Hybrid modelling using neuro fractal for fractured reservoirs

Authors:
Nam H. Tran;Karen Valencia;Kien Tran;Sheik S. Rahman
Affiliations:
School of Petroleum Engineering, The University of New South Wales, Sydney, NSW, Australia;The University of New South Wales, Sydney, NSW, Australia;The University of New South Wales, Sydney, NSW, Australia;The University of New South Wales, Sydney, NSW, Australia
Venue:
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Year:
2006

Citing 3
Cited 0

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Practical application of fuzzy logic and neural networks to fractured reservoir characterization

Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
Integrated conditional global optimisation for discrete fracture network modelling

Computers & Geosciences

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Abstract

Due to geological reasons, fractured reservoirs are extremely heterogeneous. Modelling of these reservoirs has so far been considered complex and progress is still inadequate. This paper presents a novel and hybrid method to model discrete fracture networks in naturally fractured reservoirs. It involves investigation and systematic integration of tasks spanning cross-disciplinary areas: geological, statistical and artificial intelligence characterisation of natural fractures, rock and fracture mechanics. This paper also evaluates applications of fractal mathematics on characterising natural fracture distributions, especially discrete multifractal dimensions. A case study illustrates that discrete multifractal dimensions are greatly more suitable for such complex systems as natural fractures, compared to the commonly used single-fractal and statistical distributions.