Field Optimisation: Coordinating Inflow, Lift & Network Performance
- SigML
- Apr 21
- 4 min read
Effective field optimisation depends on understanding how production constraints emerge and interact across the full production system. In practice, this comes down to three tightly coupled components: inflow, lift, and the gathering network.
Most production inefficiencies arise not because these components are unknown, but because they are assessed independently, manually, or too infrequently to keep pace with live, dynamic field conditions.
For example, wellbore and network models may be updated each month and support full field reviews. On a daily basis, surveillance dashboards and tools help identify anomalies or production losses.
What if you could bridge the gap between the two?
While it may not practical to re-configure wellbore and network models each day, what if we ensure all of the observable system dynamics, across inflow, lift and gathering, are captured using advanced analytics?
That can open up a world of opportunity in driving greater efficiency and scalability in field optimisation.
The Three Building Blocks of Field Optimisation
At the well level, optimisation begins with reservoir inflow. Engineers need to understand the maximum achievable inflow from the reservoir into each well, each day, while respecting limits on drawdown, solids production, water cut, or other reservoir management objectives. Inflow capacity is not static, it evolves as reservoir conditions change and as wells respond to operational adjustments.
The second component is lift; artificial or natural. Even if inflow potential exists, production is constrained by what the well can physically lift to surface. This depends on natural lift conditions or artificial lift system performance, operating limits, equipment health, and current setpoints. Lift constraints often shift more rapidly than inflow, driven by changing fluid properties, interference, or equipment behaviour.
Finally, production is constrained by the gathering network. Wells do not produce in isolation. Header pressures, compression or processing capacity, and downstream constraints determine how much fluid the system can accept. A change at one well can propagate through the network and impact others, making local optimisation insufficient.
True field optimisation requires these three elements to be evaluated together, not sequentially or in isolation.
From Observation to Understanding
Modern fields generate vast amounts of production data, but raw data alone does not explain where constraints are forming or why performance is changing. Therefore, the first challenge is translating production trends into meaningful system insight.
Engineers need to be able to observe how inflow, lift, and network behaviour are evolving over time and to identify which part of the system is currently limiting performance. Doing this manually, by reviewing trends well by well and system by system, is time-consuming and increasingly impractical at scale.
Advanced analytical methods allow these conditions to be interpreted continuously. By learning normal behaviour and detecting deviations across inflow, lift, and network performance, the system can surface emerging constraints without relying on manual pattern recognition.
Using Predictive Scenarios to Improve Decisions
Observation alone is not enough. Optimisation requires understanding how the system is likely to respond to change.
Predictive scenario analysis enables engineers to evaluate questions such as whether current controller setpoints are still appropriate, how much additional production might be unlocked by adjusting lift parameters, or whether network constraints will negate gains at the well level. Running these scenarios daily, or more frequently, allows optimisation decisions to be proactive rather than reactive.
When predictive analysis is integrated across inflow, lift, and network models, engineers gain a clearer view of trade-offs and unintended consequences before changes are made in the field.
Building Efficient Optimisation Workflows
Even when opportunities are identified, value is lost if workflows are inefficient. Engineers need structured ways to review optimisation opportunities, act on them, and track outcomes over time.
Effective field optimisation systems support this by prioritising opportunities based on impact and confidence, providing context for why a recommendation exists, and logging decisions and results. This not only reduces time spent on analysis, but also builds institutional knowledge and confidence in the optimisation process.
Supporting Engineers with Nexgineer™ Field Optimizer
Nexgineer™ Field Optimizer is designed to help production teams work through these challenges in a systematic way. By coordinating inflow, lift, and network performance within a single analytical framework, it enables continuous surveillance, predictive scenario evaluation, and field-wide optimisation.
The focus is not on replacing engineering judgement, but on reducing the manual effort required to understand complex system behaviour. Engineers spend less time searching for constraints and more time deciding how best to respond.
As production systems become more interconnected and operational margins tighten, field optimisation increasingly depends on tools that can reason about the whole system dynamically. Coordinating inflow, lift, and network performance is no longer optional; it is the foundation of scalable, modern field optimisation.
If you're working through how scalable your current optimisation process really is, our Digital Maturity Assessment is a practical starting point. It benchmarks where your team sits today across surveillance, setpoint optimisation, and coordinated field-level decision-making - and helps identify where predictive modelling could be embedded into your existing workflows.
Take the Digital Maturity Assessment or Book a Field Optimizer demo to learn more.
