Artificial Intelligence for Measurement

Everyone knows they need to be using AI, but when asked what problems it will solve, the answer is often unclear. AIM™ changes that by delivering tangible, real-time insights, predictive analytics, and intelligent recommendations that optimise operations, improve accuracy, and reduce costs.

AIM is a powerful new module within Profile, combining advanced artificial intelligence, machine learning and expert systems to deliver new insights into oil and gas custody transfer flow measurement.

AIM continuously monitors real-time data from the flow computer to uncover hidden patterns and trends in the underlying measurement data. This data, combined with calibrations and prove results, provides a larger picture of precisely what is happening and why. These insights enable operators to detect and address issues early, preventing them from escalating into serious problems that could impact operations and measurement integrity.

Its modular design ensures that new AI features and models can be introduced as the platform and technology evolve, keeping AIM at the forefront of measurement innovation.

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Aim™ at a glance...

  • Combines advanced artificial intelligence, machine learning and expert systems
  • Uncovers hidden patterns and trends in the underlying measurement data
  • Detects issues early, preventing them from escalating into serious problems that could impact operations and measurement integrity
  • Modular design

Aim™ is part of the Metrology™ suite of integrated products

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    Aim™ Features

  • Anomaly Detection

    AI excels at analysing vast amounts of flow measurement data to detect anomalies. Subtle deviations from expected patterns can be highlighted before they escalate into significant measurement errors or operational issues, allowing operators to address them proactively and maintain system integrity.

  • Flow Computer Validation

    Flow computers are central to custody transfer and measurement accuracy, and AI can validate their calculations by comparing live data streams with historical trends and digital twins. This ensures the outputs remain consistent and trustworthy, while automatically identifying misconfigurations, faulty logic or calculation errors that might otherwise go unnoticed.

  • Sensor Drift Detection

    Over time, sensors inevitably degrade and produce skewed readings. AI can identify this drift by recognising patterns that deviate subtly from expected baselines, even when those changes are too small for manual detection. This ensures measurement quality remains high and prevents systematic errors from creeping into custody transfer or reporting.

  • Consumption & Loss Forecasting

    By applying predictive analytics to consumption patterns and historical data, AI can forecast future usage and potential losses. This allows operators to anticipate imbalances, optimise product allocation, and reduce uncertainty in reconciliation processes, ultimately improving both financial accuracy and operational planning.

  • Predictive Maintenance

    AI can predict when calibration failures are likely to occur by analysing equipment behaviour, historical performance, and environmental conditions, so rather than relying on rigid time-based calibration cycles, AI can drive dynamic calibration schedules that are tailored to actual instrument performance and process conditions. This ensures calibrations are performed when needed, reducing operational costs while maintaining the highest levels of measurement confidence.

  • Smart Issue Filtering

    In environments where operators are overwhelmed by alarms and notifications, AI can filter and prioritise issues intelligently. By distinguishing between noise and genuinely critical events, it ensures that engineering teams focus their attention on the most impactful problems, reducing response times and improving decision quality.

  • Root Cause Analysis

    AI accelerates root cause analysis by correlating events, measurements, and operational data from multiple sources. It can uncover hidden relationships and sequences that lead to errors or failures, giving operators a clearer understanding of underlying causes and enabling more effective corrective actions.

  • Voice-Enabled SCADA Queries

    AI-powered voice interfaces allow operators to interact with SCADA systems naturally, retrieving flow data, event histories, or diagnostics through spoken queries. This speeds up access to critical information, particularly in the field, and enhances usability by reducing the need for manual navigation of complex dashboards.