Modeling extra-deep electromagnetic logs using a deep neural network

Authors:
Open Access
Publisher website
Google Scholar
Abstract
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 of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.
Funding Information
  • Österreichische Forschungsförderungsgesellschaft
  • Eusko Jaurlaritza (KK-2019-00068)
  • Eusko Jaurlaritza (KK-2019-00085)
  • Bundesministerium für Verkehr, Innovation und Technologie
  • BCAM
  • Norges Forskningsråd (268122)
  • European Regional Development Fund (EFA362/19)
  • Aker BP
  • Bundesministerium für Digitalisierung und Wirtschaftsstandort
  • Vår Energi
  • Ministerio de Ciencia e Innovación
  • European Commission (777778)
  • Equinor
  • Österreichische Forschungsförderungsgesellschaft
  • Eusko Jaurlaritza (KK-2019-00068)
  • Eusko Jaurlaritza (KK-2019-00085)
  • Bundesministerium für Verkehr, Innovation und Technologie
  • BCAM
  • Norges Forskningsråd (268122)
  • European Regional Development Fund (EFA362/19)
  • Aker BP
  • Bundesministerium für Digitalisierung und Wirtschaftsstandort
  • Vår Energi
  • Ministerio de Ciencia e Innovación
  • European Commission (777778)
  • Equinor

This publication has 31 references indexed in Scilit: