Big Data in Oil and Gas Industry

ConocoPhillips' Data-Driven Revolution: How Big Data is Reshaping Oil and Gas

Avery Sinclair in Tech & Innovation September 2025 • 4 min read.

"Unlock hidden potential by transforming raw data into actionable strategies in energy sector"

In the fast-paced world of oil and gas, where razor-thin margins and complex operations are the norm, the ability to quickly and accurately analyze data can be a game-changer. Imagine an engineer who can spend weeks gathering data just to analyze well performance, or a team struggling to interpret a flood of information from various rigs. ConocoPhillips, a leading exploration and production company, has tackled this challenge head-on by developing a comprehensive data program.

Years in the making, ConocoPhillips' integrated data warehouses (IDWs) act as centralized data stores that allow staff across various disciplines—from operations and production engineering to reservoir engineering and geoscience—to access and analyze data efficiently. Recently scaled across the organization, these IDWs have led to tangible improvements, including better well uptime, reduced drilling times, optimized completion designs, and a deeper understanding of subsurface characteristics.

This data-driven transformation didn't happen overnight. It evolved from the initial struggles of managing vast amounts of data collected from different rigs and wells. The journey required vision, strategic implementation, and a commitment to integrating data across all business functions.

From Data Overload to Strategic Insight: ConocoPhillips' Journey

The need for better data utilization became apparent as ConocoPhillips accumulated massive datasets from diverse sources but struggled to extract meaningful insights. Patrick Stanley, data analytics lead in ConocoPhillips' Canada business unit, played a crucial role in addressing this challenge. In 2016, he helped establish a team "to act as a nucleus for data analytics and data integration," supporting the company's Montney Shale business and the Surmont steam-assisted gravity drainage bitumen recovery facility.

Initially, ConocoPhillips focused on consolidating data within specific business units. Canada, Alaska, and Norway were the first to integrate data into centralized repositories, starting in the early 2000s. "These repositories were focused on enabling functional workflow such as production allocation or seismic analysis—less on cross-functional integration," Stanley explained.

Enhanced Efficiency: Integration has dramatically improved technical efficiency and reduced the time needed to derive insights.
Direct Data Access: Users can pull business and technical data directly into analytical tools like Spotfire, minimizing personal data curation.
Shifted Focus: Analytics exercises have shifted from 80-90% data access and integration to 80-90% focused on analysis.

In 2014, the data repository approach expanded into a full-fledged IDW through the company’s Eagle Ford business. This expansion incorporated data from all functions while testing emerging commercial data warehouse technology. The original data repository approach leveraged application-specific databases with front-end tools like Microsoft Access, shifting towards more sophisticated and integrated systems.

Scaling Success: The Future of Data in the Energy Sector

The success in the Eagle Ford, where expanded data analytics capabilities helped ConocoPhillips drill 80% more wells per rig, recover 20% more hydrocarbons per well, and achieve an 8% increase in direct operating efficiency, demonstrates the transformative potential of big data. As John Hand, ConocoPhillips' technology program manager, noted, the company is shrinking its average drilling time per well in the Eagle Ford to 12 days from about a month, largely due to these data capabilities.

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Everything You Need To Know

How did ConocoPhillips transform its data management to improve efficiency and reduce costs?

ConocoPhillips revolutionized its operations by implementing Integrated Data Warehouses (IDWs). These centralized data stores allowed staff across various disciplines, such as operations, production engineering, and geoscience, to efficiently access and analyze data. This strategic shift led to tangible improvements, including better well uptime, reduced drilling times, optimized completion designs, and a deeper understanding of subsurface characteristics. The initial focus was on consolidating data within specific business units, with Canada, Alaska, and Norway being the first to integrate data into centralized repositories in the early 2000s. The expansion into a full-fledged IDW occurred in 2014 through the company’s Eagle Ford business.

What specific benefits did ConocoPhillips realize by adopting a data-driven approach, and what impact did this have on their operations?

ConocoPhillips experienced significant benefits from its data-driven approach. The integration of data dramatically improved technical efficiency and reduced the time needed to derive insights. Users gained direct access to business and technical data through tools like Spotfire, reducing personal data curation. This shift allowed analytics exercises to focus 80-90% on analysis, a major improvement. The success in the Eagle Ford, where expanded data analytics capabilities helped ConocoPhillips drill 80% more wells per rig, recover 20% more hydrocarbons per well, and achieve an 8% increase in direct operating efficiency, highlights the transformative potential. Furthermore, they shrank the average drilling time per well in the Eagle Ford to 12 days from about a month, due to these data capabilities.

How did ConocoPhillips' data journey evolve from initial data challenges to the implementation of advanced data solutions?

ConocoPhillips' journey began with the realization that they were accumulating massive datasets but struggling to extract meaningful insights. Initially, data was managed in application-specific databases with front-end tools like Microsoft Access. Patrick Stanley, data analytics lead in ConocoPhillips' Canada business unit, played a crucial role in addressing this challenge by establishing a team to focus on data analytics and integration. In 2016, this team supported the company's Montney Shale business and the Surmont steam-assisted gravity drainage bitumen recovery facility. The data repository approach evolved into the integrated data warehouses (IDWs) to incorporate data from all functions. This involved a shift towards more sophisticated and integrated systems.

What role did the Eagle Ford business unit play in ConocoPhillips' data-driven transformation, and what were the key outcomes?

The Eagle Ford business unit was pivotal in ConocoPhillips' data transformation. In 2014, the data repository approach expanded into a full-fledged Integrated Data Warehouse (IDW) through the company’s Eagle Ford business. This expansion incorporated data from all functions, testing emerging commercial data warehouse technology. The key outcomes in the Eagle Ford were remarkable: ConocoPhillips drilled 80% more wells per rig, recovered 20% more hydrocarbons per well, and achieved an 8% increase in direct operating efficiency. Furthermore, the average drilling time per well was reduced significantly, from about a month to 12 days.

How did the implementation of Integrated Data Warehouses (IDWs) impact the different teams and functions within ConocoPhillips, and what were the overall strategic advantages?

The implementation of Integrated Data Warehouses (IDWs) had a profound impact on various teams and functions within ConocoPhillips. Staff across disciplines like operations, production engineering, reservoir engineering, and geoscience, could efficiently access and analyze data. This centralized approach facilitated better well uptime, reduced drilling times, and optimized completion designs. The strategic advantages were significant, including a deeper understanding of subsurface characteristics, improved technical efficiency, and reduced time to derive insights. Direct data access into analytical tools such as Spotfire enabled a shift in focus from data access and integration to in-depth analysis, fostering more informed and effective decision-making across all areas of the company.