摘要：In recent years, uncertainty has been widely recognized in geosciences, leadingto an increased need for its quantification. Predicting the subsurface is anespecially uncertain effort, as our information either comes from spatiallyhighly 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 in2-D, we currently lack both qualitative and quantitative understanding of howinterpretational uncertainties of 3-D datasets are distributed. In this work, weanalyze 78 seismic interpretations done by final-year undergraduate (BSc)students of a 3-D seismic dataset from the Gullfaks field located in thenorthern North Sea. The students used Petrel to interpret multiple (interlinked)faults and to pick the Base Cretaceous Unconformity and Top Ness horizon (partof the Middle Jurassic Brent Group). We have developed open-source Python tools toexplore and visualize the spatial uncertainty of the students' fault stickinterpretations, the subsequent variation in fault plane orientation and theuncertainty in fault network topology. The Top Ness horizon picks were used toanalyze fault offset variations across the dataset and interpretations, withimplications for fault throw. We investigate how this interpretationaluncertainty interlinks with seismic data quality and the possible use of seismicdata quality attributes as a proxy for interpretational uncertainty. Our workprovides a first quantification of fault and horizon uncertainties in 3-Dseismic interpretation, providing valuable insights into the influence ofseismic image quality on 3-D interpretation, with implications for deterministicand stochastic geomodeling and machine learning.
