One of the most important issues for the characterisation of a reservoir and the assessment of the fluid flow behaviour is the role of faults, fractures, and micro-fractures. At different scales these reservoir features have a tremendous impact on the reservoir drainage and in the overall productivity of the field1 .

Faults can compartmentalise the reservoir into different units and the design of the production plan has to account for that. Fractures can be the main path for fluid flow and can strongly affect the production drive mechanism. Micro-fractures have a significant impact on permeability enhancement and drainage areas. The reservoir model should take into account all these features in order to reproduce the internal reservoir architecture and provide a reliable input for the numerical simulation of the fluid flow.

This paper proposes a methodology for integrated reservoir characterisation incorporating micro-fractures and fault analyses. A case study is presented for the characterisation of the reservoir properties and geometry and for the integration of micro-fractures in the final reservoir model.

Keywords:
reservoir, porosity, victor alcobia, geologic modeling, simulation, machine learning, reservoir characterisation, artificial intelligence, reservoir characterization, antonio costa silva
Subjects:
Reservoir Characterization, Unconventional and Complex Reservoirs, Geologic modeling, Naturally-fractured reservoirs, Carbonate reservoirs
Copyright 2000, Society of Petroleum Engineers
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