Resumo

ROJAS-CARO, Daniel Mauricio et al. 1D petrophysical characterization of reservoirs in the Canning Basin, Australia. Bol. geol. [online]. 2020, vol.42, n.3, pp.99-122.  Epub 31-Ago-2020. ISSN 0120-0283.  https://doi.org/10.18273/revbol.v42n3-2020004.

The petrophysical characterization of deposits plays an important role in the oil industry, primarily in the integral management and optimization of recovery processes. The following project presents the petrophysical and facies modeling for the formational units of the Grant Group and the Anderson deposit within the Block-Bunda-3D-2009 of the Canning Basin in Australia. This proposal was divided into two stages. The conceptual stage was based on the study of hydrocarbon migration and accumulation in the area, and the creation of an inventory from the information recorded in the Western Australia Geothermal and Petroleum Information Management System (WAPIMS). The second stage was developed considering that the quantity and distribution of shales present in the sandstone, have a great impact on the productivity of hydrocarbon reservoirs. Thus, the first step was to calculate the volume of shales through the linear index of gamma rays. Subsequently, the facies were modeled using neural networks and the results were compared with the lithological descriptions reported of the full diameter drilling cores. The effective porosity was modeled by recording the volumetric density of the rock and the type of distribution of clay; water saturation by Poupon-Leveaux correlation and the horizontal permeability model was generated with conventional core drilling analysis data. It is highlighted that the presence of pyrite, affected the response of volumetric density, neutron porosity and resistivity records for some wells in the area. Likewise, the acting hydrodynamism and the presence of meteoric water in the aquifers affected the response of the resistive electrical record, making the identification of water-oil contacts complex.

Palavras-chave : Characterization; Petrophysics; Reservoirs; 1D models; Facies; Neural networks.

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