Caracterização de falhas utilizando dados de afloramento e sísmica de reflexão 3D, Bacia Rio do Peixe - Brasil
Fault zones accommodate deformation in a complex pattern, presenting themselves with different geometries, types of secondary structures, and changing the petrophysical parameters of host rocks. From a perspective of exploration of siliciclastic hydrocarbon reservoirs, understanding the complexit...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/52769 |
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| Resumo: | Fault zones accommodate deformation in a complex pattern, presenting themselves with
different geometries, types of secondary structures, and changing the petrophysical
parameters of host rocks. From a perspective of exploration of siliciclastic hydrocarbon
reservoirs, understanding the complexity of fault zones has become fundamental, since
these structures alter rock volumes, thus influencing fluid flow. The challenge of
understanding these structures requires the use of conventional methodologies, providing
relationships with the structural framework of the basin, as well as understanding the
deformation of a fault zone from the outcrop scale, where it is possible to observe
secondary structures such as deformation bands. In this research, fault zones in the Rio
do Peixe Basin were investigated at the outcrop scale, understanding the mechanical-stratigraphic influence of deformation bands, and fault detection and characterization
were also performed automatically using reflection seismic data. For this, structural,
sedimentological and petrophysical data were combined to analyze mechanically the rock
layers, and to characterize the deformation generated by deformation bands. Also,
seismic data were used for automatic fault detection through seismic attributes and deep
learning. Our results show the mechanical-stratigraphic influence of deformation bands
in a fault zone that indicate the same regional trend of NE-SW, E-W and NW-SE direction,
generating changes evidenced by our models in petrophysical parameters such as
porosity, permeability, Young's modulus and Poisson's ratio. The deformation bands
cross the sedimentary layers without being conditioned to their thickness, varying
structural parameters such as frequency, dip, geometry and thickness of the bands. Our
results also demonstrate the comparison between seismic attributes and deep learning
(DNN), in which DNN is more successful in detecting faults, identifying their subsidiary
segments with more strikes variation and number of minor faults. Seismic attributes are
shown to be conditional on noise in the seismic data. Furthermore, we interpreted and
mapped a new fault, which is aligned parallel to Fault Malta of E-W direction, with a central
negative flower structure. |
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