Abstract
Reservoir static modelling plays a fundamental role in the evaluation phase of a petroleum field. Integrated modelling allows a better understanding of how the local geology and depositional systems are related through the distribution of facies and petrophysical properties within the reservoir. In this study, geological static models of the siliciclastic Carapebus Formation of Campos Basin were built using subsurface data. The applied methodology was divided into five phases: (1) establishment of a conceptual model, (2) building of a structural model, (3) generation of 100 realizations of lithofacies using sequential indicator simulation, (4) generation of 100 realizations of porosity and permeability using sequential Gaussian simulation, and (5) validation of models by targeting both statistical and geological consistency. The obtained models are consistent and honor the conditioning data. A lithofacies constraint is crucial to better characterize the petrophysical properties distribution of the reservoir. A Dykstra-Parsons coefficient of V=0.52 characterizes this reservoir as moderately homogeneous.
Highlights
The geological models, often called reservoir static models, play an essential role in the understanding of intrinsic spatial characteristics and features of the reservoirs
Whereas sequential indicator simulation is suitable for categorical variables, the sequential Gaussian simulation is applied to continuous variables
This study aims at building a 3-D geological model of the turbidite reservoir of the Carapebus Formation in the Campos Basin, in order to better understand and characterize reservoir heterogeneities
Summary
The geological models, often called reservoir static models, play an essential role in the understanding of intrinsic spatial characteristics and features of the reservoirs. These models have been important to predict reservoir performance because they incorporate several information, such as static petrophysical properties within the stratigraphic layers and structural framework. Cosentino (2001) and Deutsch (2002) point out that it is necessary to create consistent 3-D geological models. These authors suggest the application of geostatistical algorithms of sequential simulation, such as Gaussian and indicator to conduct the modelling process. Whereas sequential indicator simulation is suitable for categorical variables, the sequential Gaussian simulation is applied to continuous variables
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