Oil & Gas
USA
6
5 months
The client operates a natural gas processing and compression facility in the United States. Their plant handles inlet separation, gas compression, dehydration, amine treatment, condensate storage, export metering, and flare management — a dense operational environment generating continuous sensor data across dozens of assets.
The facility ran SCADA systems, PLCs, a local historian, and engineering spreadsheets. All of it was confined to the plant network.
The client had years of valuable plant data. None of it was ready for digital twin use.
The core problems were:
We built a secure, structured data infrastructure that took plant data from SCADA and historian systems and delivered it to a cloud-based AWS data lake — organized by asset, standardized for quality, and ready for digital twin consumption.
We established a secure pathway from the plant network to AWS. Litmus Edge was deployed at the OT/IT boundary to connect in read-only mode to the plant SCADA system and local historian. It collected approved operational tags, performed initial validation and data conditioning, buffered data when needed, and prepared telemetry for cloud ingestion.
From Litmus Edge, selected telemetry was securely forwarded to AWS IoT Core, using MQTT over TLS. AWS IoT Core handled device authentication, encrypted communication, and telemetry routing from the industrial edge layer into AWS. AWS IoT SiteWise organized incoming time-series data around physical assets — compressors, separators, pumps, valves — turning raw SCADA tags into asset-contextualized data that made sense to downstream systems.
Raw, cleansed, and curated data were loaded into a structured Amazon S3 data lake with three distinct zones. Raw ingestion preserved the source data as-is. The cleansed zone applied standardized timestamps, units, tag names, and quality flags. The curated zone held processed datasets shaped specifically for digital twin and analytics use.
We reviewed and exported SCADA and historian tag lists, then mapped each sensor tag to its corresponding equipment and process unit. The result was a clean asset hierarchy for the facility — connecting every data point to the physical asset it described.
AWS Glue handled cleaning, transformation, and preparation at scale. AWS Glue Data Catalog stores metadata, schemas, and table definitions so datasets are discoverable and queryable. Amazon Athena provided engineers with direct SQL access to data in S3.
We ran initial data validation to confirm completeness, accuracy, and expected sensor ranges across the tag population. Ongoing monitoring — built using Amazon CloudWatch — detected missing values, stale tags, abnormal readings, and ingestion gaps in real time.
Grafana was used as the engineering dashboard layer on top of curated AWS data. It allowed operations and engineering users to visualize selected SCADA trends, monitor data quality metrics, identify ingestion gaps, and review key asset signals before the data was consumed by future digital twin applications.
The client received a secure data foundation for digital twin implementation. Plant sensor and SCADA data were extracted, standardized, mapped to physical assets, and landed in AWS. This allowed the client to move from isolated plant data toward cloud-based analytics, digital twin scenarios, and future predictive maintenance use cases.
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