2 real-life Business Process Automation examples in Energy, Resources and Utilities

2 real-life Business Process Automation examples in Energy, Resources and Utilities

Automation is different in energy, resources and utilities because companies manage safety-critical infrastructure, remote assets, environmental obligations, and complex reporting across control-room, field, and back-office teams.

The most useful automations reduce manual coordination and data friction in maintenance, compliance, trading, and finance workflows while preserving resilience, safety discipline, and clear operational accountability.

Business function

Industry

Saudi Aramco's Oil Field Monitoring and Optimisation

This expansive AI monitoring system allows Saudi Aramco to oversee 500 oil fields simultaneously by processing data from 40,000 sensors, resulting in a 15% increase in production through real-time performance optimisation and predictive maintenance.

Use Case
Oil Field Monitoring and Optimisation
Tools
Internal Tools & IoT Sensors
Input
Real-time data streams from sensors monitoring wells, flares, reservoirs, and pipelines.
Process
The system continuously ingests and cleanses sensor data to monitor the health of equipment and overall field performance. It identifies well performance trends and utilises big data analytics to conduct predictive maintenance, flagging potential equipment failures or pressure build-ups before they occur.
Output
High-resolution reservoir models, real-time well performance reports, and automated recommendations for extraction optimisation.
Outcome
15% increase in total oil production and enhanced operational safety through proactive risk management.
Oil and Gas
Operations

Octane Target Optimisation in Oil Refineries

This automation enables a major refinery to minimise octane giveaway by utilising Canvass AI’s predictive modelling and simulation tools, leading to a significant annual saving of US$10 million and a drastic reduction in manual data processing.

Use Case
Reducing Octane Giveaway
Tools
Canvass AI
Input
Historical and real-time process data from blending operations, including feed compositions, temperatures, and flow rates.
Process
The system employs a prediction simulator to foresee product outcomes using diverse data sets without affecting the underlying AI model. It allows for 'what-if' analyses to experiment with process changes and uses machine learning to predict octane levels based on blend recipes and operating conditions.
Output
Accurate predictions of octane levels, recommended blend ratios, and dashboards comparing predicted versus actual octane performance.
Outcome
US$10 million annual savings and an 80% reduction in data wrangling time.
Oil and Gas
Operations
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