What challenges did Senex face before AI implementation?

Senex needed near real-time insights across well and operational data. The team wanted to predict pump failures before unplanned downtime occurred and improve reliability, efficiency, and emissions performance. Operators could not manually inspect every well daily or easily digitize legacy geospatial information from paper documents and images.

What was the impact of the AI solution?

Senex predicts pump failures 4x faster. The company reduced analysis time to under four minutes and handles 2.67 TB of data daily. Operators can prioritize high-risk wells each morning, supporting more informed maintenance planning. Digital projects enabled by Google Cloud are expected to realize financial benefits of up to $60 million over five years.

Source

GCP

Senex: predictive maintenance and real-time well insights using BigQuery ML and Cloud Vision AI

Use case typePredictive maintenanceUpdated Jun 13, 2026

Senex is an Australian natural gas producer that migrated to Google Cloud to unify operational and enterprise data for near real-time insights across gas wells. The company used BigQuery to centralize SCADA and gas sales data, and Cloud Vision AI to extract geospatial information from maps, handwritten notes, and PDF documents.

Organization: Senex
Industry: Energy & Utilities
Location: Australia
Published: January 2024

Reported outcomes

timeTime & speed

Strategic outcomes

Better decisions & insightPrioritized high-risk wells for maintenanceNew product / capabilityEnabled predictive pump failure detectionNew product / capabilityCreated a single source of truthSpeed & agilityNear real-time operational insights

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Predictive maintenance
2Operational analytics
3Document data extraction

Senex needed near real-time insights across well and operational data.
The team wanted to predict pump failures before unplanned downtime occurred and improve reliability, efficiency, and emissions performance.
Operators could not manually inspect every well daily or easily digitize legacy geospatial information from paper documents and images.

Senex built a data lake on Google Cloud to integrate disparate systems and create a single source of truth.
Live SCADA Historian data was ingested into tables that fed a TensorFlow machine learning model used to predict torque events and identify issues before they led to operational problems.
The company used BigQuery ML for predictive maintenance analysis and scheduled the model to run every 15 minutes across assets, with Cloud Vision AI extracting location information from images and scanned documents.

Technologies

BigQuery BigQuery ML Cloud Vision AI TensorFlow

Senex predicts pump failures 4x faster.
The company reduced analysis time to under four minutes and handles 2.67 TB of data daily.
Operators can prioritize high-risk wells each morning, supporting more informed maintenance planning.
Digital projects enabled by Google Cloud are expected to realize financial benefits of up to $60 million over five years.

Architecture

Senex built a Google Cloud data lake to connect SCADA Historian and business data sources. Live operational data was ingested into tables feeding a TensorFlow model for near real-time pump-failure prediction, with BigQuery used for large-scale analysis and Cloud Vision AI used to extract geospatial data from maps, handwritten notes, and PDF documents.

Implementation partners1

Servian

Sources & evidence1

Groundedness: 4/5

Primary source

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Customer evidence