Digital subsurface for improved decisions

Large amounts of subsurface data are available, but current workflows and programs for subsurface understanding are not optimal, resulting in inadequate utilization of datasets.

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Digital Subsurface for Improved Decisions​, illustrasjon

To build a Sustainable Subsurface Value Chain and make more informed decisions, digitalization and ML are necessary to integrate the knowledge and competence building from different WPs. A digital infrastructure, Subsurface Knowledge Cloud (SKC), will be established to provide readily usable data and high-performance computing power and visualization tools. Specific targets of WP5 are:

  • More robust model forecasts with feasible computational cost and better accessibility of big datasets.
  • More comprehensive and reliable uncertainty quantification for multi-purpose reservoir usage.
  • Develop data-driven approaches to integrate ML into subsurface-characterization, uncertainty quantification, and the decision-making process.

Work package 5 summed up

Work package 5 summed up.

Six projects have been defined:

This project creates a Federated Knowledge Cloud that will serve as the cloud infrastructure and AI platform for subsurface digital integration in NCS2030. It aims to enable users to de-velop, deploy, and execute AI projects efficiently. Moreover, it brings together cloud services, federated learning, marketing, and assisting tools in enabling seamless across-silos collabo-rations for advanced knowledge gain, improved decisions, and efficient workflows.

Increasing model complexities and a desire to include multi-scenario models, including, e.g., various geological settings, make the problem of uncertainty quantification a daunting com-putational challenge. A compelling way of handling computational issues is to use a single, or multiple, computational models with reduced fidelity. Methodologies for robust probabilistic production forecasts, utilizing scenarios and fidelity models, will be developed.

In ensemble-based reservoir management, one typically runs many reservoir models in parallel. A reservoir-model workflow can include several software and scripts, and automation is essential. This project will provide the ensemble tools needed to automate the simulation of the ensemble of workflows and the ensemble updates through history matching and optimization.

Ensemble data assimilation (DA) and optimization methods are popular approaches to sub-surface characterization, development and management problems. Meanwhile, machine learning (ML) has emerged as a powerful toolset with a variety of applications in subsurface problems. The similarities and connections among DA, optimization and ML pave the way of developing advanced DA and optimization algorithms that are powered by modern ML tech-nologies, and have the potential to go beyond the current state-of-the-art.

Utilize Open Earth Community. Open Earth Community (OEC): Landmark Graphics to provide access to a full open development environment which includes all development tools and all Halliburton Landmark solutions. The Data Analytics platform can be utilized for building ML models. Pre-models are available that can be re-used or extended. In addition, Landmark Graphics can provide access to DISKOS data upon approval from NPD.

Schlumberger will assist NCS2030 in getting trained and utilizing modern OSDU-based workflows by getting access to the DELFI Cognitive E&P Environment. DELFI has a modern OpenAPI based Developer environment and a flexible Data science and Analytics platform leveraging Dataiku and TIBCO’s Spotfire. In collaboration with NCS2030 researchers, topics like Real-time Reservoir Optimization, Proxy Modelling and Data-driven Physics-based Predic-tive Modelling can be investigated.

Key People

Geir Evensen
Chief Scientist
Randi Valestrand
Research director
NORCE, Bergen
Professor i informatikk/datateknikk
Faculty of Science and Technology
Department of Electrical Engineering and Computer Science
Forsker i informasjonsteknologi
Faculty of Science and Technology
Department of Electrical Engineering and Computer Science


Stipendiat i Applied Data Science
Faculty of Science and Technology
Department of Electrical Engineering and Computer Science
Stipendiat i Robust reservoarstyring for trygg og effektiv anvendelse og lagring
Faculty of Science and Technology
Department of Energy Resources
Faculty of Science and Technology
Department of Energy Resources