Data Driven Workflows

What if how you work, screams 1983 Toyota Corolla?

Removing data barriers for modern workflows with OSDU Wells data foundation

Data barriers

Data structures might sound dull and irrelevant at first glance, but take a deeper dive, and you will see it mirrors the organization you are in. What about the other way around, does the data model structure how you work?

An updated data model makes sure important information is available for all decision makers at all times, and that company is able to capture its value generation, which is why the Wells Data Foundation project in OSDU is so important for our industry. We are working with operators, rig companies, service companies to implement a data structure for value-adding workflows.


If your data model is not updated, it can be like driving a 1983 Toyota Corolla - you take pride of every mile you get out of it, but you lack all the safety, performance, and comfort. And we are very proud of the mileage of information flow in our business. The problem is when the data model no longer caters for the workflows, like when the cassette doesn't fit in the streaming device. Or the plans need to populate decisions AND control operations.

1983 had cars with cassette players. Today the cars stream media


Ok, we made a point about needing renewal, cloud, collaboration, more complex wells, and all that. How does the data model impact our day-to-day life?

First of all, information needs to be stored to create value. In 1983, workflows were completely manual, and people were the only assets. How much information one individual could capture during planning and utilize during operations was the measure of success. In 2022 (it's getting close to 2023 - yikes), the digital footprint of any company is the value. So capturing the learnings and making them available for decisions is the main task of the data structure.

Presentations, spreadsheets, and reports are evidence of a broken data model, they represent gaps or information barriers in the data model. If they are as present in routine tasks as they are in our industry, it means the data model is not capturing the value generated in the organization.

Data quality barriers
Data quality barriers are when information flows in presentations, spreadsheets and reports

The flow of information has to survive the different phases, domains and teams. Wells are planned, iterated, executed, and summarized, and there are many experts involved from management, finance, subsurface, drilling, completions, and more. And there are many companies and workflows that depend on the outcome of planning and operations. Logistics, risk management, partner processes, vendor integrations and more make up a complex flow of information.


The OSDU Wells Data Foundation project has team members from planning and execution, from operations, rig companies, and from service companies. They work together to define a data model for all stages of the well lifecycle.

With a top down approach, the project started by defining the problem: No data is flowing from the operator without a presentation, spreadsheet or report - it's all emails, meetings and phonecalls (1983 is very proud)

The next step was to start mapping the most important information throughout the various phases, to connect the source of the data to the user of data. Sounds simple, but this is rather complex.

The agreed end goal is to have a model as shown in the figure below

OSDU WDF data structrue
The OSDU WDF data structure, removing data barriers


Well, except for making better investment decisions in well projects and increasing the company value, the data model is an enabler for higher quality projects

Where are we going
Where are we going?

For operations, there is a need for a data structure which allows for reusing the information generated in planning. Today, documents are updated day by day to describe the next steps, and wellbore schematics are drawn on multiple whiteboards around the rig site. This distracts the overall strategic management of the operations, as the holistic well goals are secondary to the detailed next few steps.

Investing in a well is a 500 MNOK investment or more, and it is done on a premise of a single design, made available in a presentation and documents. The decisions made are more: ok, let's go ahead and do our best to make a well, than a thorough evaluation of cost, risk, and value. This is evident by the number of risk matrices which are populated with text in decision documents. When you have a good data model, you can calculate risk based on the project parameters - and therefore keep track of the overall goals of the well.

And lastly, for data management, this is where oil companies and most other companies in the industry are spending their days. Looking for documents, writing documents, correcting old databases, and combining different datasets to create experiences. With a proper data structure, 60% of engineer's and manager's time can be freed up, which estimates to thousands of hours per well.

Want to learn more?

Our platform is built to create value for the industry, and we would like to learn your challenges to the workflow and data structure.

Reach out to us so we can share more about our products and projects.

Share this

Latest news