Media Summary: Well log data provides a wealth of information about the , from mineralogy to porosity, permeability and saturation. A key impact on reservoir studies is a rigorous strategy around facies for modeling. Decisions on facies, how to define them and ... By Lucas Aguiar, Maurício Matos, Jadson Muniz and Rodrigo Canário. This work adopted petrophysical perspective to specialize ...

Facimage A Machine Learning Based - Detailed Analysis & Overview

Well log data provides a wealth of information about the , from mineralogy to porosity, permeability and saturation. A key impact on reservoir studies is a rigorous strategy around facies for modeling. Decisions on facies, how to define them and ... By Lucas Aguiar, Maurício Matos, Jadson Muniz and Rodrigo Canário. This work adopted petrophysical perspective to specialize ... lectrofacies modeling workflow steps are not widely established in industry best practices. Misuse and lack of dissemination hold ... Video of the session as part of the Cegal Americas Data Science and Python workshop, January 21 2021. Presented originally at the GeoConvention 2022 conference, this video showcases how the analyze ...

Today we close out our 2019 NeurIPS series with Mohamed Sidahmed, Data driven modeling is becoming a key differentiation to unlock higher recoveries from existing fields as well as identify new ... Streamlining petrophysical workflows with Interpretable models can be understood by a human without any other aids/techniques. On the other hand, explainable models ... This video shows how to perform permeability predictions

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Facimage: A Machine Learning-based Approach for Faster, More Precise Rock Type Classification
Electrofacies, a guided machine learning, for improving facies logs for the practice of geomodeling
#2  Petrophysical guided preconditioning applied to electrofacies classification by machine learning
PetroTeach webinar on Electrofacies, A Guided Machine Learning For The Practice of Geomodeling
Americas Data Science and Python Workshop - Machine Learning drives facies classification
Data Science Workshop - Machine Learning Facies Classification by Neptune and Cegal
Analysis of unsupervised & supervised facies classification with petrophysics in the Gulf of Mexico
FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333
Machine Learning Techniques in Reservoir Characterization - Applications & Pitfalls
04FORCE MacGregor Streamlining petrophysical workflows with machine learning focused on the estimati
Machine Learning assisted carbonate lithofacies classification - A Barents Sea case study
Interpretable vs Explainable Machine Learning
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Facimage: A Machine Learning-based Approach for Faster, More Precise Rock Type Classification

Facimage: A Machine Learning-based Approach for Faster, More Precise Rock Type Classification

Well log data provides a wealth of information about the #subsurface, from mineralogy to porosity, permeability and saturation.

Electrofacies, a guided machine learning, for improving facies logs for the practice of geomodeling

Electrofacies, a guided machine learning, for improving facies logs for the practice of geomodeling

A key impact on reservoir studies is a rigorous strategy around facies for modeling. Decisions on facies, how to define them and ...

#2  Petrophysical guided preconditioning applied to electrofacies classification by machine learning

#2 Petrophysical guided preconditioning applied to electrofacies classification by machine learning

By Lucas Aguiar, Maurício Matos, Jadson Muniz and Rodrigo Canário. This work adopted petrophysical perspective to specialize ...

PetroTeach webinar on Electrofacies, A Guided Machine Learning For The Practice of Geomodeling

PetroTeach webinar on Electrofacies, A Guided Machine Learning For The Practice of Geomodeling

lectrofacies modeling workflow steps are not widely established in industry best practices. Misuse and lack of dissemination hold ...

Americas Data Science and Python Workshop - Machine Learning drives facies classification

Americas Data Science and Python Workshop - Machine Learning drives facies classification

Video of the session as part of the Cegal Americas Data Science and Python workshop, January 21 2021.

Data Science Workshop - Machine Learning Facies Classification by Neptune and Cegal

Data Science Workshop - Machine Learning Facies Classification by Neptune and Cegal

Video of the session on

Analysis of unsupervised & supervised facies classification with petrophysics in the Gulf of Mexico

Analysis of unsupervised & supervised facies classification with petrophysics in the Gulf of Mexico

Presented originally at the GeoConvention 2022 conference, this video showcases how the #ReservoirExperts analyze ...

FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333

FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333

Today we close out our 2019 NeurIPS series with Mohamed Sidahmed,

Machine Learning Techniques in Reservoir Characterization - Applications & Pitfalls

Machine Learning Techniques in Reservoir Characterization - Applications & Pitfalls

Data driven modeling is becoming a key differentiation to unlock higher recoveries from existing fields as well as identify new ...

04FORCE MacGregor Streamlining petrophysical workflows with machine learning focused on the estimati

04FORCE MacGregor Streamlining petrophysical workflows with machine learning focused on the estimati

Streamlining petrophysical workflows with

Machine Learning assisted carbonate lithofacies classification - A Barents Sea case study

Machine Learning assisted carbonate lithofacies classification - A Barents Sea case study

Leveraging the advantages of

Interpretable vs Explainable Machine Learning

Interpretable vs Explainable Machine Learning

Interpretable models can be understood by a human without any other aids/techniques. On the other hand, explainable models ...

CARP 4.3-4:  Permeability prediction from sedimentary facies

CARP 4.3-4: Permeability prediction from sedimentary facies

This video shows how to perform permeability predictions