AI approach maintains energy-efficient operation under uncertain feed composition, saving significant energy that would otherwise be wasted. This allowed the refinery to process opportunity crudes (cheaper feedstocks) while keeping the unit stable and efficient.
Energy Management and Fuel Savings: A large refinery complex used AI to optimize its fuel gas system and fired heaters. By analyzing heater performance data, the AI identified opportunities to lower firing rates during certain periods without impacting throughput. Over time, this led to roughly a 20% reduction in natural gas consumption for the same processing rates. The cost saving from this fuel efficiency gain was substantial (into the millions annually) and also reduced the refinery’s CO₂ emissions. This case demonstrates AI’s dual benefit for cost and sustainability.
Predictive Maintenance Avoiding Downtime: Refineries are also applying AI for equipment health monitoring. For instance, an AI-based predictive maintenance platform was rolled out to monitor critical compressors and pumps in a refinery. The AI algorithms learned normal vibration and temperature patterns and could detect subtle anomalies indicating early-stage problems. On several occasions, the system gave advance warning of an equipment issue (such as a compressor bearing failure) days or weeks before a failure, allowing maintenance to be scheduled proactively. This proactive approach prevented unexpected shutdowns. Considering that an unplanned refinery-wide outage can cost on the order of hundreds of thousands of dollars per day, the AI system paid for itself quickly by preventing downtime. (Industry analyses show predictive maintenance driven by AI can cut unplanned downtime by as much as 50%, which this example corroborates.)
These examples scratch the surface of AI applications in real refining environments. Other deployments include AI-assisted yield optimization tools in planning departments, machine vision for automated quality inspection of products, and robotics/AI combos for faster inspections of equipment (like tank and vessel inspections using drones). The growing number of successful case studies is building confidence in AI within the industry. Each success – whether it's higher margin, lower cost, or improved safety/environmental performance – paves the way for broader AI adoption in refining.
Implementation Challenges & Strategies
Adopting AI in refining operations is not without its challenges. Refineries are complex organizations with established processes and cultures, so introducing AI requires careful strategy. Common barriers to AI adoption in refining include:
Data Quality and Silos: AI is only as good as the data fed into it. Many refineries struggle with data that is fragmented across different systems or of poor quality (noisy, incomplete). A significant barrier to deploying AI in manufacturing (including refining) is data quality and fragmentation; AI systems depend on high-quality, unified data to produce reliable insights. Overcoming this may involve investing in data infrastructure – historians, data lakes, and cleansing processes – to ensure models have accurate and timely information.
Legacy Systems Integration: Refinery control systems (DCS, PLCs, etc.) are often legacy platforms. Integrating new AI tools with these existing systems can be technically challenging. There may also be cybersecurity concerns when connecting AI software to critical plant control networks. Companies need strategies to securely interface AI engines with operational tech, sometimes using middleware or digital twin setups to test AI recommendations before applying to the real plant.
Workforce Skepticism and Fear: Perhaps the biggest hurdle is the human factor. Operators and engineers may be skeptical of AI or concerned that it will replace their roles. A limited understanding of AI’s purpose and potential is a major barrier, often leading to skepticism on the plant floor. Employees often fear that AI might make their jobs redundant, creating resistance to its adoption. This cultural resistance can derail AI initiatives if not addressed.
Lack of Skilled Personnel: Implementing and maintaining AI solutions requires data scientists and engineers with relevant expertise. The oil & gas industry faces a skills gap in this area, as traditional engineering teams may not have experience with machine learning. Hiring or upskilling staff is an upfront challenge.
Change Management: Introducing AI means changing how decisions are made – shifting some decision-making from humans to algorithms. Without proper change management, this can lead to confusion or misuse of the AI tools. There might also be initial setbacks or learning curves where AI suggestions are not perfect, which, if not managed, could reduce trust in the system.
To overcome these barriers, refineries are employing several best practices for AI integration:
Start Small with Quick Wins: Rather than a big-bang approach, successful companies often start with pilot projects focusing on specific, high-impact use cases. Implementing a pilot program can generate quick wins and build confidence among stakeholders. For example, they might deploy AI on one unit or problem area (like optimizing a single distillation column) and demonstrate tangible benefits in a short time. Early adopters involved in the pilot become champions for the technology.
Involve the Workforce & Build Trust: It’s crucial to involve operations staff from day one. By including operators and engineers in the development and rollout of AI tools, they gain understanding of how the AI works and are more likely to trust its recommendations. Training programs and workshops can demystify AI, showing it as a tool to augment their expertise, not a black box to fear. Reframing AI’s role helps reduce fear—employees begin to see efficiency gains not as a threat to job security but as freed-up time to focus on higher-level tasks. Leadership should communicate clearly that the goal is to assist workers (e.g. automating routine calculations) so they can concentrate on safety and innovation.
Executive Support and Strategy Alignment: Strong leadership backing is key to drive the AI agenda and allocate necessary resources. AI projects should be tied to clear business objectives (cost reduction, margin improvement, safety metrics) that leadership cares about. This alignment ensures that AI adoption isn’t just an IT experiment but a core part of the refinery’s operational excellence strategy. Executives can also set the tone for an innovation culture that welcomes data-driven decision making.
Invest in Data and IT Infrastructure: Given the data challenges, investing in modern data infrastructure is a foundational step. This means establishing a robust pipeline for collecting, storing, and processing data from all relevant sources (sensors, lab results, maintenance logs, etc.). Many refineries are creating centralized data lakes or employing IIoT platforms to break down silos. Cleaning and validating historical data before feeding it to AI models is another best practice – sometimes requiring dedicated data engineering teams. Essentially, data readiness is critical for AI success.
Scale Gradually and Integrate: After a successful pilot, refineries should scale AI in phases – e.g., move from one unit to multiple units, and eventually to an integrated refinery-wide AI system. Each step brings new lessons and improvement to the models. It’s also wise to integrate AI solutions with existing workflows incrementally. For instance, initially the AI might function as an advisory system (providing recommendations that operators can accept or reject). Once trust and reliability are established, it can move to closed-loop control. This phased integration helps the workforce adapt and the organization to absorb changes smoothly.
Continuous Learning and Improvement: AI adoption is not a one-time project but an ongoing journey. Refinery AI models should be continuously updated with new data, and their performance should be regularly reviewed. Setting up a dedicated team to monitor AI outcomes and retrain models as needed ensures the technology keeps delivering value. Organizations that embrace a culture of continuous improvement – using AI insights to constantly refine operations – will reap the most benefit in the long run.
By acknowledging the challenges and proactively managing them, refineries can successfully embed AI into their operations. The key is to combine technical readiness (data, tools) with human readiness (skills, mindset). Those that do so are already seeing AI become a game-changer in operational performance, while those that don’t risk falling behind in efficiency and competitiveness.
Conclusion & Next Steps
AI is increasingly at the heart of refining operations improvement—from optimizing complex processes and enhancing control to saving energy and costs. As we’ve seen, AI applications can boost yields, cut waste, and help meet sustainability targets, all of which contribute to a more profitable and future-ready refinery. Real-world examples underscore AI’s potential, but achieving those gains requires more than just technology. Refineries must address organizational and data challenges while bringing their people along on the AI journey.
At Fidelis Associates, we understand that each 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 entire 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.
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Coming Up Next: AI in Health, Safety, and Environmental (HSE) Applications
In our next installment, we’ll explore how AI is transforming HSE—from protecting workers on the plant floor to monitoring emissions, detecting leaks, and ensuring regulatory compliance. You won’t want to miss how these technologies are reshaping the safety and sustainability landscape of modern refineries.
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.
