Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling

J. Mullins*, H. van der Vegt, J. Howell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.

Original languageEnglish
Title of host publication80th EAGE Conference and Exhibition 2018
Subtitle of host publicationOpportunities Presented by the Energy Transition
PublisherEuropean Association of Geoscientists and Engineers, EAGE
ISBN (Electronic)9789462822542
DOIs
Publication statusPublished - 11 Jun 2018
Event80th EAGE Conference and Exhibition 2018: Opportunities Presented by the Energy Transition - Copenhagen, Denmark
Duration: 11 Jun 201814 Jun 2018

Publication series

Name80th EAGE Conference and Exhibition 2018: Opportunities Presented by the Energy Transition

Conference

Conference80th EAGE Conference and Exhibition 2018: Opportunities Presented by the Energy Transition
CountryDenmark
CityCopenhagen
Period11/06/1814/06/18

    Fingerprint

ASJC Scopus subject areas

  • Geophysics
  • Geochemistry and Petrology

Cite this

Mullins, J., van der Vegt, H., & Howell, J. (2018). Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling. In 80th EAGE Conference and Exhibition 2018: Opportunities Presented by the Energy Transition (80th EAGE Conference and Exhibition 2018: Opportunities Presented by the Energy Transition). European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.201800772