The geochemical characterization of the Mungaroo Formation rocks shows the presence of kerogen type II/III and III which characterizes the Exmouth Plateau sub-basin as a gas prone basin. Using 13 burial histories constructed from well data, we identified three tendencies: One with high sedimentation rates between the Middle Triassic and Upper Triassic (69.3-95.3 m/Ma), another one with high sedimentation rates between the Lower and Upper Cretaceous (67-158 m/Ma), and the last one with low sedimentation rates (18-40 m/Ma) during the Upper Cretaceous until present time. All these trends defined active generation zones (or gas kitchens) between 2,000 and 4,400 km2. High sedimentation rates during the Cretaceous and Triassic times were key to the burial history of the Mungaroo Formation because they allowed these rocks to reach the required depths to transform its organic matter. In contrast, in the area with low sedimentation rates, radiogenic heat flow was the trigger for the transformation of the organic matter. The generation/expulsion of hydrocarbons from these shales occurs since 100 My, consequently explaining the large gas accumulation found in the sub-basin. Currently, the Mungaroo formation is in the gas generation window at depths of 4,500 to 5,500 m below sea level. The shales of this formation show TOC% values higher than 2% reaching the gas generation zone (Ro% >1.3) and suggesting its potential as an unconventional gas reservoir. However, geomechanical features such as low fragility, under pressure, and thickness, condemns its unconventional potential.
Seismic stratigraphy becomes a useful tool when it comes to 3D lithology distribution, since it gives the interpreter insights of the facies most likely to be present in a certain sedimentary environment. On the other hand, it is also the main input information while modeling petrophysical properties like water saturation, effective porosity and permeability, which are critical in the process of evaluation of a hydrocarbon reservoir. In this context, techniques such as seismic inversion allows the geoscientists to get 3D models of P-impedance, S-impedance and density, which are used as the main input to estimate the reservoir petrophysical properties just mentioned and additionally useful parameters used as a lithology indicator. This paper proposes a workflow to achieve the goal of integrating seismic stratigraphy, seismic inversion and attributes to get a lithology 3D model. Now, to get a suitable correlation between the facies interpreted using well logs and core data with the elastic properties, rock physic templates (RPT’s) were made where proper elastic modulus was carefully chosen to define probability distribution functions (PDF’s) for each facies defined in the correlation wells. On the other hand, based on a set of stratigraphic surfaces created on a different study, 3D models of P-impedance, S-impedance and density were obtained from seismic inversion so that the RPT’s could be built. For this specific instance, only a set of the elastic properties and seismic attributes offered a suitable correlation with the facies defined in the calibration wells. Moreover, the probability distribution functions (PDF’s) already generated allowed the distribution in 3D and the definition of the ranges in which each facies previously stated varies for the elastic modulus estimated.
La caracterización petrofísica de yacimientos desempeña un rol importante en la industria petrolera, siendo primordial en el gerenciamiento integral y la optimización de procesos de recuperación. El siguiente trabajo planteó el modelado petrofísico y de facies para las unidades formacionales del Grupo Grant y el yacimiento Anderson dentro del Bloque-Bunda-3D-2009 de la cuenca Canning en Australia. Esta propuesta fue dividida en dos etapas. La etapa conceptual se basó en el estudio de la migración y acumulación de hidrocarburos en el área, y la creación de un inventario desde la información registrada en el Sistema de Gestión de Información Geotérmica y de Petróleo de Australia Occidental (WAPIMS). La segunda etapa se desarrolló considerando que la cantidad y distribución de lutitas presentes en las areniscas, tienen un gran impacto en la productividad de los yacimientos de hidrocarburos. Así, el primer paso fue calcular el volumen de lutitas a través del índice lineal de rayos gamma. Posteriormente, se modelaron las facies mediante el uso de redes neuronales y los resultados fueron comparados con las descripciones litológicas reportadas de los núcleos de diámetro completo de perforación. La porosidad efectiva fue modelada mediante el registro de densidad volumétrica de la roca y el tipo de distribución de arcilla; la saturación de agua mediante la correlación de Poupon-Leveaux y el modelo de permeabilidad horizontal fue generado con los datos de análisis convencionales de núcleos de diámetro completo de perforación. Se resalta que la presencia de pirita afectó la respuesta de los registros de densidad volumétrica, porosidad neutrón y de resistividad para algunos pozos del área. Igualmente, el hidrodinamismo actuante y la presencia de agua meteórica en los acuíferos incidió en la respuesta del registro eléctrico resistivo, resultando complejo la identificación de contactos agua - hidrocarburo
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