Inability to link geological and geophysical observations.High variability in the well curves not depicting geological variations.The difficulty of tying well data with seismic.Scarcity of wells within the study area.WellGen addresses common machine learning challenges, including: WellGen overcomes this challenge by generating synthetic data, simulating many pseudo-wells based on existing well statistics and rock physics modeling. These methods, particularly deep learning, depend on having enough labeled data to adequately train the neural network. In standard supervised machine learning approaches, the seismic-to-rock property relationship is learned using available data. Utilizes rock physics guided machine learning to optimize extraction of information and value addition from all available data.Allows direct prediction of facies and reservoir properties.Improves Reservoir Characterization for low well-control areas.A simplified machine learning approach employs Convolutional Neural Networks (CNN) estimating multiple rock property volumes in a greatly simplified workflow. Rock Physics theory and statistical simulations generate synthetic data for various geological scenarios. GeoAI encompasses a novel methodology for seismic reservoir characterization with limited well control, speeding up reservoir property predictions with a rock physics driven machine learning technique. HampsonRussell offers a comprehensive suite of reservoir characterization software tools:
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