Journal of Petroleum Science and Engineering 77 (2011) 200–208Contents lists available at ScienceDirect Journal of Petroleum Science and Engineering j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p e t r o l 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 b Institute of Geosciences, P.O.Box 6152, University of Campinas, UNICAMP, 13083–870, Campinas, SP, Brazil Roxar do Brasil Ltda, Rua Assembleia 10, Sala 2412 CEP 20011–910, Centro Rio de Janeiro, RJ, Brazil a r t i c l e i n f o a b s t r a c t 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 × 106 m3. © 2011 Published by Elsevier B.V. Article history: Received 13 August 2010 Accepted 28 March 2011 Available online xxxx Keywords: Uncertainty analysis 3D geological modeling Volumetric estimation Namorado Field Case study 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 ⁎ Corresponding author. Tel.: + 55 19 3521 4659; fax: + 55 19 3289 1562. E-mail addresses:
[email protected] (J.F. Bueno),
[email protected] (R.D. Drummond),
[email protected] (A.C. Vidal),
[email protected] (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 provide a measure of uncertainty by means of multiple realizations involving lithofacies and petrophysical parameters. Despite the advantages 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 uncertainty behaviors as deterministic, discrete and stochastic uncertainties. 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 uncertainty 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: uncertainty that arises from the process of making a measurement at a and distal. Zoom of Fig. Fig. Each sequence corresponds to a distinct depositional environment and crustal rifting phase (Guardado et al. and usually massive. The Campos Basin stratigraphic sequence includes the Campos. and structural features. The area of Campos Basin is approximately 100. Bueno et al. The installation of the marine environment started with carbonate deposition in shallow-water conditions. Location map of the Namorado Field (offshore Brazil). According to geological and strategic criteria of oil production. encompassing more than 90% of the Brazilian reserves for oil and gas (Winter et al. Field description 2. 1).. but also from the way the static reservoir model is constructed. 1989). Hydrocarbon accumulation is controlled by turbidite sandstone pinchout. and (4) modeling: uncertainty associated with processing of data to create the model. resulting in the separation of South America and Africa (Guardado et al.. The Namorado Field was the first giant offshore Brazil oil field and has been productive since 1979 (Winter et al. Macaé. The reservoir of the Namorado Field occurs at depths between − 2900 m and − 3400 m. (2008). The Campos Basin encompasses dozens of oil-producing fields. Geological setting The Namorado Field is located in the Brazilian continental platform.. According to Beucher et al. (2) a transitional megasequence related to the initial drift. C.J.. uncertainty studies concerning the analysis of all input parameters used to build a static geological model are not often performed. 1998). The estimated in-place oil volume is 669 million bbl or 106 × 106 m3 (Guardado et al. in the central part of Campos Basin (Fig. and consequently in volumetric estimation. (2008) large uncertainties in gross volume estimation. 2.. 1989.. B showing the Namorado Field and the location of wells (black circles). 1989). and is composed of the Namorado sandstone (Meneses and Adams. medium-grained. the reader is referred to the web version of this article. The axis of the depositional paleochannel strikes NW–SE. with depths varying from 100 to 3000 m (Schlumberger. and (3) a marine megasequence related to the late drift phase. this basin can be divided into three compartments: proximal. The Campos Basin is composed of several hydrocarbon producing fields of Oligo-Miocene ages. the results of uncertainty analyses carried out in some Brazilian oil fields are not always made available by the oil companies.. and Lagoa Feia Formations (Guardado et al. The Campos Basin is a passive continental margin-type basin formed during the breakup of the Gondwana supercontinent.. Zoom of Fig. Ponte and Asmus (1976) proposed the division of the Campos Basin into three sedimentary megasequences from base to top: (1) a continental megasequence related to the rift phase. intermediate. This work focuses on the uncertainties associated with stochastic static reservoir modeling of the Namorado Field.000 km2. because these data are taken as confidential. More than 1600 wells have been drilled for over three decades of oil and gas exploration.) . 1. the static reservoir probabilistic model and its associated uncertainties have not been well established in the literature. and Namorado is a major field in this basin. The turbidite sandstones are up to 115 m thick. 2004). Sediment starvation occurred in the basin from the Cenomanian to the Maestrichtian as a consequence of tectonic subsidence. 1989). A. derive not only from measurement errors.1. This study focuses on the identification and quantification of uncertainties associated with the geological parameters used to model the Namorado Field static reservoir. followed by the siliciclastics of the Macaé Formation. 1990). According to Keogh et al. The Namorado Field was discovered by Petrobrás in 1975.. (For interpretation of the references to color in this figure legend. The main hydrocarbon producing block is at the center of the field. 2007). / Journal of Petroleum Science and Engineering 77 (2011) 200–208 201 specific spatial location.F. 1989). Although the deterministic volumetric estimate of the field has been known. and the constraint of the probabilistic oil volume estimation. and a relatively low influx of terrigenous sediments (Guardado et al. and has become the first giant offshore Brazilian oil field with reserves of more than 250 million bbl (Mendonça et al. The reservoir seals are marbles and shales of the hemipelagic sequence (Guardado et al. Despite the importance of reservoir uncertainties to predict the recovery of petroleum volumes and flow performance. A showing the Namorado Field. 1989). B. The Namorado field is a faulted structure separated into five blocks by normal faults. offshore Brazil. 2007). The Namorado sandstone is composed of turbidite sands deposited during the Cenomanian/ Turonian and is intercalated with shale and carbonates. eustatic sealevel rise. 2000). Campos is an important offshore basin. the incorporation of these uncertainties into the proposed workflow. Eight wells were cored and qualitative petrographic description is available. This algorithm calculates the amplitude to a family of bell-shaped functions (B-splines) using a local heuristic Fig. These surfaces were used as input for the horizon simulation in the second step. 3B). using stochastic modeling techniques to build the geological model based on geometrical. (3) population of the geological model with petrophysical parameters. 1990). Meneses and Adams. The workflow (Fig. and petrophysical properties of the reservoir. to grid building. 1989. Soleimani et al. diamictites and carbonates. approach (de Boor. In the third iteration. base and low-cases of the static model were defined. to petrophysical modeling. to facies modeling.F. the reservoir top was used as the reference surface for the reservoir organization. The eight faults mapped in the reservoir area were used to build the structural model (Fig.2. the workflow consists of a series of IPL (Internal Programming Language) scripts that execute a routine of modeling jobs. from clean to shaly sandstones. where the function is only approximated locally and all computation is deferred until classification is concluded (Hechenbichler and Schliep. They were grouped into three major lithotypes according to their overall character.. 2004).and low-case scenarios. and locally conglomeratic (Guardado et al. A total of 55 wells drilled and logged between 1975 and 1986 were used in this study.. 3. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 arkosic. The second iteration was carried out to address the uncertainty of the parameters used to construct the static model. gamma-ray (GR). which give the true vertical depth at the intersections of the well with the sedimentary units. from structural modeling. Workflow This study was conducted in the Roxar Irap-RMS geostatistical framework. 2008). 2) comprises the following steps: (1) construction of the structural model. In the first step the deterministic horizon surfaces were built using the Local B-spline algorithm. Stage 1: construction of the structural model The data consist of a set of depth markers measured along the wells. and finally to volume calculations. The reservoir top and bottom were defined in OpenDtect software. The workflow used to model the Namorado Field consisted of three phases. 1989. 1990. Fault F3 divides the Namorado field into two smaller blocks: the high-block (left of Fig. Bueno et al. The fault model was not incorporated in the structural simulation. each progressively more complex.202 J. (2) construction of the geological model. and the permeability is higher than 1 darcy (Guardado et al. 2005). For each parameter the high and low cases were relative to the mean value of the variable distribution. This kind of investigation is known as a ‘three levels full factorial’ experimental set-up. and (4) uncertainty analysis. 2. 2008). 3.2. in order to give the response variable for that particular scenario. In the wk-NN classification the class determination of each point not only takes into account the classes of k nearest neighbors among the points from the training set. y) that approaches the input data.1. from which structural and sedimentological information was derived for reservoir evaluation. This allows performing realistic uncertainty analysis since depth uncertainty is often a function of the well density. The well logs presented in the dataset are: density (RHOB). Each IPL workflow job involves the building of a full model.. while the other parameters are kept to the high-. The workflow set up is a scenario-based workflow where high and low cases around the base case are defined to each of the parameters under investigation. the multiple stochastic realizations were run. and sonic (DT). The dataset is currently available by the Brazilian National Agency of Petroleum (ANP). The wk-NN is a type of instance-based learning or lazy learning. the horizon simulation around the deterministic ones was used in order to introduce laterally varying uncertainties into the simulation. base. The initial phase comprised steps 1. shales. Workflow diagram used in the uncertainty study. 1978. The parameter under investigation is varied. The porosity of the sandstones lies between 20 and 30%. In the second step.and low-case scenarios (Keogh et al. neutron porosity (NPHI). In Irap-RMS. 3B) and the low-block (right of Fig. geological.to . 2. resistivity (ILD). After conversion of the seismic horizons picks into depth units. The algorithm used for horizon simulation was ordinary kriging. Three depositional sequences labeled 3. which is a method for classifying objects based on closest training examples in the feature space. conglomerates. 2 and 1 were found in the 3D seismic data. base. the highestranked contributors to uncertainty were used to constrain the oil field volume. The sum of these functions defines a function in (x. Stage 2: construction of the geological model The facies were defined by means of the weighed k-nearest neighbors (wk-NN) algorithm. and seismic horizons recorded at time units.. and petrophysical properties: coarse. 2 and 3 so that the high. Barboza. All interpreted surfaces are approximately parallel to this reference surface. but also the distance of each of these neighbors to the point in question. The construction of horizon surfaces was divided into two steps. The fault model created was used to construct the structural model for the high-. Database The Namorado Field is covered by a 3D seismic survey. Twenty-nine lithofacies were identified from the core samples. 3A). Meneses and Adams. 3. These depositional sequences had already been identified in previous works such as Johann (1997) and Souza (1997) as a succession of sandstones and shales. because correctly honoring varying fault locations in RMS is not easily automated. reproducing per-facies distribution as derived from the blocked well data. Although SIS does not define geological bodies. which corresponds to the free water level (FWL) (Fig. 8A) and P90. i. Porosity (Fig. The facies model (Fig. 4A) (Labourdette et al. 4B).e. 4C). In this first iteration. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 203 Fig. and a decrease of the reservoir facies in the eastern portion of the Namorado field. 6A). When these 2D vectorial properties are stacked vertically. 2008).4. 4A). based on probabilities calculated from well data and user-defined input (Srivastava.J.. normal and vertical directions in relation to the NW–SE direction of the paleochannel in the Namorado field (Fig. water saturation and net-to-gross models. 2008). and shaly sands (possible-reservoir). medium-grained sand (reservoir). and P10 cases (Fig. According to stratigraphic division of the field. which represents the direction of the paleodepositional channel. 5B) honored the field mean percentage of that facies preserved in wells (Fig. reservoir and non-reservoir) in the reservoir. The Bo factor was not included in the . defining the facies (reservoir. Two oil–water contacts (OWC) were defined according to the main fault (F3) that divides the Namorado Field into high and low blocks. Sequential Gaussian Simulation (SGS) was then used to populate grid cells. the elongation direction can be imposed through use of the variogram model (Martin. 100% in the reservoir facies. For all reservoir layers.e. In order to capture the reservoir heterogeneities. Variograms were developed in all direction for each facies from blocked well data. Stage 4: uncertainty analysis One-hundred realizations for the complete model were generated by varying seed number only. 3. The VPC gives the proportion of each lithotype per level in the flattened space. a grid cell resolution of 50 × 50 × 1 m was defined. Seifert and Jensen. For each block OWC was defined at the depth where water saturation first reaches. According to this criterion OWC were defined at a − 3100 m depth in the high block (Fig. base and high cases (Fig. 4B shows a higher content of the reservoir facies in the NW–SE direction. 1992. 3B). The spatial correlation of the porosity. a (one dimension) proportion curve representing the vertical evolution of facies proportions is obtained. B.. Kelkar and Perez. the zone below sequence 3 shows a higher content of the reservoir facies (Fig. This number was obtained from percentile 10% of porosity distribution in the reservoir facies after simulation (Fig. 1994. In the second iteration. the vertical trends being obtained with vertical proportion curves (Fig. grid. after transformation into the Gaussian scale. and the Simandoux method for the possible-reservoir facies. shale and mixed lithotypes (nonreservoir). This deterministic grid model can also be described vertically. C).8% of the core samples were classified correctly by the wk-NN triangular weight function. 5C). Fig. 8B) were picked as low-. Structural model for the Namorado field. facies. 2002). 6B). A deterministic grid model can be described in terms of proportions by building the global vertical proportion curve (VPC). and at a − 3155 m depth in the low block (Fig. facies evolution with depth (Fig. the percentage of each facies within the low. It reflects the vertical variations of the lithotype proportions and confirms the depositional process that governed the facies distribution (Ravenne et al. SGS is a kriging-based method in which unsampled locations are sequentially visited in a random order until all unsampled points are visited (Deutsch and Journel. 3. An interval average porosity cut-off N 20% was used to calculate the net-to-gross (NTG) ratio of each interval. uncertainties associated with parameters were addressed in iteration 1. The facies log defined in wells with the wk-NN algorithm was scaled and discretized to this grid resolution without any loss of heterogeneity. or is close to. 3. The indicator variograms were calculated for parallel. The results are then back-transformed to the initial structural system before volume calculations. possible-reservoir and non-reservoir) present in that cell. For each horizontal layer of the grid. 2002).F. and the validation showed that 92. porosity. Porosity and water saturation data were scaled up to grid resolution without loss of heterogeneity and checked for trends related to depth. parameters were ranked by STOIIP (Fig. SIS is an algorithm used to generate a discrete 3D facies parameter for the current realization. Stage 3: population of the geological model with petrophysical parameters Water saturation was defined for all 55 wells using the Archie method for the reservoir facies. 1999). 7B. NTG was then calculated for the geological models in each of the three reservoir layers from blocked well data. Each single column of the model can be defined by the proportion of each facies it contains (Fig. integrated laterally over the whole field. 7A) and water saturation are then simulated. Porosity and water saturation curves calculated from well data were used to model the properties of the reservoir and the possiblereservoir facies of the Namorado field.and high-case scenarios for structural. 4A). is fitted in the sedimentary system where the simulations are performed. Bueno et al. Structural division of the field into high and low blocks. base. the probability of occurrence of a facies can be extracted from the VPC and transferred as a 2D vectorial property. These three new groups were used as training examples in the wk-NN classification. To each cell in the parameter a facies code is assigned. and for all facies (i. A.3. P50. 5A) was built using the Sequential Indicator Simulation (SIS). possible-reservoir. Tornado style plot was used for ranking each parameter in terms of its contribution to the total uncertainty range in STOIIP (Fig.F. (2) oil–water contact in the low block. Then. By using this algorithm. azimuth and direction. 9B): (1) oil–water contact in the high block. base and high cases. 243 realizations of the workflow were run. a number is randomly selected from each of the N intervals. the parameters that are actually influential on the production were identified. the combination with all sensitivities at base value was tested. Xu et al.. To address uncertainties associated with variographic parameters like range. C. Well data distributed along columns defined by the proportion of each facies they contain. Maschio et al. 2005. the value of the uncertainty is sampled to the defined normal distribution. base and low values for each sensitivity were tested (Montgomery. base. uncertainty analysis. The three levels full factorial algorithm was used in this iteration. Using this option for each realization. (3) range of . because a previous PVT study had reported a base-case Bo factor around 1% uncertainty. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 Fig. a normal (Gaussian) distribution was adopted.. 2008). 4. This type of distribution was used in this stage. In addition.and high-case models were used to address uncertainties associated with 3D porosity. Low-. because it consumes less time than the low. A. 1979. In this step.204 J. and net-to-gross parameters. 2001). For the second iteration. which prescribes a subdivision of the distribution into N equiprobable intervals.. Global vertical proportion curve representing the vertical distribution of facies. water saturation. Experimental variograms are represented by dotted lines and the models by thick lines. 9A). The highest-ranked contributors to uncertainty were (Fig. Bueno et al. Latin Hypercube was used as sampling method. and theoretically and empirically a normal distribution best represents the uncertainty associated with any new sample of a geological parameter (Quirk and Ruthrauff. Indicator variograms along the wells per lithotype. all combinations of high. B. 2010). in order to achieve a better representation of the underlying distribution (McKay et al. A. B. B.07 × 106 m3 for P90. (1989) of 106 × 106 m3. . one at a − 3100 m depth in the high block. high block. Water saturation vs. base.and high-case parameters. These values are close to those found by Meneses and Adams (1990) around − 3200 m to − 3100 m. and (4) 3D water saturation in the oil zone. The variogram model describes the spatial correlation between the parameter of interest as a function of distance. and this may have been caused by several factors. In the third iteration. 5. In this iteration the three levels full factorial algorithm was used and 81 realizations of this workflow were run.J. The third major contributor to uncertainty was the range of variogram used to simulate porosity in the parallel direction to the field paleo-channel in the possible-reservoir facies (Fig. low block. Base-case scenario for the Namorado field 3D reservoir model showing facies distribution.04 × 106 m3 for P10 scenarios (Fig. the effect on the total uncertainty. 9B). where the aim is to investigate the effect of the different uncertainties/sensitivities related to each other. this caused a large impact in the uncertainty analysis.11 × 106 m3 for P50 and 134. 109. 10A. base. The P50 volume is close to that shown by Guardado et al. C. In the proposed workflow it was defined that OWC correspond to FWL. The possible-reservoir facies and its petrophysical properties can have an erratic distribution along the wells in the Namorado field causing its impact in the uncertainty analysis. combining all low-. and two OWC were found according to structural model of the field. or alternatively.and low-case models. depth diagrams showing oil–water contacts (OWC) for the Namorado Field: A. The two highest ranked contributors to uncertainty were oil–water contacts in high and low blocks (Fig. Facies distribution using the Sequential Indicator Simulation (SIS). 4. The low/base/ high algorithm is typically used in sensitivity studies. such as the choice of algorithm used to simulate the water saturation in the whole field or the method used to calculate Fig. Results The STOIIP obtained after the third iteration was: 92. 9B). the four highest ranked parameters determined in iteration 2 (Fig. but this relation could be in reality quite different. The purpose of the third iteration was to address the uncertainty associated with the main parameters ranked in the second iteration into the proposed workflow and consequently to constrain the hydrocarbon volume of the Namorado field. variogram used for porosity simulation in parallel direction in the possible-reservoir facies. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 205 Fig. 6. Facies distribution per well. As a single value for this contact was adopted for each block. B). The fourth main parameter that affected the volumetric calculation was the 3D water saturation. Probably the oil–water contact in the high and low blocks is not flat in the field. and another at a − 3155 m depth in the low block (Fig. Bueno et al. 9B) were used for addressing uncertainty in the high-.F. Oil–water contacts were defined based on water saturation. 7A). 206 J. and multiple realizations of a given scenario. porosity and water saturation. possible-reservoir facies. If capillary pressure data were available. and produced significant results. and C.F. Conclusions The workflow used in this study successfully integrated all the geological uncertainty scenarios. The sources of uncertainties related to the inaccuracy of the measurements were not taken into account. The major contributors to uncertainty were oil–water contacts in both high and low blocks. The ‘top 4’ contributors to the total uncertainty range in STOIIP were identified and the corresponding uncertainties were used to build the low. as has been shown by Keogh et al. In the Irap-RMS. may be another factor that could have generated this impact of water saturation in the calculation of uncertainties. (1989). A. A. other methods could be applied to calculate the water saturation. and 3D water saturation. fault modeling was not automated. net-to-gross ratio and oil–water contact uncertainties. base. this limitation is not significant. such as capillary pressure.11 × 106 m3. Histogram showing the total uncertainty range in STOIIP. After 81 realizations of all combinations of low-. The impact on the calculation of gross rock volume was Fig. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 Fig. Bueno et al. reservoir facies. such as the J-function. the hydrocarbon volume of the Namorado Field was established as varying from 92. and in the ranking of the impact of these parameters in volume estimation. B. base and high-case scenarios to the Namorado Field. 8. 5. which is very close to the deterministic value of 106 × 106 m3 presented by Guardado et al. limiting the rebuilding of grids where fault patterns have changed due to interpretation. As this study focuses on static modeling uncertainties. Porosity distribution per facies: B. The combination of different parameters: depositional facies. resulted in 243 hydrocarbon volume estimations.and highcase scenarios and ‘top 4’ parameters. A modeling workflow has been established to handle both multiple scenarios. water saturation. in which cell permeability is a proxy for capillarity (Beucher et al.07 to 134. Statistics for STOIIP before uncertainty analysis of the P90. The lack of certain petrophysical parameters.04 × 106 m3. followed by range of variogram used to calculate porosity in the parallel direction in the possible-reservoir facies.. P50 and P10 cases. Base-case scenario for Namorado field 3D reservoir model showing porosity simulation and division of the field in high and low blocks. (2008) who conducted a static modeling of the Glitne Field (North Sea). . 2008). The limitation of the proposed workflow is that structural modeling is restricted because the fault model was not incorporated into the simulation. The value obtained for STOIIP at P50 was 109. 7. 2008. Guardado.. in the proposed workflow all the sources of uncertainties were considered to quantify the variability linked to the construction of a reservoir in the static model. AAPG Bulletin 92.. A Pratical Guide to Splines. Gamboa.J.. / Journal of Petroleum Science and Engineering 77 (2011) 200–208 207 Fig.. Petroleum geology of the Campos basin.P.G. List of the ‘top 4’ contributors to the uncertainty range in STOIIP as identified in the Tornado plot.. H. Lucchesi. A. Tese de Doutorado da Universidade Federal do Rio Grande do Sul.A. with focus on the understanding of the behavior of the highest-ranked contributors to uncertainty in STOIIP.. G. 2005. and incorporate all uncertainties involved in the construction of a static model for the Namorado field. range and sill) involved in each geostatistical process were also considered. C. L.F. The most significant parameter at the top and the least significant parameter at the bottom. Oxford University Press. A. 1992.. but also the variability linked to the choice of the parameter. . C. Despite the limitation described above. 1978. Bueno et al. Deutsch. 9. statistics for STOIIP after uncertainty analysis of the P90. References Barboza. The main focus of this work was to identify. the uncertainty in fault modeling must be accounted for. 1989. Beucher. Análise estratigráfica do Campo de Namorado (Bacia de Campos) com base na interpretação sísmica tridimensional. quantify. 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