FORCE Hackathon and/or the symposium: Applied Machine Learning and Advanced Analytics with Oil and Gas Data

Available Presentations
Bormann- Introduction to Force symposium
PPTX
Waldeland- An Introduction From Traditional Machine Learning to Deep Learning
PPTX
Purves- Core to seismic property prediction - 3D rock property prediction using
PDF
Lowell- Geoteric Combining Artificial Intelligence with Human Reasoning for Seismic Interpretation
PPTX
Stinson- Applications of artificial intelligence in geoscience at Total Deep Neural Networks in seismic interpretation
PDF
Grimsgaard- Deep Learning Neural Network Solution Applied to Seismic Horizon Interpretation
PDF
Pena- Reservoir Petrophysical Properties estimation from drill cuttings using advance data analytics_AP
PDF
Pena-WMV- Reservoir Petrophysical Properties estimation from drill cuttings using advance data analytics_AP
WMV
Tungland- Innovation culture, how to use open innovation to solve real problems
PDF
Hall- Presentation of the 2019 Force Machine Learning Hackthon projects
PPTX
Oikonomou- ESA Automatic Seismic Interpretation. A review of architectures and model performances
PDF
Demyanov- How can machine learning add value to making inferences from reservoir data
PPTX
Luo- An ensemble-based kernel learning approach to account for model errors of rock physics models in 4D seismic history matching a real field case study
PPTX
Tolstukhin- Data-driven estimates of reservbrown field study
PPTX
Velde- Creating Trends for Reservoir Modelling Using ANN
PPTX
Hernandez- An effective G&G exploration strategy inspired by a wolfpack
PDF
Naylor- Using NLP to make unstructured data highly accessible in E&P
PPTX
Blondelle- Using machine learning to read composite logs
PPTX