Digitally transforming downstream operations

The entire energy industry is experiencing a profound shift, driven by digital technologies. Once characterised as hesitant to change, the sector is now embracing advanced analytics, artificial intelligence (AI), and enhanced monitoring platforms to optimise operations, improving safety, efficiency, and sustainability.

However, parsing out prescriptive process optimisation from time series data is an arduous task, and it often requires the expertise of expensive internal or external data science teams. By instead deploying self-service advanced analytics applications, companies’ internal engineers and frontline subject matter experts (SMEs) are empowered to leverage data science concepts themselves, equipped with point-and-click tools, and user-friendly interfaces. Combined with the process knowledge of internal experts, these applications transform data into insights, helping plant personnel optimise operations.

Challenges in downstream operations

Downstream oil and gas teams face several complex and interconnected process challenges. First, there is a constant battle against corrosion due to the harsh operating conditions prevalent in the industry, threatening infrastructure integrity. Simultaneously, operators must strive for optimal process efficiency and product yield, continuously adapting to fluctuations in feedstock quality and market demands. Regardless of environmental parameters and input process conditions, teams must maintain consistent product quality, which requires rigorous quality control measures and real-time monitoring throughout production.

The energy-intensive nature of downstream operations presents another significant challenge. Rising utility costs and growing environmental concerns compel a strong focus on power management and sustainability, requiring exploration and implementation of energy-saving technologies, process optimisation to reduce consumption, and investigation of alternative energy sources.

Furthermore, the inherent risks associated with handling flammable and hazardous materials demands robust safety and risk management systems, including comprehensive training programmes and proactive hazard mitigation strategies. Increasingly stringent environmental regulations add an additional layer of complexity, requiring meticulous monitoring, reporting, and continuous improvement efforts to minimise environmental impact and ensure compliance.

These modern needs in downstream oil and gas cannot be met without effective data management and integration strategies to manage the considerable volumes of data generated from numerous sources, such as sensors, process control systems, and laboratory analyses. Digital advanced analytics tools are essential for parsing out datasets in detail and generating actionable insights, which enables informed decision-making.

Driving downstream profitability requires supply chain optimisation from raw material procurement to finished product delivery. This can only be accomplished with sophisticated planning, execution, and coordination among stakeholders, which is difficult without modern digital tools, such as predictive analytics and supply chain management software.

Digital solutions

Fortunately, advanced analytics platforms are providing increasingly innovative tools, supporting digitisation efforts in this complex landscape to achieve enhanced safety, efficiency, and sustainability.

Sensors deployed throughout refineries and petrochemical plants provide a wealth of information relating to process or equipment performance, such as pressure, flow rate, temperature, and vibration data. Advanced analytics platforms continuously monitor these signals to detect signs of operational degradation. This real-time monitoring provides continuous visibility into process conditions, equipment performance, and product quality.

Advanced analytics platforms use this information to identify anomalous operations, subsequently triggering alerts and prompting swift responses to minimise potential risks and process disruptions. Particularly in complex processing units, rapid issue identification and resolution can be the difference between optimal performance and catastrophic failure.

After a while, operational baselines begin to take shape, facilitating predictive trends based on historical performance when similar conditions are encountered. This predictive behaviour enables proactive maintenance procedures, reducing unplanned downtime, repair costs, and safety risks.

For example, irregular vibration in a pump can signal the need for a root cause analysis, often uncovering deeper process issues and facilitating repairs to prevent failure. In this scenario, engineers can quickly identify deviations from expected performance and highlight opportunities for proactive maintenance by overlaying real-time process data on OEM pump curves.

Figure 1. A Seeq generated pump performance chart illustrates how actual operating data (orange points) aligns with reference pump curves (blue lines) at different speeds. The vertical axis tracks head pressure, the horizontal axis represents flow rate, and the timeline provides a time-based view to filter and analyse operational data in context.

AI-powered anomaly detection paired with predictive insights further enhances the ability to identify and mitigate subtle irregularities in the vast amounts of data generated that are easily missed by human operators. Flagging these anomalies in near-real-time produces faster and more accurate interventions, preventing costly downtime and ensuring product quality.

Maximising throughput rates in fouling service

Many heavy hydrocarbon and polymer production units experience significant rate constraints due to steady-state fouling or coking. The two remedies for this are offline maintenance — which requires prolonged unit shutdown and a significant hit to production capacity — and online procedures. The latter are typically less effective, but they require only a small rate hit or temporary quality upset. Heat exchangers and furnaces are prime candidates for this analysis because the internals of these fixed equipment types foul or coke universally throughout the industry.

Using an advanced analytics platform — such as Seeq — connected to process historian data and historical maintenance events, engineering teams can easily differentiate between runtime and maintenance periods, calculate fouling rates, and identify process variables with significant impact on fouling. By extrapolating current conditions and planned operations, the platform can calculate optimal maintenance windows, providing operations teams with significant lead time to coordinate resources and materials.

In ethylene production, for example, applying decoke procedures to mitigate rate limitations, and optimising the run time between decokes, can ensure operation at a higher average throughput rate over time. In the past, decoke timing was frequently based on estimating and calculating an optimal coil outlet temperature aligned with visual observation, but even the ideal decoke trigger point was singular and static.

As equipment ages and undergoes irreversible coking, degradation rates steepen, and models must be adjusted. The best models are dynamic, using online process data in addition to production and scheduling targets to dynamically calculate the optimal decoke frequency to meet planned production volumes sooner. Figure 2 shows an example of this, where a coil outlet temperature degradation model is optimised for the time between decokes. This optimal run length is overlaid against live operation as an ideal degradation and recovery profile.

Figure 2. Seeq is used to model the ideal run length between decokes in a fouling-prone ethylene production process. The purple line represents a predictive model that estimates how production can be maintained or extended with optimised decoke and maintenance intervals, while the green traces show actual production rates declining over each operational cycle due to fouling. The vertical highlights mark discrete run cycles, and the timeline at the bottom enables users to align these performance trends with maintenance events and operational targets.

Case study: Optimising refinery operations

One major North American refinery struggling with siloed data, limited operational visibility, reactive maintenance practices, and underutilised time-series data underwent a digital transformation initiative, centred around an advanced analytics platform and a standardised asset framework to address these limitations. The effort entailed data integration from various sources, providing a unified view of refinery operations.

The team employed real-time monitoring and AI-powered anomaly detection to enable proactive identification and resolution of operational issues, reducing downtime and improving safety. Advanced analytics helped optimise process parameters, increasing energy efficiency and product yields. Leveraging enhanced operational insights, the team improved its data consistency, sped up response times, implemented proactive maintenance practices, and enhanced operational efficiency.

Digital twins optimise operations and training

Digital twin technology creates virtual replicas of physical assets, such as refinery units or petrochemical plants, enabling dynamic simulations of real-world conditions. This further transforms planning, optimisation, training, and risk management, providing plant personnel with the ability to simulate various scenarios, from routine maintenance to emergency responses, without impacting live operations.

For example, simulating the effects of different feedstock compositions on a refinery unit allows for optimisation of process parameters and prediction of product yields. Digital twins can also be used for operator training, providing a safe and realistic environment for practicing procedures and responding to abnormal situations.

Downstream’s digital future

Successful digital transformation in downstream operations hinges on three interconnected elements — data, people, and extensible software platforms that combine monitoring, analytics, and AI — working together to unlock insights. Additionally, information technology (IT) and operational technology (OT) integration must occur across all plant processes, such as hydrocrackers, distillation columns, heat exchangers, and complex blending operations. These steps unify process data, reduce silos, and drive continuous improvement in production efficiency.

Furthermore, superimposing and analysing historical, real-time, and predictive data in advanced analytics platforms helps plant personnel identify opportunities for process improvements, such as adjusting operating parameters to reduce energy consumption, or optimising feedstock utilisation to maximise product output. Additionally, these digital platforms streamline environmental reporting and compliance processes, ensuring adherence to stringent regulations. This empowers teams to triage large volumes of event data, integrate with other enterprise systems, and leverage AI insights to streamline both site-level and corporate-level decision-making.

Advanced analytics, AI, and monitoring tools are driving the digital transformation throughout downstream oil and gas, addressing key challenges in maintenance, safety, efficiency, and sustainability while optimising performance, minimising risks, and achieving greater profitability. Digital adoption is no longer simply a luxury; it is essential for companies to remain competitive and ensure safe, reliable, and sustainable operations far into the future.

Read the article online at: https://www.hydrocarbonengineering.com/special-reports/16042025/digitally-transforming-downstream-operations/