A novel workflow for building multiple point statistics training images from virtual outcrops

J. R. Mullins*, J. A. Howell, S. J. Buckley, C. Kehl

*Corresponding author for this work

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

Abstract

Outcrop analogues of reservoirs are applied because facies-scale reservoir heterogeneities are frequently unresolvable at seismic-scale and well data provides sparse 1D geometrical data. Traditionally, geocellular models rely on manually measuring variograms or object dimensions from outcrops to define the geometry, size and directionality of facies proportions. Therefore, their ability to capture complex shapes and facies relationships in the subsurface is restricted by the quality of available geological data and the limitations of modelling algorithms. Multiple-point statistics (MPS) is a property modelling technique dependent on representative training images (TIs)- conceptual numerical descriptions of the geology expected in the reservoir under study. Lack of suitable TIs has limited the application of the MPS method to date. Recent advances in digital outcrop mapping methods, including lidar and photogrammetry, permit the rapid acquisition of high-resolution 3D virtual outcrop models. These provide a critical and underused source of qualitative and quantitative information for high quality TI generation. We present a novel approach to apply 3D virtual outcrops as TIs; coupled with the streamlining of lidar integration into subsurface models using examples from the Bolea area, Ebro Basin, northern Spain. This approach will significantly improve prediction of 3D facies heterogeneity and its impact on reservoir performance.

Original languageEnglish
Title of host publication2nd Conference on Forward Modelling of Sedimentary Systems
Subtitle of host publicationFrom Desert to Deep Marine Depositioned Systems
PublisherEuropean Association of Geoscientists and Engineers, EAGE
Pages1-5
Number of pages5
ISBN (Print)9781510822870
DOIs
Publication statusPublished - 2016
Event2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems - Trondheim, Norway
Duration: 25 Apr 201628 Apr 2016

Conference

Conference2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems
CountryNorway
CityTrondheim
Period25/04/1628/04/16

Fingerprint

outcrop
statistics
lidar
photogrammetry
mapping method
variogram
geology
modeling
methodology
Spain
basins
geometry
prediction
basin

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science
  • Oceanography

Cite this

Mullins, J. R., Howell, J. A., Buckley, S. J., & Kehl, C. (2016). A novel workflow for building multiple point statistics training images from virtual outcrops. In 2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems (pp. 1-5). European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.201600354

A novel workflow for building multiple point statistics training images from virtual outcrops. / Mullins, J. R.; Howell, J. A.; Buckley, S. J.; Kehl, C.

2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems. European Association of Geoscientists and Engineers, EAGE, 2016. p. 1-5.

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

Mullins, JR, Howell, JA, Buckley, SJ & Kehl, C 2016, A novel workflow for building multiple point statistics training images from virtual outcrops. in 2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems. European Association of Geoscientists and Engineers, EAGE, pp. 1-5, 2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems, Trondheim, Norway, 25/04/16. https://doi.org/10.3997/2214-4609.201600354
Mullins JR, Howell JA, Buckley SJ, Kehl C. A novel workflow for building multiple point statistics training images from virtual outcrops. In 2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems. European Association of Geoscientists and Engineers, EAGE. 2016. p. 1-5 https://doi.org/10.3997/2214-4609.201600354
Mullins, J. R. ; Howell, J. A. ; Buckley, S. J. ; Kehl, C. / A novel workflow for building multiple point statistics training images from virtual outcrops. 2nd Conference on Forward Modelling of Sedimentary Systems: From Desert to Deep Marine Depositioned Systems. European Association of Geoscientists and Engineers, EAGE, 2016. pp. 1-5
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