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Predictive Maintenance in Oil Rigs

Energy & Utilities
case study

Business Impacts

15s

Near real-time inference of time series telemetry data

5M

Autoscaled data ingestion (in records per second)

Customer Key Facts

  • Location : North America
  • Industry : Oil & Energy

Problem Context

The customer is an American multinational energy corporation engaged in oil, natural gas, and geothermal energy and produces approximately 160 thousand barrels of oil per day across ten oil rigs, also known as facilities. Approximately 7 to 8 percent of facility downtime in their oil rigs could be attributed to malfunctioning compressors, leading to significant increases in resource and maintenance costs. They wanted to minimize facility downtime by using predictive tools to understand causes of such failures.

Challenges

 

  • Incredibly large volumes of input data
  • High throughput required in streaming data with milliseconds of lag
  • Customizing third-party application OpenTSDB to increase performance
Cloud Storage

Cloud Storage

OpenTSDB

OpenTSDB

Cloud Pub/Sub

Cloud Pub/Sub

Google's BigQuery

Google's BigQuery

Cloud ML

Cloud ML

Google Cloud Dataflow

Google Cloud Dataflow

Cloud Bigtable

Cloud Bigtable

Grafana

Grafana

TensorFlow

TensorFlow

Data Pipeline and Data Science Workflow to Streamline Processes, Make Predictions, and Visualize Telemetry Data

Solution

Quantiphi built an end-to-end data pipeline from the client's on-prem to Google Cloud Platform (GCP) that routes high throughput telemetry data to GCP APIs, data stores, and visualization platforms. In addition to building machine learning training and serving pipeline to identify patterns in the ingested data and inference, the data science workflow facilitates predictive maintenance.

Result

  • Near real-time ingestion of telemetry data
  • Minimized facility downtime
  • Increased cost savings
  • Improved safety of operators

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