Constraining uncertainty in volumetric estimation: A case study from Namorado Field, Brazil Juliana Finoto Bueno a, ⁎, Rodrigo Duarte Drummond a,1 , Alexandre Campane Vidal a,1 , Sérgio Sacani Sancevero b,2 a Institute of Geosciences, P.O.Box 6152, University of Campinas, UNICAMP, 13083–870, Campinas, SP, Brazil b Roxar do Brasil Ltda, Rua Assembleia 10, Sala 2412 CEP 20011–910, Centro Rio de Janeiro, RJ, Brazil abstract article info Article history: Received 13 August 2010 Accepted 28 March 2011 Available online 6 April 2011 Keywords: Uncertainty analysis 3D geological modeling Volumetric estimation Namorado Field Case study This paper describes 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 was built and a method to quantify the uncertainty associated with geological parameters was tested. The proposed workflow was developed in the Irap-RMS software and comprised the following steps: construction of the structural model; construction of the geological model; population of the geological model with petrophysical parameters, and uncertainty analysis. To construct the static reservoir model, the low, base and high cases of each uncertainty parameter were defined and used, and all combinations of these parameters were tested. The uncertainties related to the choice of parameters such as the variogram characteristics (type, range, and sill) involved in each geostatistical iteration were included into the workflow. The highest ranked contributors to uncertainty in Stock Tank Oil Initially in Place (STOIIP) were oil–water contacts, range of variogram used to calculate porosity in possible-reservoir facies, and 3D water saturation. The uncertainties related to the main parameters that affect the volumetric calculation were incorporated into the proposed workflow. The hydrocarbon probabilistic volume established for the Namorado Field varies from 92.07 to 134.04 × 10 6 m 3 . © 2011 Published by Elsevier B.V. 1. Introduction The available data for oil and gas fields are in general not enough to minimize the uncertainties related to the construction of reservoir models. The understanding of uncertainties involved in reservoir modeling is an essential tool to support decisions in the petroleum industry. The knowledge of uncertainty management related to prediction of hydrocarbon volumes has increased in the last decades, as a result of reliable 3D geological models made available by improvements in computer processing. A successful geological model should represent the ‘real’ situation as accurate as possible. However, the ‘real’ geological situation is often unknown, and the model represents an interpretation based on limited assumptions of what is likely to occur between data points (Lelliott et al., 2009). When soft and hard data are not enough to define the distribution of parameters between data points, stochastic algorithms can be used to provide a measure of uncertainty by means of multiple realizations involving lithofacies and petrophysical parameters. Despite the advan- tages of using deterministic methods to calculate hydrocarbon reservoir volumes in simple and understandable ways, the uncertainties inherent to each input data set used to build 3D static reservoir models cannot be expressed in a single deterministic realization. According to Zabalza-Mezghani et al. (2004) the sources of uncertainties, in reservoir engineering, can be classified as anywhere within the reservoir modeling workflow. Such uncertainties are associated with: the static model, upscaling, fluid flow modeling, production data integration, production scheme development, and economic evaluation. These authors classified the different uncer- tainty behaviors as deterministic, discrete and stochastic uncer- tainties. Lelliott et al. (2009) grouped the sources of uncertainties related to geological modeling into: data density (the density of boreholes used to construct the model); data quality (quality of the data used to construct the model, including borehole elevation, sample type, drilling method and logging quality); geological complexity (geological variability throughout the site); and modeling softwares. Mann (1993) suggested four main categories of uncertain- ty in geology: (1) variability: the inherent natural variability that exists in geological objects; (2) measurement: uncertainty caused by imperfections in the measurement procedure; (3) sampling: uncer- tainty that arises from the process of making a measurement at a Journal of Petroleum Science and Engineering 77 (2011) 200–208 ⁎ Corresponding author. Tel.: + 55 19 3521 4659; fax: + 55 19 3289 1562. E-mail addresses: juliana.bueno@ige.unicamp.br (J.F. Bueno), rdrummond@ige.unicamp.br (R.D. Drummond), vidal@ige.unicamp.br (A.C. Vidal), sergio.sancevero@roxar.com (S.S. Sancevero). 1 Tel.: +55 19 3521 4659; fax: +55 19 3289 1562. 2 Tel./fax: +55 21 2222 1941. 0920-4105/$ – see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.petrol.2011.03.003 Contents lists available at ScienceDirect Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol