Incertitudes structurales en géomodélisation : échantillonnage et approche inverse

Nicolas Cherpeau. ( 2012 )
Université de Lorraine

Abstract

Subsurface modeling is a key tool to describe, understand and quantify geological processes. As the subsurface is inaccessible and its observation is limited by acquisition methods, 3D models of the subsurface are usually built from the interpretation of sparse data with limited resolution. Therefore, uncertainties occur during the model building process, due to possible cognitive human bias, natural variability of geological objects and intrinsic uncertainties of data. In such context, the predictability of models is limited by uncertainties, which must be assessed in order to reduce economical and human risks linked to the use of models. This thesis focuses more specically on uncertainties about geological structures. Our contributions are : (1) a stochastic method for generating structural models with various fault and horizon geometries as well as fault connections, combining prior information and interpreted data, has been developped ; (2) realistic geological objects are obtained using implicit modeling that represents a surface by an equipotential of a volumetric scalar field ; (3) faults have been described by a reduced set of uncertain parameters, which opens the way to the inversion of structural objects using geophysical or fluid flow data by baysian methods.

Download / Links

BibTeX Reference

@PHDTHESIS{,
    author = { Cherpeau, Nicolas },
     title = { Incertitudes structurales en géomodélisation : échantillonnage et approche inverse },
      year = { 2012 },
    school = { Université de Lorraine },
  abstract = { Subsurface modeling is a key tool to describe, understand and quantify geological processes. As
the subsurface is inaccessible and its observation is limited by acquisition methods, 3D models
of the subsurface are usually built from the interpretation of sparse data with limited resolution.
Therefore, uncertainties occur during the model building process, due to possible cognitive human
bias, natural variability of geological objects and intrinsic uncertainties of data. In such context,
the predictability of models is limited by uncertainties, which must be assessed in order to reduce
economical and human risks linked to the use of models.
This thesis focuses more specically on uncertainties about geological structures. Our contributions
are : (1) a stochastic method for generating structural models with various fault and horizon
geometries as well as fault connections, combining prior information and interpreted data, has been
developped ; (2) realistic geological objects are obtained using implicit modeling that represents
a surface by an equipotential of a volumetric scalar field ; (3) faults have been described by a
reduced set of uncertain parameters, which opens the way to the inversion of structural objects
using geophysical or fluid flow data by baysian methods. }
}