First EAGE Workshop on Machine Learning in Americas

Abstract Submission has been extended!

Please submit your extended abstract before June 12th 2020

The Conference intends to offer new perspectives on the Digital Transformation era. Technical Committee invites practitioners, innovators and tech experts to contribute to this Conference and to share success stories, best practices, lay out big innovative ideas and seek new alliances to jointly develop and implement the changes to embrace this new world.

We hope to offer a series of technical presentations as well as an exciting knowledge sharing platform to provide an interactive and integrated learning environment with opportunities to network through panel discussions, technical presentations and hands on activities.

Ready to submit?

Call for Abstracts has closed

Topics

Abstract submission has been extended, please submit before 12 June 2020.

Please submit abstracts on the following topics:

1) Geoscience Applications:

Seismic processing, model building, and imaging using Machine Learning
How to account for uncertainties with Machine Learning workflows in Geosciences?
Applying Machine Learning in oil & gas industry to Improve Subsurface Characterisation
Estimation of static and dynamic reservoir properties through AI, at well and/or field scale
Porosity and permeability estimation through AI at well
Interpreted logs through data analytics.

2) Reservoir Engineering Applications

Perform a forecast oil production analysis using machine learning algorithms
Support history matching with ML concepts.
Visual analytics, how to integrate geostatistic with engineer information in an integrated tool.
Added-value of Machine Learning techniques to enhance Seismic Data interpretation and rock-properties estimation
How to handle specificities of Geoscience dataset (scarcity, geospatial information, the mix of resolution, etc) to optimize Machine Learning algorithm
Integration of Machine Learning algorithm to existing physical-based Geosciences workflows
Pressure estimation using Deep and ML algorithms
Machine Learning to Predict Operational Outcomes
Is it possible to group wells using their static or dynamic information?

3) Operational & Intelligent Fields applications

Follow the well’s behavior daily through the integration of the field information in a warehouse database.
Optimizing Drilling Operations
Predictive Maintenance

4) Enabling Technologies & knowledge-transfer

Facilitating the adoption of Machine Learning techniques for non-data scientists.
Sharing information across the industry to facilitate R&D work for academics & corporate research center
Data Analytics using GPUs
What is Big Data? How to manage all this data?