Exploration efficiency

Exploration efficiency

Prototyping future geoscience data organization and analytics tools for improved exploration workflows

Interpreters in exploration assets need a productivity increase in their handling and integrating steadily increasing amounts of data. These terabytes of heterogeneous Big data, including various geophysical data ranging from 2D and 3D surveys with numerous processing versions and large populations of wells, need to be analyzed faster, yet reliably, to provide valuable inputs to decision makers. BRU21 aims at developing novel automated tools to increase data analysis efficiency in the exploration workflows through modern computational methods, e.g. machine learning and artificial intelligence, combined with cross-disciplinary subsurface expertise...read more

Program area team

Current Projects

Current Projects


Current Projects

The impact of well data quality on machine learning performance

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PhD Candidate Veronica Torres Caceres

Main Supervisor Kenneth Duffaut

Sponsor: Aker BP

The project focuses on two topics:
1) Prototyping the “future” well database that integrates ‘’all” measurements acquired in wells together with their corresponding metadata;
2) applying and training machine learning algorithms to automatically access data quality, depth shifting, rock typing, similarity recognition, as well as estimate petrophysical and geophysical parameters

print screen from video of phd candidate

Title

Automated seismic reconstruction of missing sections 

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Post Doctoral fellow Luc Alberts

Main Supervisor Kenneth Duffaut

Sponsor: Neptune Energy Norge AS

The project deals with designing and implementing software algorithms that automatically detect and trace geological unconformities typically found within seismic data sets, and label these before reconstructing the surfaces that have undergone uplift and erosion

 

Kurdistan

Automated lithology classification employing whole core CT scans

PhD candidate, kurdistan Chaswin

Main Supervisor, Kenneth Duffaut

Sponsor: Equinor

This project aims at developing automated routines and workflows for lithology classification and estimation of transport properties. The main objectives of this project can be summarized as below: 

  • Enhance current utilization of whole core CT images in rock characterization workflows 
  • Rock typing based on automated image analysis routines 
  • Explore the application of machine learning algorithms to classify lithology 
  • Explore the application of machine learning algorithms to estimate transport properties such as porosity, permeability and water saturation based on the underlying lithology classification