Meta or hybrid attributes extracted from seismic data through artificial neural networks enhance structural details in subsurface strata. Cretaceous units within the Penobscot prospect are devoid of such interpretations. Although these units are plausible zones for hydrocarbon generation and accumulation, the role of geologic structures responsible for such entrapment is poorly understood. The present study enlightens these latter geological structures through a case study that uses 3D time-migrated seismic data from the Penobscot prospect in the Scotian Basin. A modem data conditioning approach, used together with multi-attribute analyses and a supervised neural network, have brought out the minute details of subsurface geologic structures through a hybrid attribute, called the fault probability cube (FPC). The present fault displacement analysis reveals that the major fault of the prospect has evolved by the coalescence of different fault segments grown over space and time, and has demonstrated a predominant NNW-SSE structural trend. Such a geometry has structurally modulated the Cretaceous units (the Logan Canyon, the Dawson Canyon and the Wyandot Formations) giving rise to different horst-graben structures and fracture networks, as shown by the FPC attribute. This generates complicated structural deformation and accommodation space, thereby enabling different trapping zones for hydrocarbon accumulation within these units. Such a detailed structural understanding will provide significant input to the future exploration and development programme in the Penobscot prospect.

Mandal, A., Srivastava, E. (2018). Enhanced structural interpretation from 3D seismic data using hybrid attributes: New insights into fault visualization and displacement in Cretaceous formations of the Scotian Basin, offshore Nova Scotia. MARINE AND PETROLEUM GEOLOGY, 89, 464-478 [10.1016/j.marpetgeo.2017.10.013].

Enhanced structural interpretation from 3D seismic data using hybrid attributes: New insights into fault visualization and displacement in Cretaceous formations of the Scotian Basin, offshore Nova Scotia

Srivastava E.
Secondo
Conceptualization
2018-01-01

Abstract

Meta or hybrid attributes extracted from seismic data through artificial neural networks enhance structural details in subsurface strata. Cretaceous units within the Penobscot prospect are devoid of such interpretations. Although these units are plausible zones for hydrocarbon generation and accumulation, the role of geologic structures responsible for such entrapment is poorly understood. The present study enlightens these latter geological structures through a case study that uses 3D time-migrated seismic data from the Penobscot prospect in the Scotian Basin. A modem data conditioning approach, used together with multi-attribute analyses and a supervised neural network, have brought out the minute details of subsurface geologic structures through a hybrid attribute, called the fault probability cube (FPC). The present fault displacement analysis reveals that the major fault of the prospect has evolved by the coalescence of different fault segments grown over space and time, and has demonstrated a predominant NNW-SSE structural trend. Such a geometry has structurally modulated the Cretaceous units (the Logan Canyon, the Dawson Canyon and the Wyandot Formations) giving rise to different horst-graben structures and fracture networks, as shown by the FPC attribute. This generates complicated structural deformation and accommodation space, thereby enabling different trapping zones for hydrocarbon accumulation within these units. Such a detailed structural understanding will provide significant input to the future exploration and development programme in the Penobscot prospect.
2018
Mandal, A., Srivastava, E. (2018). Enhanced structural interpretation from 3D seismic data using hybrid attributes: New insights into fault visualization and displacement in Cretaceous formations of the Scotian Basin, offshore Nova Scotia. MARINE AND PETROLEUM GEOLOGY, 89, 464-478 [10.1016/j.marpetgeo.2017.10.013].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/688682
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