Reservoir Quality Modeling and Streamline Simulation: An Application in Campos Basin, Brazil
Authors: Albertão Gilberto A. , Bampi Dirceu , Schwedersky Guenther 
Published: December 2007

Expanded Abstract
The uses of seismic data in the reservoir characterization process and of stochastic simulation algorithms to generate facies and petrophysical models have been increasingly used in reservoir management during the last years. Such integration process is particularly important in cases where the amount of well information is sparse. This is the case for most of the Campos Basin oil fields as well as for the field object of this study. This practice allows assessing uncertainties, which are not usually incorporated during the routine reservoir analysis.
The present paper describes a sequence of methods, which permitted to select a feasible 3-D geological model to be used in flow simulation, after modeling the reservoir quality in an oil field of Campos Basin, Brazil. Stochastic simulation algorithms were used to generate facies, porosity and permeability models: the acoustic impedance was used as a soft constraint for the facies modeling and the facies distribution conditioned the porosity realizations. Based on the geological knowledge of the reservoir, some porosity and permeability models were selected to be the basis of the flow simulation model.
The reservoir quality modeling was aimed at attaining models for some reservoir properties such as facies, porosity and permeability. The reservoir quality modeling comprised the following steps: (i) stratigraphic and petrographic characterization, (ii) seismic mapping, (iii) seismic inversion to acoustic impedance, (iv) facies modeling, (v) porosity modeling and (vi) permeability modeling. Facies modeling incorporated seismic data and used P-field simulation algorithm to generate seismofacies models, selected from a geological knowledge of the reservoir. Porosity modeling was performed by using a set of sequential Gaussian simulations with collocated cokriging. The seismic parameter impedance was used as a soft variable. The same geostatistical procedure used in porosity modeling was considered in the present work for permeability modeling.
Ten realizations for porosity and the same for permeability were performed. Seven combined models for porosity and permeability were randomly selected. Three other combined models were built using statistical treatment of the permeability and porosity realizations: average, minimum and maximum. Average was the combination of the average of the realizations for permeability and porosity. Minimum was defined as the combination of average minus standard deviation of the realizations for porosity and permeability. Finally, maximum was the combination of average plus standard deviation of the realizations for porosity and permeability. At the end of this step, 10 combined models were obtained.
A streamline simulator was used to simulate the performances of all of these 10 models. The same fine grid imported from the seismic interpretation and used in all previous steps was considered for these flow simulations. To simulate the real behavior of the oil field, the same well distribution and completion characteristics were incorporated in the simulator. Since this streamline simulator is monophase, horizontal wells were proposed to indicate the aquifer vertical action.
Forecast performances of simulations applied on the previously selected 10 models were analyzed. Minimum and maximum combined models constituted the curves envelope. The performances of total volume as well as of recovered volume were also considered. Depending on specific objectives, it is possible to select one model; in the present case, the model presenting the medium performance was chosen. The last step was performing an upscaling process in order to adapt the scale of the selected geological model to a black oil flow simulator.
These procedures have indicated more realistic images from the drainage system to supply the flow simulator and yield a more effective decision making on the reservoir management.