Field Optimisation: Traditional vs. Modern Approaches

Field optimisation has always been an integral part of production engineering. This objective hasn’t changed in modern oil & gas production, but the environment within which engineers operate has. Fields are more interconnected, constraints shift faster, and teams are expected to manage larger asset bases with fewer resources.

In this context, traditional surveillance and optimisation approaches are increasingly under strain. While they remain valuable in certain scenarios (e.g. network modelling, field development planning), when it comes to routine production surveillance and optimisation, they were not designed for the pace and complexity of modern, data-intensive operations. Modern optimisation approaches have emerged to address these gaps; not by replacing engineering fundamentals, but by changing how optimisation is executed day to day.

The Traditional Approach to Field Optimisation

Traditional field optimisation is typically built around assessing wellsites individually. Engineers assess well performance through trend analysis, compare current conditions to historical benchmarks, and apply adjustments based on experience and established rules.

This approach relies heavily on static or semi-static models. Inflow, lift, and network behaviour are often analysed separately, using different tools and assumptions. Network studies might be run monthly or quarterly, while well-level tuning happens on a more frequent but reactive basis.

The strength of this approach is control. Engineers understand the logic behind decisions, changes are deliberate, and risk is managed conservatively. However, the limitations become apparent as field complexity grows. Manual analysis does not scale well, opportunities can be missed between review cycles, and optimisation tends to focus on local improvements rather than system-level outcomes.

Most importantly, traditional workflows are time-intensive. A large portion of engineering effort is spent finding issues rather than evaluating options and acting on them.

The Modern Approach to Field Optimisation

Modern field optimisation shifts the emphasis from periodic well reviews by exception to continuous evaluation, using analytics-driven methods to capture more of the integrated production system dynamics, with less manual effort from production teams in configuring rules or adjusting assumptions to match evolving conditions.

In a modern approach, inflow, lift, and network dynamics are evaluated together. Surveillance is automated, as all well and network trends are dynamically profiled for statistical deviations, detecting emerging constraints and identifying optimisation opportunities.

Predictive scenario analysis is central to this approach. Rather than relying solely on historical comparisons, engineers introduce physics-led machine learning models to predict how the system is likely to respond to changes in setpoints, operating strategies, or network conditions. This enables more proactive decision-making and reduces the risk of unintended consequences. Shifting engineering time from reviewing trends and dashboards each day to building expertise in python-based programming to characterise their system dynamically with data-driven techniques applicable to their data and systems.

Modern optimisation also places strong emphasis on workflow management. Optimisation opportunities should be prioritised before reviewing surveillance insights, surveillance insights should be reviewed by exception, and all decisions should be logged, such that outcomes and learnings are tracked over time. This creates a feedback loop where models and recommendations improve over time.

Key Differences in Practice

The most noticeable difference between traditional and modern optimisation is where engineering time is spent. Traditional approaches consume time in data review and issue identification. Modern approaches automate much of this work, allowing engineers to focus on evaluating trade-offs and making decisions.

Another key difference is cadence. Traditional optimisation happens in discrete cycles, while agile optimisation runs continuously at a pace aligned with operational dynamics. This makes it better suited to managing transient behaviour, short-lived opportunities, and rapidly changing constraints.

Finally, there is a difference in scale. Traditional methods work well for small asset bases or stable systems. Modern approaches are designed to scale across large, interconnected fields where manual optimisation becomes impractical.

Where Traditional Approaches Still Matter

It is important to note that modern optimisation does not eliminate the need for traditional engineering analysis and simulation. Detailed studies, offline modelling, and deep diagnostics remain essential for understanding complex issues and designing long-term strategies.

The difference is how these tools are used. In a modern framework, traditional analysis supports and refines continuous optimisation rather than replacing it.

Enabling Modern Optimisation in Practice

Moving toward modern field optimisation requires more than new algorithms. It requires systems that integrate data, analytics, optimisation logic, and engineering workflows in a way that is transparent and usable.

Tools like Nexgineer™ Field Optimizer are designed to support this shift by coordinating inflow, lift, and network performance within a single framework, automating surveillance, and enabling predictive scenario evaluation at a field level.

Field Optimizer provides a missing operational layer between surveillance, control, and offline network modelling - turning field data into prioritised, predictive optimisation decisions that engineers can use every day.

The goal is not to optimise faster for the sake of speed, but to make optimisation a routine, scalable part of daily operations.

Looking Ahead

As production systems continue to grow in complexity, the gap between traditional and modern optimisation approaches will widen. Teams that rely solely on manual, periodic optimisation will struggle to keep pace with changing conditions. Those that adopt agile approaches will be better positioned to respond quickly, prioritise effectively, and sustain performance improvements over time.

Field optimisation is no longer just an engineering task. It is an operational capability and, increasingly, an agile one.

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Curious to see how this applies to your current operations? At SIG Machine Learning, we partner with oil & gas teams to implement practical, engineering-led optimisation tools that integrate into real workflows. We'd be happy to show you what modern field optimisation looks like in practice.