From Trend to Decision: Rethinking the Production Engineer’s Day

Ask most production engineers how they start their day and the answer is usually the same: reviewing production trends.

Well trends, lift performance, pressures, rates, alarms, exceptions. One review becomes five, five become ten. Over time, this becomes the default rhythm of daily surveillance.

It is a workflow built on experience and engineering instinct, and it works. That is, until fields grow more complex and asset counts increase, and the question becomes whether it’s the most efficient use of expertise and/or scales as well as it needs to.

The Reality of Daily Production Surveillance

Production engineers are expected to oversee increasingly complex fields with more wells, tighter operating constraints, and fewer resources. What has not always kept pace is the tooling that supports them.

Most surveillance still centres on manually reviewing historical trends to identify what looks “off.” Engineers search for subtle changes in behaviour, cross-reference multiple systems, and apply experience to distinguish signal from noise, and make judgement calls about what needs attention now versus what can wait.

This takes real expertise, and it produces results - the problem is not lack of skill or effort. It is that modern production systems generate more interactions, more variables, and more potential opportunities than any manual review cycle can reliably surface. Important optimisation windows can emerge and close between reviews. Subtle network-level interactions are difficult to detect by looking at wells individually.

Why Trend Review Becomes a Bottleneck

As asset counts grow, this approach becomes increasingly reactive. Engineers spend more time finding issues than evaluating options or improving performance.

This creates several challenges:

• Important opportunities can be missed because they do not present as obvious anomalies.

• Subtle interactions between wells and the network are difficult to detect manually.

• Engineering effort is spread thinly across many assets, rather than focused on the highest-impact decisions.

• Most importantly, the workflow does not scale. Adding more wells or more data increases workload almost linearly.

Shifting the Focus to Decision Review

A more effective approach is to invert the workflow — from reviewing raw data to reviewing a prioritised set of decisions already identified by the system.

In a decision-review model, the system continuously evaluates field conditions against objectives and constraints, identifies optimisation opportunities, and presents engineers with a prioritised list of actions to consider. Instead of "what do I need to look at today?", the day begins with "here are the decisions that matter most right now... what do I think about them?”

Engineering judgement remains central. The difference is their expertise is focused on the decisions that benefit most from it.

What Makes this Possible

Moving to a decision-review workflow requires more than “better dashboards”. It requires systems that can reason about production behaviour at a field level, in a way that aligns with engineering thinking and evaluates in the context of current objectives, constraints, and operating conditions.

That means integrating inflow, lift, and network performance into a single optimisation view. It also means running predictive scenarios to assess whether existing setpoints are still appropriate, and ranking opportunities by impact.

When this runs continuously and automatically, the daily workflow changes in a practical way: fewer hours spent scanning for issues, more time spent on the trade-offs and decisions that actually require engineering expertise.

What Changes in Practice

In practice, this changes how the engineering day is structured.

Teams that make this shift typically describe a similar set of outcomes. Decision cycles get shorter. Cognitive load eases. Consistency improves across the team because the same opportunities are visible to everyone, not just the engineer who happened to review the right well on the right morning.

There is also a clearer record of what was decided and why — something that tends to disappear in manual workflows and becomes more valuable as fields grow and teams change.

Supporting the Shift with Nexgineer™ Field Optimizer

Nexgineer™ Field Optimizer is built around this decision-review model.

The system automates routine surveillance, evaluates inflow, lift, and network performance within a single optimisation framework, and presents engineers with the prioritised optimisation opportunities aligned with field objectives.

Engineers remain in control; the result is not faster engineering for its own sake, but more effective use of engineering time.

Where Does Your Team Sit Today?

Moving from trend review to decision review is a practical, achievable step toward more scalable field optimisation; and one that reflects how modern production systems actually operate.

Every team is at a different point in this journey. Some are already running advanced analytics alongside their existing workflows. Others are earlier in the process, evaluating where predictive optimisation would have the most impact.

Wondering what stage your team and systems are at? If you are thinking about how to evolve your surveillance and optimisation approach, our Digital Maturity Assessment is a practical starting point. It helps you understand your current workflows, the stage of maturity your systems are in, and where there is the most opportunity to improve. Take the assessment here.