Journal cover Journal topic
Solid Earth An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.165 IF 4.165
  • IF 5-year value: 4.075 IF 5-year
    4.075
  • CiteScore value: 4.28 CiteScore
    4.28
  • SNIP value: 1.501 SNIP 1.501
  • SJR value: 1.060 SJR 1.060
  • IPP value: 4.21 IPP 4.21
  • h5-index value: 29 h5-index 29
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 27 Scimago H
    index 27
Discussion papers
https://doi.org/10.5194/se-2019-54
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2019-54
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Apr 2019

Research article | 02 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Solid Earth (SE).

Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodelling and machine learning

Alexander Schaaf and Clare E. Bond Alexander Schaaf and Clare E. Bond
  • Geology and Petroleum Geology, School of Geosciences, University of Aberdeen, AB24 3UE, UK

Abstract. In recent years uncertainty has been widely recognized in geosciences, leading to an increased need for its quantification. Predicting the subsurface is an especially uncertain effort, as our information either comes from spatially highly limited direct (1-D boreholes) or indirect 2-D and 3-D sources (e.g. seismic). And while uncertainty in seismic interpretation has been explored in 2-D, we currently lack both qualitatitive and quantitative understanding of how interpretational uncertainties of 3-D datasets are distributed. In this work we analyze 78 seismic interpretations done by final year undergraduate (BSc) students of a 3-D seismic dataset from the Gullfaks field located in the northern North Sea. The students used Petrel to interpret multiple (interlinked) faults and to pick the Base Cretaceous Unconformity and Top Ness horizon (part of the Mid-Jurassic Brent Group). We have developed open-source Python tools to explore and visualize the spatial uncertainty of the students fault stick interpretations, the subsequent variation in fault plane orientation and the uncertainty in fault network topology. The Top Ness horizon picks were used to analyze fault offset variations across the dataset and interpretations, with implications for fault throw. We investigate how this interpretational uncertainty interlinks with seismic data quality and the possible use of seismic data quality attributes as a proxy for interpretational uncertainty. Our work provides a first quantification of fault and horizon uncertainties in 3-D seismic interpretation, providing valuable insights into the influence of seismic image quality on 3-D interpretation, with implications for deterministic and stochastic geomodelling and machine learning.

Alexander Schaaf and Clare E. Bond
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Alexander Schaaf and Clare E. Bond
Alexander Schaaf and Clare E. Bond
Viewed  
Total article views: 430 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
332 96 2 430 0 0
  • HTML: 332
  • PDF: 96
  • XML: 2
  • Total: 430
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 02 Apr 2019)
Cumulative views and downloads (calculated since 02 Apr 2019)
Viewed (geographical distribution)  
Total article views: 266 (including HTML, PDF, and XML) Thereof 266 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
Latest update: 24 May 2019
Publications Copernicus
Download
Short summary
Seismic reflection data allows us to infer subsurface structures such as horizon and fault surfaces. The interpretation of this indirect data source is inherently uncertainty and our work takes a first look at the scope of uncertainties involved in the interpretation of 3-D seismic data. We show how uncertainties of fault interpretations can be related to data quality and discuss the implications for the 3-D modeling of subsurface structures derived from 3-D seismic data.
Seismic reflection data allows us to infer subsurface structures such as horizon and fault...
Citation