Well Log Inputting for OSDU Data With SLB

Retrieve data from the OSDU® Data Platform and fill in any missing measurement values from the well logs with SLB and Dataiku.

Business Overview 

The OSDU® Data Platform is an industry initiative to facilitate collaboration among energy companies, technology providers, and service providers. With data format standardization and open APIs, the cloud-based platform enables efficient access, sharing, and analysis of industry-specific data, including seismic data, well logs, production data, and reservoir models. The final goal is to achieve seamless data integration and interoperability across various domains and disciplines in the energy industry.

However, data quality assurance is crucial when utilizing data transferred from the OSDU® Data Platform. Users may face challenges related to missing data, which directly impact the decision-making process and the value extracted from the data. Incomplete measurements hinder the ability of geologists to interpret subsurface formations and reservoir properties. Additionally, petrophysicists’ calculations are compromised when missing components from the dataset impede well log correlation efforts, impacting reservoir characterization and geologic mapping.

This seamless integration to the OSDU® Data Platform ensures comprehensive and consistent data availability. Domain experts can leverage ML models to predict missing values in well logs, fill in measurements, and create more complete datasets. Automating these processes saves users hours of effort on scaling analysis across multiple wells, optimizing their resources and making decisions faster throughout the asset lifecycle.


  • Seamless integration between Dataiku and the OSDU® Data Platform.
  • Enhanced data completeness using machine learning (ML) algorithms to fill in missing measurements and to create more complete datasets.
  • Improved reservoir characterization and formation evaluation, which enables efficient scaling across multiple wells to optimize resources.
  • More efficiency and scalability with automation of log missing data generation, which saves time and effort for exploration and production workflows.