Abstract
This paper outlines a novel approach to an integrated 1D to 3D characterization and geomodeling of Vugular pore systems (VPS) in carbonate reservoirs. The study focused on capturing the various scales of the VPS and how they related to the 3D architecture.
An integrated approach for detecting, characterizing and modeling the multi-scale VPS has been employed by utilizing multi-disciplinary datasets, that span from 3D seismic volumes, borehole images, production data to petrographic analyses. These datasets were analyzed and corroborated in the 1D, 2D and 3D domains to validate and define the occurrence and architecture of the VPS within the reservoir. Eventually, a 3D geo-cellular model of the VPS is constructed by honoring diagenetic proxies and experiments, VPS flags, fluid flow behavior and seismic attributes geobodies.
VPS are observed at nearly all scales of datasets (i.e. geoscience and reservoir engineering data). They also occur at different scales of pore size ranging from millimeters to multi-meters. Many of them are even beyond the resolution of conventional whole core and basic well logs. A complete set of static and dynamic data allows a classification of the VPS into several classes based on their size, intensity and effect on reservoir properties and flow rates. Diagenetic proxies, well-based experimental variogram analyses and seismic-based geobody extraction further confirmed their architecture and distribution within 3D space. Their ramified patterns within the proximity of structural crestal areas tie quite consistently with well and seismic data. This architecture is further supported by possible hydro-dynamic corrosive fluid behavior that potentially had longer residence time in the crestal areas during late burial diagenesis stages.
A modeling-while-interpreting workflow is also configured to model the VPS in 3D interactively and confirm the VPS occurrence on a well-by-well basis. This method is applied directly to the 3D full-field model and is linked to an interpretation platform. This unique approach contributes to the reduction of ambiguity in subsurface data and analysis.
