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

Staff photo

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

Project result: Automatic depth matching of well log data

Structured well log data base and algorithm for fast and accurate depth-matching of well log data

Automated lithofacies classification of pre-stack seismic data

Automated lithofacies classification of pre-stack seismic data


PhD Candidate Pranav Audhkhasi

Main Supervisor 

Sponsor: Wintershall DEA

BRU21 Conference 2022


Automated seismic reconstruction of missing sections 

staff photo

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

Project result: Automated detection of geological unconformities

Machine learning-enabled workflow for automatic detection and classification of geological unconformities



Automated lithology classification employing whole core CT scans

PhD candidate, kurdistan Chawshin

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 

Project result: Workflows to classify lithology using 2D and 3D CT images
Convolutional Neural Networks-based workflows for high-resolution classification of lithology and porosity

BRU21 innovation:

BRU21 innovation:

Fast downhole testing of permeability anisotropy
New technology and method to determine permeability anisotropy accurately and within minutes in downhole testing