Result: Combination of fuzzy ranking and simulated annealing to improve discrete fracture inversion

Title:
Combination of fuzzy ranking and simulated annealing to improve discrete fracture inversion
Authors:
TRAN, Nam H, TRAN, Kien
Source:
Mathematical and computer modelling. 45(7-8):1010-1020
Publisher Information:
Oxford: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 18 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 numérique. Calcul scientifique, Numerical analysis. Scientific computation, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Optimisation. Problèmes de recherche, Optimization. Search problems, Analyse numérique, Numerical analysis, Análisis numérico, Analyse sensibilité, Sensitivity analysis, Análisis sensibilidad, Convergence, Convergencia, Fonction objectif, Objective function, Función objetivo, Logique floue, Fuzzy logic, Lógica difusa, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Méthode optimisation, Optimization method, Método optimización, Programmation mathématique, Mathematical programming, Programación matemática, Recuit simulé, Simulated annealing, Recocido simulado, Ressource naturelle, Natural resources, Recurso natural, Sélection automatique, Automatic selection, Selección automática, Optimisation globale, Réseau discret rupture, Discrete fracture network, 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=18490930
Rights:
Copyright 2007 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

Operational research. Management
Accession Number:
edscal.18490930
Database:
PASCAL Archive

Further Information

Mathematical and computational modelling of discrete fracture networks is critical for the exploration and development of natural resource reservoirs. Utilizing the concept of fuzzy memberships, this paper advances the fundamental understanding in fracture network inversion and presents a systematic procedure to solve the most important problem in global optimization (simulated annealing): objective function formulation. First, a comprehensive field study identifies all potential components of an objective function. The components are statistical, geostatistical, mathematical and spatial measurements of fracture properties (location, orientation and size). The characteristic measurements can be input in parametric or non-parametric, discrete or continuum forms. Next, sensitivity analysis and fuzzy logic are combined to rank the candidate components based on their effects on the final objective function value and optimization convergence. The process negates guess works in objective function formulation by automatic selection of highly ranked components and their corresponding weighting factors. A case study is applied to a surface DFN in New York. The derived discrete fracture network is representative of the field data.