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

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 qualitative 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 Middle 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 geomodeling and machine learning.