• Modelling of Type I fracture n...

Result: Modelling of Type I fracture network : Objective function formulation by fuzzy sensitivity analysis

Title:
Modelling of Type I fracture network : Objective function formulation by fuzzy sensitivity analysis
Authors:
TRAN, Nam H, RAVOOF, Abdul, NGUYEN, Tung, TRAN, Kien
Source:
Mathematical and computer modelling. 49(7-8):1283-1287
Publisher Information:
Kidlington: Elsevier, 2009.
Publication Year:
2009
Physical Description:
print, 9 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Logique mathématique, fondements, théorie des ensembles, Mathematical logic, foundations, set theory, Logique et fondements, Logic and foundations, Logique générale, General logic, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Analyse assistée, Computer aided analysis, Análisis asistido, Analyse numérique, Numerical analysis, Análisis numérico, Analyse sensibilité, Sensitivity analysis, Análisis sensibilidad, Calcul scientifique, Scientific computation, Computación científica, Convergence, Convergencia, Logique floue, Fuzzy logic, Lógica difusa, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Méthode optimisation, Optimization method, Método optimización, Méthode stochastique, Stochastic method, Método estocástico, Programmation mathématique, Mathematical programming, Programación matemática, 03B52, 49K40, 49XX, 65Kxx, Fracture inversion, Fuzzy sensitivity, Global optimization
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
School of Petroleum Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
College of Business Administration, The University of Tulsa, 600 S. College Ave, Tulsa, OK 74104, United States
ISSN:
0895-7177
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=21285220
Rights:
Copyright 2009 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.21285220
Database:
PASCAL Archive

Further Information

This paper advances the fundamental understanding in mathematical and computational modelling of discrete fracture networks (Type I). It presents a systematic procedure to solve the most important problem in modelling by global optimization - objective function formulation, which negates guesswork in objective function formulation by automatic selection of highly ranked components and their corresponding weighting factors. The procedure starts from real data to identify potential components of the objective function. The components are then ranked by fuzzy sensitivity analysis, based on their effects on the final objective function value and simulation convergence. The final fracture network inversion is subsequently realized and validated. Results of the study provide an explanation why previous methods such as stochastic simulations are not sufficiently reliable, compared to global optimization methods.