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

6 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

Hydro-Québec's deep neural network short-term load forecasting

Hydro-Québec deploys deep neural network models for real-time short-term electricity load forecasting, replacing legacy rule-based models that struggled to anticipate atypical demand behaviours during extreme weather events.

Use Case
AI-Powered Electricity Demand Forecasting
Tools
Internal Tools
Input
Historical electricity consumption data, real-time smart meter feeds, weather station data, and calendar and economic indicators.
Process
Deep neural networks trained on years of consumption and climate data continuously generate rolling short-term load forecasts, adapting dynamically to unusual demand patterns that confounded legacy statistical models.
Output
Continuous near-term electricity demand forecasts consumed by grid operators and generation dispatch teams for balancing and reserve management decisions.
Outcome
During a recent heatwave, the AI model correctly predicted the absence of a typical load decrease that the legacy model missed, avoiding the need for significant emergency operator corrections of 1,500MW.
Power Generation and Utilities
Operations

BHP's Azure Machine Learning for Copper Concentrator Optimisation

BHP implemented Azure Machine Learning to provide real-time operational recommendations for concentrator operations, improving copper recovery rates and generating measurable production uplift.

Use Case
AI-Powered Mineral Processing Optimisation
Tools
Azure Machine Learning
Input
Real-time sensor and operational data from the Escondida concentrator plant, including ore feed characteristics, reagent dosing, flotation cell performance and plant throughput metrics.
Process
Azure Machine Learning models analyse incoming process data and compare it to historical performance patterns, generating real-time recommendations for operators to adjust reagent dosing, grind size and flotation parameters to maximise copper recovery.
Output
Real-time operational guidance delivered to plant operators, recommending specific process adjustments to optimise concentrate grade and recovery within current operating conditions.
Outcome
BHP reports an operational uplift of $18.9 million from improved copper recovery rates at the Escondida site, leading the company to launch its first Industry AI Hub to scale predictive analytics across its global supply chain.
Mining and Commodities
Operations

Vestas's AI-Driven Turbine Health Monitoring and Predictive Maintenance

Vestas uses machine learning to continuously monitor wind turbine component health from control sensors across its global installed base, predicting wear and performance issues before they result in unscheduled downtime.

Use Case
AI Predictive Maintenance for Wind Turbines
Tools
IoT Sensors
Input
Continuous data streams from control sensors monitoring vibration, temperature, acoustics and performance parameters across Vestas turbines operating globally.
Process
Machine learning models trained on sensor data and maintenance records analyse real-time streams to identify subtle patterns indicative of bearing wear, blade imbalance, or component degradation with self-calibrating sensors that adapt to each turbine type and site.
Output
Condition health scores and specific maintenance recommendations provided to service teams, specifying which components require intervention and when, enabling condition-based rather than time-based servicing.
Outcome
Vestas reports the ability to predict failure signatures weeks in advance, reducing unscheduled downtime and optimising maintenance scheduling across turbines in over 80 countries.
Renewables
Operations

National Grid ESO's AI solar generation nowcasting

National Grid ESO partners with Open Climate Fix to deploy an AI solar nowcasting system that produces highly accurate short-term forecasts of solar generation output, enabling control room operators to reduce expensive backup gas plant kept on standby.

Use Case
AI Solar Generation Short-Term Forecasting
Tools
Open Climate Fix
Input
Real-time satellite imagery of cloud cover, historical solar generation data, and weather station feeds covering the GB electricity network.
Process
AI models train on satellite image sequences to track cloud movement and predict solar irradiance levels, translating outputs into generation forecasts for the transmission system operator control room.
Output
Accurate near-term solar generation forecasts updated continuously, consumed directly by grid balancing and dispatch teams.
Outcome
Improved solar nowcasting gives grid operators greater confidence to reduce idling gas reserve capacity, saving millions in balancing costs and reducing carbon emissions from unnecessary standby generation.
Power Generation and Utilities
Operations
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