Oil rig integrated with data streams

Unlock Your Data Goldmine: How ConocoPhillips Revolutionized Big Data in Oil & Gas

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

"From data chaos to optimized drilling: Discover how ConocoPhillips leveraged integrated data warehouses to transform operations and boost efficiency."

Imagine an engineer spending weeks just gathering data for basic well performance analysis. At ConocoPhillips, that's a thing of the past. Thanks to a forward-thinking program years in the making, their staff can now dive straight into analysis, bypassing the time-consuming process of data collection.

The key to this transformation? Integrated data warehouses (IDWs). These centralized data stores, recently scaled across the organization, serve as a hub for staff involved in operations, production engineering, well construction, reservoir engineering, and geoscience.

This deployment has led to impressive results, including improved well uptime, decreased drilling times, optimized completion designs, and a better understanding of what lies beneath the surface, according to the company. But how did ConocoPhillips achieve this big data breakthrough?

The Big Data Bottleneck: From Gobs of Data to Actionable Insights

The challenge began with a familiar problem: an overwhelming influx of data from various rigs and wells, but a struggle to actually use that data for meaningful interpretation. Patrick Stanley, data analytics lead in ConocoPhillips' Canada business unit, explains that this is a common hurdle for companies venturing into big data analytics.

In 2016, Stanley played a pivotal role in establishing a dedicated team. This team acted as a 'nucleus for data analytics and data integration,' specifically supporting the company's Montney Shale business and the Surmont steam-assisted gravity drainage bitumen recovery facility.

Canada, Alaska, and Norway became the pioneering business units within ConocoPhillips to consolidate data into a centralized repository.
This initiative, dating back to the early 2000s, laid the groundwork for the IDW.
These repositories initially focused on enabling functional workflows like production allocation or seismic analysis, with less emphasis on cross-functional integration.

In 2014, the data repository approach evolved into a full-fledged IDW, spearheaded by the company's Eagle Ford business. This expansion incorporated data from all functions while simultaneously testing emerging commercial data warehouse technology. Stanley notes that this integration across functions has dramatically improved technical efficiency and reduced the time needed to derive insights from the data. Users can now pull business and technical data directly into analytical tools like Spotfire, minimizing what he calls 'personal data curation.'

The Future of Data in Oil & Gas: Agnostic and Adaptable

ConocoPhillips' journey highlights the transformative power of big data in the oil and gas industry. By focusing on data integration and developing internal analytics capabilities, they've created a model that's both effective and adaptable. This approach allows them to remain vendor-agnostic, embracing the most efficient technologies as they emerge, ensuring they stay ahead in a rapidly evolving landscape. The ability to leverage big data is 'going to be huge going forward because unconventionals are a big data problem.'

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Everything You Need To Know

What are integrated data warehouses (IDWs) and how are they used?

Integrated data warehouses (IDWs) are centralized data stores that serve as a hub for staff involved in operations, production engineering, well construction, reservoir engineering, and geoscience. ConocoPhillips uses IDWs to consolidate data from various rigs and wells, enabling improved well uptime, decreased drilling times, optimized completion designs, and a better understanding of subsurface characteristics. Prior to IDWs, engineers spent weeks gathering data. The IDW allows staff to directly analyze data, rather than spend time on collection.

What were some of the initial challenges ConocoPhillips faced in leveraging big data, and how did they overcome them?

ConocoPhillips initially faced the challenge of an overwhelming influx of data from various rigs and wells but struggled to use that data for meaningful interpretation. This is a common issue for companies venturing into big data analytics. They overcame this by establishing a dedicated team in 2016 that acted as a 'nucleus for data analytics and data integration.' This team supported the company's Montney Shale business and the Surmont steam-assisted gravity drainage bitumen recovery facility. This then evolved to a full-fledged IDW in 2014, spearheaded by the Eagle Ford business unit.

Which ConocoPhillips business units pioneered the use of centralized data repositories, and what was the initial focus of these repositories?

The Canada, Alaska, and Norway business units within ConocoPhillips were the pioneering units to consolidate data into a centralized repository. This initiative dates back to the early 2000s and laid the groundwork for the IDW. Initially, these repositories focused on enabling functional workflows like production allocation or seismic analysis, with less emphasis on cross-functional integration.

How has the implementation of integrated data warehouses (IDWs) affected technical efficiency and data curation at ConocoPhillips?

The implementation of integrated data warehouses (IDWs) has dramatically improved technical efficiency and reduced the time needed to derive insights from the data. Users can now directly pull business and technical data into analytical tools like Spotfire, minimizing what Patrick Stanley calls 'personal data curation.' This integration across functions allows for quicker and more informed decision-making.

How does ConocoPhillips ensure adaptability and vendor-agnosticism in its approach to big data solutions in the oil and gas industry?

ConocoPhillips focuses on data integration and developing internal analytics capabilities. This allows them to remain vendor-agnostic, embracing the most efficient technologies as they emerge, ensuring they stay ahead in a rapidly evolving landscape. This approach enables them to leverage big data effectively while maintaining flexibility in choosing the best tools and technologies for their specific needs, which will be crucial for unconventionals.