Shales can be distributed in sand through four different ways; laminated, structural, dispersed and any combination of these aforementioned styles. A careful analysis of well log data is required for the determination of shale distribution in sand affecting its reservoir quality. The objective of this study is to characterize the effect of shale distribution on reservoir quality of sands using well log data. The correlation of well data in terms of lithology has revealed four sand and three shale layers in Lower Goru Formation acting as a major reservoir in the study area. Our results indicate that the laminated type of shale distribution prevails at the Basal sand level, which does not affect its reservoir quality greatly. The remaining layers of variable vertical extent show a variety of shale distribution models affecting their reservoir quality adversely. We also present anisotropic rock physics modelling for AVA analysis at Basal sand level.
For economical production from a fractured reservoir, a characteristic analysis of the fracture parameters like its density and orientation within the reservoir is essential to improve the fluid flow during extraction. This study deals with the development of a proper anisotropic rock physics model for a media with multiple fracture sets to study the spatial distribution of important fracture parameters i.e., fracture density and orientation in the absence of sophisticated laboratory/wireline and pre-stack seismic data. The crest of hydrocarbon producing fault-bounded Balkassar Anticline in Northern Potwar, Upper Indus Basin, Pakistan is selected as a case study representing a potential zone for development of fractures at reservoir level (Sakesar Limestone). The methodology consists of the interpretation of 3D post-stack seismic and conventional wireline log data to demarcate the reservoir containing fractures. The Ant-tracking discrete fracture network (DFN) attribute is applied on 3D post-stack seismic data to obtain an initial estimate about the presence of fracture corridors and their orientations. Based on this initial estimate, a proper rock physics model has been developed utilizing inverse Gassmann relations, T-matrix approximation, and Brown and Korringa relations. The output from the developed rock physics model has been displayed in the form of 13 effective independent elastic stiffness constants (monoclinic symmetry–representing media comprising of multiple fracture sets) as a function of fracture densities and azimuthal fracture orientations. A clear decreasing trend in effective elastic stiffness constants with increasing fracture densities can be observed. Similarly, a periodic trend of effective elastic stiffness constants with fracture orientations can be observed. These trends are more or less expected, but they would have been difficult to quantify without a proper rock physics model. The use of independent effective elastic constants for the generation of synthetic seismic amplitude versus angle and azimuth (AVAZ) data and its correlation with observed seismic AVAZ data in a geostatistical sense has been discussed.
Porosity is a key parameter for reservoir evaluation. Inferring the porosity from seismic data is often challenging and prone to uncertainties due to number of factors. The main aim of this paper is to show the applicability of seismic inversion on old vintage seismic data to map spatial porosity at reservoir level. 3D-seismic and wireline log data are used to map the reservoir properties of the Lower Goru productive sands in the Gambat Latif block, Central Indus Basin, Pakistan. The Lower Goru formation was interpreted with the help of seismic and well data. Interpreted horizons are thus further used in model-based seismic inversion techniques to map the spatial distribution of porosity. Well-log data are used in the construction of low acoustic impedance models. Calibration of reservoir porosity with inverted acoustic impedance is achieved through well-log data. The results from model-based inversion reasonably estimate the porosity distribution within the C-sand interval of the Lower Goru Member. After post-stack inversion, the porosity values at wells Tajjal-01, Tajjal-02 and Tajjal-03 are 10%, 8% and 12%, respectively. Porosity values calculated from post-stack inversion at the corresponding well locations are in good agreement with the borehole-derived porosity.
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