Blar i NORCE vitenarkiv på forfatter "Alyaev, Sergey"

Viser treff 1-8 av 8

    • Direct Multi-Modal Inversion of Geophysical Logs Using Deep Learning 

      Alyaev, Sergey; Elsheikh, Ahmed (Peer reviewed; Journal article, 2022)
      Geosteering of wells requires fast interpretation of geophysical logs which is a non-unique inverse problem. Current work presents a proof-of-concept approach to multi-modal probabilistic inversion of logs using a single ...

    • Ensemble-Based Well-Log Interpretation and Uncertainty Quantification for Well Geosteering 

      Jahani, Nazanin; Ambia Garrido, Joaquin; Alyaev, Sergey; Fossum, Kristian; Suter, Erich Christian; Torres-Verdin, Carlos (Peer reviewed; Journal article, 2022)
      Hydrocarbon reservoirs are often located in spatially complex and uncertain geologic environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced ...

    • Improving predictive models for rate of penetration in real drilling operations through transfer learning 

      Pacis, Felix James Cardano; Ambrus, Adrian; Alyaev, Sergey; Khosravanian, Rasool; Kristiansen, Tron Golder; Wiktorski, Tomasz (Peer reviewed; Journal article, 2023)
      The rate of penetration (ROP) is a key performance indicator in the oil and gas drilling industry as it directly translates to cost savings and emission reductions. A prerequisite for a drilling optimization algorithm is ...

    • An interactive sequential-decision benchmark from geosteering 

      Alyaev, Sergey; Ivanova, Sofija; Holsaeter, Andrew Martin; Bratvold, Reidar Brumer; Bendiksen, Morten (Peer reviewed; Journal article, 2021)
      During drilling, to maximize future expected production of hydrocarbon resources, the experts commonly adjust the trajectory (geosteer) in response to new insights obtained through real-time measurements. Geosteering ...

    • Modeling extra-deep electromagnetic logs using a deep neural network 

      Alyaev, Sergey; Shahriari, Mostafa; Pardo, David; Omella, Ángel Javier; Larsen, David Selvåg; Jahani, Nazanin; Suter, Erich Christian (Peer reviewed; Journal article, 2021)
      Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set ...

    • A numerical study of flow field and particle deposition in nasal channels with deviant geometry 

      Thune, Eveline Løvaas; Kosinski, Pawel Jan; Balakin, Boris; Alyaev, Sergey (Peer reviewed; Journal article, 2021)
      Deviant geometry of nasal channels results in significant changes to nasal aerodynamics that alter flow resistance, sensation, and the ability to filter aerosols. The invasive, operative modification of nasal geometry might ...

    • Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements 

      Rammay, Muzammil Hussain; Alyaev, Sergey; Elsheikh, Ahmed (Peer reviewed; Journal article, 2022)
      The advent of fast sensing technologies allow for real-time model updates in many applications where the model parameters are uncertain. Once the observations are collected, Bayesian algorithms offer a pathway for real-time ...

    • Verification of a real-time ensemble-based method for updating earth model based on GAN 

      Fossum, Kristian; Alyaev, Sergey; Tveranger, Jan; Elsheikh, Ahmed H. (Peer reviewed; Journal article, 2022)
      The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of ...