Abstract

Abstract The Permo-Carboniferous reservoirs of central Saudi Arabia comprise important accumulations of natural gas and light oils. Some of the reservoirs exhibit low resistivity and low contrast resistivity (LRLC) phenomena. Low resistivity pay reservoirs often produce gas/oil with little or no water at very low resistivities. Low contrast resistivity pay zones, on the other hand, produce hydrocarbon at minimum resistivity contrast between hydrocarbon-bearing intervals and adjacent water-wet or shaley zones. Evaluating these types of reservoirs poses a major challenge to petrophysicists and petroleum engineers due to the difficulty in recognizing them on logs and quantifying their hydrocarbon potential when using simple resistivity-based petrophysical models. As a result, potential pay zones can be incorrectly evaluated or bypassed. The objectives of this study, therefore, are: 1) to understand the causes of LRLC pay in the study reservoirs through a detailed assessment of the textural and mineralogical composition of the rock, and 2) to validate a simple porosity-based methodology for recognizing LRLC pay in both old and new wells. To achieve the set objectives, 38 core samples and 107 thin sections were selected from two wells exhibiting low resistivity (Well-1) and low contrast resistivity (Well-2) phenomena, for detailed petrographic and mineralogical studies (SEM/XRD). Furthermore, a water salinity map was created to provide general salinity trends in the area. Well data combined with results of mineralogical and petrographic studies suggest that low resistivity and low contrast resistivity pay in the study reservoirs is the product of a complex mix of: 1) clay mineral types and its mode of distribution, 2) thin reservoirs below resistivity tool resolution, 3) significant grain size variation and microporosity, and 4) variable formation water salinities. This paper highlights the causes of low resistivity and low contrast resistivity phenomena in reservoirs in central Saudi Arabia. Furthermore, the applicability of a porosity-based methodology for recognizing LRLC phenomena was verified with promising results.

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