Appraising structural models using seismic data: Problem and challenges

Irakarama, Modeste; Cupillard, Paul; Caumon, Guillaume; Sava, Paul

doi:10.1190/segam2017-17791860.1

Cited by 2 publications

(2 citation statements)

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“…Herron, 2015), although increasing development on the basis of machine learning algorithms will potentially lead to improved algorithms in the future. Nonetheless, to manage possibly different interpretations and get a sense about how they quantitatively honor the initial seismic data, several authors have proposed to consider alternative geological structural models and then either generate synthetic seismic images by convolution (e.g., Lallier et al, 2012;Botter et al, 2016), or forward model wave propagation to generate synthetic seismograms (Irakarama et al, 2017). Computing misfit functions remains, however a significant challenge as cycle skipping is likely to occur in the presence of large interpretation uncertainties owing for instance to poor illumination.…”

Section: Uncertainties Related To Different Data Typesmentioning

confidence: 99%

3-D Structural geological models: Concepts, methods, and uncertainties

Wellmann

Caumon

2018

Advances in Geophysics

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178

103

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The Earth below ground is the subject of interest for many geophysical as well as geological investigations. Even though most practitioners would agree that all available information should be used in such an investigation, it is common practice that only a fraction of geological and geophysical information is used. We believe that some reasons for this omission are (a) an incomplete picture of available geological modeling methods, and (b) the problem of the perceived static picture of an inflexible geological representation in an image or geological model. With this work, we aim to contribute to the problem of subsurface interface detection through (a) the review of state-of-the-art geological modeling methods that allow the consideration of multiple aspects of geological realism in the form of observations, information and knowledge, cast in geometric representations of subsurface structures, and (b) concepts and methods to analyze, quantify, and communicate related uncertainties in these models. We introduce a formulation for geological model representation and interpolation and uncertainty analysis methods with the aim to clarify similarities and differences in the diverse set of approaches that developed in recent years. We hope that this chapter provides an entry point to recent developments in geological modeling methods, helps researchers in the field to better consider uncertainties, and supports the integration of geological observations and knowledge in geophysical interpretation, modeling and inverse approaches.

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Section: Uncertainties Related To Different Data Typesmentioning

confidence: 99%

3-D Structural geological models: Concepts, methods, and uncertainties

Wellmann

Caumon

2018

Advances in Geophysics

Self Cite

178

103

View full text Add to dashboard Cite

show abstract

“…Both in bottom-up and top-down approaches, the feedback coming from physical simulations (reservoir production data, subsidence, acoustic sensors, etc.) must be re-injected spatially to better characterize geological features and reduce uncertainties (e.g., Irakarama et al (2017)). This is a highly challenging problem, which calls for a common geological data model that is able to represent knowledge at multiple scales and transfer information gained from various sources.…”

Section: Introductionmentioning

confidence: 99%

RINGMesh: an Open Source Platform for Shared Earth Modeling

Bonneau¹,

Botella

Mazuyer³

et al. 2018

Proceedings

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Appraising structural models using seismic data: Problem and challenges

Cited by 2 publications

References 10 publications

3-D Structural geological models: Concepts, methods, and uncertainties

3-D Structural geological models: Concepts, methods, and uncertainties

RINGMesh: an Open Source Platform for Shared Earth Modeling

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