Print this pageAAPG ANNUAL CONFERENCE AND EXHIBITION
Making the Next Giant Leap in Geosciences
April 10-13, 2011, Houston, Texas, USA
Constraining Uncertainty in Static Reservoir Modeling: A Case Study from Namorado Field, Brazil
(1) Department of Geology and Natural Resources, University of Campinas, Campinas, Brazil.
(2) Roxar do Brasil Ltda, Rio de Janeiro, Brazil.
The understanding of uncertainties involved in reservoir modeling is an essential tool to support decisions in the petroleum industry. This study focused on the reservoir-modeling case of Namorado, an oil field located in offshore Brazil, the workflow, tolls and benefits of a 3D integrated study with uncertainties. A geological uncertainty study was initiated to identify and quantify the input parameters of greatest impact in the reservoir model. In order to rank reservoir uncertainties, a series of static models were built, a method to quantify the uncertainty associated with geological parameters was proposed, and all combinations of these parameters were tested.
The proposed workflow comprises the following steps: (1) construction of the structural model - using depositional sequences and major faults found in 3D seismic data, and depth markers measured along the 55 wells; (2) construction of the geological model - facies were defined by using the weighed k-nearest neighbors algorithm, then facies model was built with Sequential Indicator Simulation; (3) populate the geological model with petrophysical parameters - Sequential Gaussian Simulation was used to populate grid cells with porosity and water saturation models; and (4) uncertainty analysis. After the stages described above, 100 realizations of complete model were generated by varying seed number alone. In this first iteration parameters were ranked by STOIIP and P90, P50 and P10 cases picked as low, base and high-case for structural, grid, facies, porosity, water saturation and net-to-gross models. In the second iteration, addressing uncertainties associated with parameters was used. In this step, the parameters that are actually influent on the production response were identified and 243 realizations of the workflow were run. In the third iteration, the highest parameters ranked in the second iteration were used for addressing uncertainty in the high, base and low-case models, and 81 realizations of this workflow were run with the three levels full factorial algorithm.
The identified highest ranked contributors to uncertainty were: oil-water contact in the field; range of variogram used for porosity simulation; and water saturation. The workflow used in this study successfully integrated geophysical and geological data, and all geological uncertainty scenarios. A modeling workflow has been established to handle both multiple scenarios, and multiple realizations of a given scenario.