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How Cognite Data Fusion reduced the time to value for one supermajor

Cognite worked with a global integrated energy company to liberate and contextualize subsurface data.

The solution empowers subsurface and drilling experts to make data-driven decisions that reduce the time to decision and create value.

In Short

A global integrated energy company used Cognite Data Fusion (CDF) to liberate and contextualize data from eight different systems (including EDM, Petrel, and SiteCom) to improve its well planning processes.

Benefits of CDF include:

How Cognite visualized risk factors to boost worker safety for Aker BP-02
Democratized access to data reduces time to value
oil and gas digitalization
Flexible data decreases the cost of building, deploying, and scaling models
oil and gas digitalization
Auditable data increases trust, helping experts cut time and costs

Challenge

In the oil and gas industry, the well planning cycle can take up to eight months. More than half of this time is spent looking for data.

Subject-matter experts such as geologists and drilling, completion, and geomechanical engineers need access to many different kinds of data in order to do their jobs efficiently. Instead of being easily accessible in one location, all this historical data tends to be locked away in silos — trajectories in one database, risks and hazards in another, well construction documents in a third, and so on.

Locating the data is often cumbersome and time-consuming. Some sources may be restricted to different domains; for example, drilling experts may not have access to the applications and systems that geologists use, and vice versa. Additionally, different data sources often require different access permissions, meaning that users need to track down both the data sources themselves and the people who can grant them access. Sometimes users simply can’t get access to a data source, because the software caps the number of users.

The challenges don’t stop there, however. Once the analysis is complete and decisions are made, the application files, analytical tools, and captured knowledge become additional silos of information, remembered or understood only by the people originally involved in the analysis and decision-making. Every time a new project is started, this process repeats itself, costing operators time and money by prolonging the time to value.

Fixing these issues requires a new approach to data operations (DataOps), a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and users. Once data has been liberated from silos and contextualized — meaning that critical information from different data sources has been connected — subject-matters experts across an organization can access timely and accurate data in their day-to-day work.

Solution

Cognite works with companies across the oil and gas industry, helping them draw insights from their data and unlock opportunities that make their operations faster, safer, and more sustainable.

For example, Cognite is working with a global integrated energy company that is pursuing an ambitious digitalization agenda. As part of that agenda, a joint team of experts from Cognite and the company liberated and contextualized data from several systems (including EDM, Petrel, and SiteCom) to improve the company’s well planning processes.

Historically, users had to access eight different data sources — casing assemblies, risks, drilling data, trajectories, public well data, formations tops, well logs, and well construction documents — to view previously drilled wells and run analyses. The team liberated all the data from its silos and collected it in Cognite Data Fusion (CDF), making it easily and securely accessible in the cloud.

Once the data was liberated, it had to be contextualized. This process fuses structured data (such as geospatial and well data) with unstructured data (information found in sources such as documents, images, reports, and spreadsheets).

The development team then streamed the liberated, contextualized data about previously drilled wells to an application built on top of CDF. The application significantly streamlines the analytical workflow by eliminating the need to access multiple different systems to view different data.

For example, an engineer can search — either by entering keywords or by drawing a polygon on a map — to view previously drilled wells and all the structured and unstructured data associated with them: well schematics, well header data from the company’s data warehouse, casing section information from EDM, formation tops from Petrel Studio, historical drilling data from SiteCom, and more.

Impact

Liberated, contextualized data, both structured and unstructured, takes out the guesswork in subsurface and drilling operations. It removes human biases, reduces processing time, enhances collaboration, and empowers geoscience, drilling, and petroleum engineers to become innovators.

The combination of Cognite Data Fusion and Cognite’s suite of business applications enables full integration of planning and operations, empowering subsurface and drilling experts to make data-driven decisions that reduce the time to decision and create value.

In this example, liberating and contextualizing data about previously drilled wells produces several benefits for the company:

  • Access to the data becomes democratized; instead of manually accessing and combining information from the different data sources, users can now explore all the data they need to do their job in a single location. This can cut the time it takes to plan a well from months to weeks, which is time that the company can reinvest in activities that generate bottom-line value.
  • The data becomes flexible; instead of being associated with a single use case and locked away in a silo, the data can be reused for other well construction and subsurface use cases. This decreases the cost of building, deploying, and scaling models to other assets.
  • The data becomes auditable; users will always know which data sets are validated and able to run their digital workflows. This increases trust in the data, helping experts make decisions that save time and costs.

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