Title
Compaction trend estimation and applications to sedimentary basin reconstruction (BasinVis 2.0)
Author
Eun Young Lee
Faculty of Earth System and Environmental Sciences, Chonnam National University
Author
Johannes Novotny
Department of Computer Science, Brown University
Abstract
BasinVis 1.0, a MATLAB-based modular open-source program released in 2016, has been used for multiple application studies of sedimentary basin analysis and modelling in both academic and industry fields. Based on these studies and user feedbacks, we have improved the workflow, revised user interfaces and developed novel techniques for the compaction trend estimation of infilling sediments and its applications (decompaction process) to sedimentary basin reconstruction and visualization. These improved functions are implemented in BasinVis, upgrading the software to Version 2.0. This study introduces BasinVis 2.0 and demonstrates its functions through extensive case studies comprising of well data from the Perth Basin (Australia) and the Vienna Basin (Austria). Compaction trend estimation and decompaction process are crucial for analyzing numerical basin evolution (e.g., subsidence) and evaluating hydrocarbon reservoirs and geological storages. The compaction trend is estimated with improved accuracy using linear and exponential trending equations. The quality is evaluated using porosity-depth data from IODP Site U1459 and the industry well Houtman-1 in the northern Perth Basin, offshore southwestern Australia. Data from 38 industry wells in the southern Vienna Basin, central Europe, are applied to demonstrate the redesigned interfaces and new functions using the decompaction technique in the stratigraphic visualization process for basin reconstruction. The results provide useful and more detailed information for the compaction trends and the sedimentation setting during basin formation as well as changes during burial.
Keywords
BasinVis 2.0Compaction trendDecompactionBasin reconstructionSubsidencePorosity-depth relation
Object type
Language
English [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:1230018
Appeared in
Title
Applied Computing and Geosciences
Volume
5
ISSN
2590-1974
Issued
2020
Publisher
Elsevier BV
Date issued
2020
Access rights
Rights statement
© 2019 The Authors
University of Vienna | Universitätsring 1 | 1010 Vienna | T +43-1-4277-0