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

Abstract

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.

Imaging the Subsurface: How Different Visualizations of Cross-sections Affect the Sense of Uncertainty

“…The resulting model is dependent on the geologists' a priori knowledge and experience and therefore subjective (Polson and Curtis 2010). When these models are made, geologists struggle to model and communicate the uncertainty involved (Randle et al 2018;Pérez-Díaz et al 2020;Schaaf and Bond 2019).…”

Constraining stochastic 3-D structural geological models with topology information using Approximate Bayesian Computation using GemPy 2.1

“…The field is located in the western part of the Viking Graben, and consists of the NNE-SSW-trending 10-25 km wide Gullfaks fault block (Fossen and Hesthammer, 1998). For a detailed overview of the regional and structural geology we refer to Fossen and Rørnes (1996); Fossen and Hesthammer (1998); Fossen et al (2000); Schaaf and Bond (2019).…”

Quantifying the impact of the structural uncertainty on the gross rock volume in the Lubina and Montanazo oil fields (Western Mediterranean)

“…This practice, which ignores the other equally probable solutions that match all the available data and interpretations, has often led to unpleasant surprises, for example, when new wells have been drilled. Another challenge is that many of these models are not easy to update with new data when they become available (Seiler et al, 2009;Skjervheim et al, 2012;Pettan and Strømsvik, 2013), and companies may run the risk of taking decisions based on models that are no longer valid.…”

“…Manzocchi et al (2008) evaluated the impact on the production of the uncertainty on sedimentological characteristics of shallow marine reservoirs combined with the fault uncertainty associated with fault density and permeability. Seiler et al (2009) presented a reservoir modeling workflow that evaluates the structural uncertainty, where reservoir models use elastic grids that are deformed according to the top and the basal simulated reservoir horizons. Wellmann et al (2010) develop a methodology for modeling the uncertainty in the geological structure of the subsurface related to the error, bias or imprecision in input data.…”