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

Bormann- Introduction to Force symposium

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Waldeland- An Introduction From Traditional Machine Learning to Deep Learning

PPTX

Purves- Core to seismic property prediction - 3D rock property prediction using

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Lowell- Geoteric Combining Artificial Intelligence with Human Reasoning for Seismic Interpretation

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Stinson- Applications of artificial intelligence in geoscience at Total Deep Neural Networks in seismic interpretation

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Grimsgaard- Deep Learning Neural Network Solution Applied to Seismic Horizon Interpretation

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Pena- Reservoir Petrophysical Properties estimation from drill cuttings using advance data analytics_AP

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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

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Hall- Presentation of the 2019 Force Machine Learning Hackthon projects

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Oikonomou- ESA Automatic Seismic Interpretation. A review of architectures and model performances

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Demyanov- How can machine learning add value to making inferences from reservoir data

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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

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Tolstukhin- Data-driven estimates of reservbrown field study

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Velde- Creating Trends for Reservoir Modelling Using ANN

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Hernandez- An effective G&G exploration strategy inspired by a wolfpack

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Naylor- Using NLP to make unstructured data highly accessible in E&P

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Blondelle- Using machine learning to read composite logs

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Jones- A million wells

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