Striking Data Gold: Expand Energy Taps into Snowflake to Scale Smarter

As the largest natural gas producer in the U.S., Expand Energy plays a crucial role in meeting the world’s growing energy needs. Amid an intricate web of 3,700 production sites across some of the most prolific shale regions in the U.S., the company harnesses the power of data to drive smarter, more efficient operations — transforming challenges into opportunities with every well drilled and every decision made. 

For the technology delivery team, the real challenge was overcoming the limitations of legacy systems. With operations spanning Haynesville and Marcellus Shales, and an ever-increasing demand for data from business systems and IoT data from equipment in the field, the company needed a modern data solution that could scale, facilitate self-service insights, and empower domain experts to make more-informed choices, all while minimizing complexity.

Expand Energy turned to Snowflake. With its scalability, ease of use, and ability to seamlessly manage traditional data warehouses and big data workloads, Snowflake provided the agility and efficiency Expand Energy needs. Crucially, Snowflake supported self-service analytics, allowing engineers and business users to access and analyze critical data without heavy reliance on IT, accelerating decision-making and revealing new opportunities to improve capital efficiency.

Before Snowflake, Expand Energy faced data architecture limitations. The company’s legacy data warehouse could not address use cases involving increasingly complex financial and OT datasets. As the company grew, the legacy architecture caused bottlenecks, with a single IT team managing all data requests. In 2014, Expand Energy adopted Hadoop for big data, but the system based on ​​Hadoop Distributed File System (HDFS) made real-time updates difficult and system uptime hard to maintain. 

Adding SAP HANA created another silo, making data unification harder. This led to inefficiencies, slowed decision-making and limited access to data. After evaluating several options, Expand Energy chose Snowflake. “Snowflake reduces the need for specialized teams and uses consistent patterns for IoT, SAP and other data all in one place,” says Ryan Goltz, Chief Architect at Expand Energy. The shift empowered employees to analyze data independently, enhancing agility and accelerating progress. “Reducing complexity creates clarity, and with Snowflake, everyone understands how things get done, driving value and reducing opportunity costs.”

Accurate production forecasting is critical for companies like Expand Energy to effectively plan and execute. Traditionally, engineering teams rely on “type curve modeling,” a labor-intensive process where engineers manually find and analyze analog wells and adjust for known variations in things like spacing, depletion and reservoir quality where exact analogs don’t exist to anticipate new well performance. This can be slow and cumbersome, requiring engineers to move data by hand across various tools and platforms.

Snowflake and Snowpark have enabled Expand Energy to streamline and automate its forecasting process with machine learning models. “Snowflake allows us to integrate comprehensive datasets seamlessly and leverage cloud-hosted machine learning models to maintain dynamic well forecasts that reflect changing operational and investment strategies,” explains David Gurney, Reservoir Engineering Advisor. This integrated, data-driven approach significantly improves strategic decision-making, optimizes development plans and drives operational efficiencies across assets.

In one business unit alone, the team estimates over 1,000 hours of labor can be saved annually. With this time savings, engineering and operations staff can now focus on exploring new technologies, improving reservoir understanding, testing innovative methods and enhancing recovery efficiency — all enabling Expand Energy to lead as a top operator in the field.

A capital-intensive process, drilling costs millions of dollars per well and requires precise execution. A single incident can result in material delays, safety concerns and financial loss. 

Expand Energy uses Snowflake to host real-time data and ML models for drilling activities, allowing the team to optimize the drilling rate of penetration, prevent equipment failures and enhance safety. Building on the foundation of real-time data ingestion and Snowpark data models, Cortex Analyst allows engineers to ask questions in natural language such as, “What were the top contributors to nonproductive time?” or “What is a summary of activities over the last 24 hours?” and get answers on the fly. 

Rather than sending personnel to monitor each of the 3,700 production sites, Snowflake enables Expand Energy to centralize data from operational systems and supervisory control and data acquisition (SCADA) systems. The data combines with well production, equipment age and site details, creating a digital twin for each site. Snowflake continuously runs queries to detect potential issues, such as tank corrosion, and alerts are sent to the operations center for investigation. This proactive approach reduces environmental risks and impacts, minimizes downtime and improves efficiency across all sites.

At Expand Energy, mergers and acquisitions are a key component to strategic growth. In any M&A activity, the goal is to quickly realize synergies that streamline operations and maximize value. For example, in managing royalty ownership, with over 1.2 million owners, tracking and consolidating ownership data during mergers is crucial for accurate payments and communications.

Snowflake helps first by enabling co-location of key systems from the merging company, which allows finance and accounting teams to create a unified picture of both companies. Then, this data can be used to expedite data cleansing, and conversion activities to reconcile and load information to target state systems. These repeatable processes provide a blueprint to accelerate the next M&A opportunity.

In transforming its operations through Snowflake, Expand Energy ensures compliance, auditability and secure data access across its infrastructure. Adopting DevOps processes has enhanced efficiency, and aligning existing data classifications to Snowflake’s role-based access control (RBAC) has improved accessibility, securing data based on sensitivity, not user role. This simplifies audits, boosts security and enables a self-service model, giving employees access to data without bottlenecks. It also helps Expand Energy meet regulatory requirements efficiently — a must in such a heavily regulated industry.

The reliable performance and operational stability required for Expand Energy’s most demanding use cases, including continuous operations in drilling and production, allows teams to focus on delivering value rather than managing infrastructure.

Expand Energy is continuing to explore new and emerging Snowflake features to deliver greater business value across the organization. The team is actively developing novel solutions using Cortex, such as building a full-scale, AI-powered drilling dashboard that can help better predict future performance and that is easy enough for stakeholders across the business to use, from drilling engineers to operations support specialists. Expand Energy also uses Snowflake to enhance environmental and regulatory reporting, aligning with Expand Energies role as an environmental steward.

Additionally, Expand Energy has a roadmap to mature its data science program by leveraging Snowflake’s machine learning and operations capabilities, targeting Snowflake as its data science and ML platform. In doing so, the team plans to streamline model management, deployment and performance at scale. The partnership between Snowflake and Expand Energy will enable the company to continue innovating and making data-driven decisions that matter.