Near real-time inference of time series telemetry data
Autoscaled data ingestion (in records per second)
The client is an American multinational energy corporation engaged in oil, natural gas, and geothermal energy and is one of the largest oil companies in the world. They produce approximately 160 thousand barrels of oil per day across ten oil rigs, also known as facilities.
The client stated that 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. Thus, the client wanted facility downtime to be minimized by using predictive tools to understand causes of such failures.
Cloud Storage
OpenTSDB
Pub/Sub
BigQuery
Cloud ML
Dataflow
Big Table
Grafana
TensorFlow
Quantiphi built an end-to-end data pipeline from the client’s on-prem to Google Cloud Platform 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.
