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14 papers · 1 filter
Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: A Case Study on the Goliat Field
Muzammil Hussain Rammay, Sergey Alyaev, David Selvåg Larsen +2
The real-time interpretation of the logging-while-drilling data allows us to estimate the positions and properties of the geological layers in an anisotropic subsurface environment…
Explainability in subgraphs-enhanced Graph Neural Networks
Michele Guerra, Indro Spinelli, Simone Scardapane +1
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher t…
Verification of a real-time ensemble-based method for updating earth model based on GAN
Kristian Fossum, Sergey Alyaev, Jan Tveranger +1
The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GAN…
Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network
Sergey Alyaev, Jan Tveranger, Kristian Fossum +1
Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data…
Simplifying Clustering with Graph Neural Networks
Filippo Maria Bianchi
The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the grap…
Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements
Muzammil Hussain Rammay, Sergey Alyaev, Ahmed H Elsheikh
The advent of fast sensing technologies allows for real-time model updates in many applications where the model parameters are uncertain. Bayesian algorithms, such as ensemble smoo…