Improvement through collaboration

As the oil and gas industry continues to pursue operational excellence, safety, and sustainability, digital innovation has become a critical enabler for transformation. Within the downstream sector, leading companies are increasingly turning to intelligent automation, machine vision, and data-driven insights to improve efficiency, reduce operational risk, and meet environmental goals.

A prominent example of this shift is a strategic collaboration between Shell and Yokogawa. Shell has developed an advanced machine vision tool based on extensive experience in integrity management, remote inspections, and corrosion monitoring. Yokogawa is now integrating this capability into its platform with the aim of making it available to industrial facilities globally in the future. As deployments expand, insights from real operating environments will inform the joint evolution of machine vision capabilities, enabling continuous improvement through collaboration. This cycle supports safer, more efficient, and more reliable operations across the global energy and manufacturing industries, contributing to the advancement of industrial operations worldwide.

This article explores the essence of this collaboration, the technology and vision behind Operator Round by Exception (ORE), and the broader implications for the downstream oil and gas sector.

The context for change: digitalisation in downstream operations

Downstream facilities operate under complex, tightly regulated environments. Plant operators are responsible for performing regular rounds, visually inspecting gauges, valves, pumps, pipelines, and other equipment to ensure optimal and safe functioning. Traditionally, these inspections have been labour-intensive, prone to human error, and limited in their frequency and accuracy.

As energy markets face growing pressure to reduce emissions, improve energy efficiency, and maintain high safety standards, plant operators require more than incremental improvements. They need transformation. Digital innovation – particularly in the form of robotics, sensor networks, and AI-powered analytics – is enabling a shift from reactive to predictive operations.

Within this landscape, the collaboration between Shell and Yokogawa exemplifies how a legacy industry can adopt transformative technologies without compromising its core operational reliability.

A paradigm shift

The inefficiency of routine manual inspections is a key bottleneck in plant management. Traditionally, operators follow predetermined routes and checklists regardless of actual equipment status. This often leads to unnecessary work and delayed detection of abnormal conditions.

The ORE solution developed by Shell and integrated by Yokogawa addresses this problem. Using autonomous robots equipped with cameras and sensors, the system continuously monitors field assets. Images, thermal signatures, and audio cues are analysed in real time using AI-powered machine vision algorithms. These algorithms can detect deviations such as analogue gauge drift, valve misalignment, leakage, or unusual sounds, triggering alerts and providing contextual data to the control room.

This approach shifts operator activity from scheduled rounds to exception-based interventions. When no anomalies are detected, operators are not dispatched. When an issue is identified, ORE delivers actionable insight with supporting imagery and diagnostics, allowing targeted and timely responses...

This article originally appeared in the December 2025 issue of Hydrocarbon Engineering.

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Read the article online at: https://www.hydrocarbonengineering.com/special-reports/08122025/improvement-through-collaboration/