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Machine Learning Workflow

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This presentation and demonstration will focus on a machine learning workflow in the upstream Oil and Gas domain to predict formation tops by applying artificial intelligence and machine learning techniques to learn the well logs signatures. This deep learning model provides high quality predictions to aid the geologists in picking lithology markers consistently and in an accelerated fashion thus boosting their operational efficiency. The self-learning model, which is a unique differentiator of dataVediK and encompasses the detection of outliers and data quality issues and their subsequent validation and suggested corrections to improve the quality of data in an automated fashion during the model training process. The demo will then showcase a real-time drilling solution built using this ML model, whereby the formation tops are predicted, and the structural model is updated automatically as the GR log is acquired.

 
Machine Learning Workflow
London AAPG Region Office
LondonCity of London
United Kingdom
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Sunil Garg
Marta Diaz Events Manager
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01/07/2020 12:00 PM 01/07/2020 1:00 PM Central Standard Time Webinar: Machine Learning Workflow This presentation and demonstration will focus on a machine learning workflow in the upstream Oil and Gas domain to predict formation tops by applying artificial intelligence and machine learning techniques to learn the well logs signatures. This deep learning model provides high quality predictions to aid the geologists in picking lithology markers consistently and in an accelerated fashion thus boosting their operational efficiency. The self-learning model, which is a unique differentiator of dataVediK and encompasses the detection of outliers and data quality issues and their subsequent validation and suggested corrections to improve the quality of data in an automated fashion during the model training process. The demo will then showcase a real-time drilling solution built using this ML model, whereby the formation tops are predicted, and the structural model is updated automatically as the GR log is acquired. London, City of London, United Kingdom AAPG false DD/MM/YYYY
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