Data Lake / Data Warehouse Modernization · Predictive Maintenance · Real-time Analytics
replace siloed warehouse, Hadoop, and SAP systems that could not combine financial, IoT, and OT data fast enough for self-service analytics, drilling decisions, forecasting, and M&A integration.
Reported impact
Expand Energy cut data-related M&A time and cost by 40%, saves more than 1,000 labor hours per year in one business unit, and gives engineers and operators faster self-service insight for forecasting, drilling, safety, and regulatory work.
Expand Energy standardized on Snowflake as the shared platform for business, IoT, and operational data across its shale operations, replacing a fragmented stack of legacy warehouse, HDFS, and SAP HANA systems. Data from business systems, SCADA feeds, well production, equipment telemetry, site details, and M&A source systems is centralized in Snowflake so engineers, finance teams, and field operations can work from a single governed foundation. Snowpark models and cloud-hosted machine learning workflows maintain dynamic well forecasts, while Cortex Analyst adds natural-language analysis over drilling activity and nonproductive time. Continuous Snowflake queries create digital-twin-style monitoring for 3,700 sites and surface anomalies such as corrosion, equipment risk, or downtime for the operations center. The same platform also co-locates data from acquired companies, accelerates cleansing and conversion, and enforces RBAC-based access, auditability, and regulatory reporting.
Business systems, SCADA feeds, well production data, equipment age, and site details land in Snowflake. Snowpark models and ML workflows maintain forecasts and drilling datasets, while continuous queries generate digital-twin-style monitoring and alerts for corrosion or downtime risk. Cortex Analyst serves natural-language answers to engineers, and the same governed platform co-locates acquired-company data for finance and accounting integration.
40% reduction in costs and time for merger and acquisition activities
Snowflake allows us to integrate comprehensive datasets seamlessly and leverage cloud-hosted machine learning models to maintain dynamic well forecasts
Snowflake reduces the need for specialized teams and uses consistent patterns for IoT, SAP and other data all in one place
The data combines with well production, equipment age and site details, creating a digital twin for each site
over 1,000 hours of labor can be saved annually
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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