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Hydrocarbon Reservoir Modeling: Comparison Between Theoretical and Real Petrophysical Properties from the Namorado Field (Brazil) Case Study

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Hydrocarbon Reservoir Modeling: Comparison Between Theoretical and Real Petrophysical Properties from the Namorado Field (Brazil) Case Study Hydrocarbon reservoir modeling: comparison between theoretical and real petrophysical properties from the Namorado Field (Brazil) case study. Hashimoto, Marcos Deguti, Student from the Master in Oil Engineering E-mail: [email protected] 1. Abstract In reservoir characterization and modeling, due to information-acquisition’s high costs, frequently only indirect measurements of the subsurface properties such as seismic reflection data is available. In the worst-case scenario, only regional geological information is at disposal. In an attempt to provide deeper insights over the study area, with low costs, modeling synthetic reservoirs has been a reliable tool to better characterize reservoir/prospects. In this work two synthetic hydrocarbons reservoirs were modelled recurring to two different approaches to characterize Earth’s subsurface petrophysical (facies, porosity and permeability) and elastic (P-wave, S-wave and density) properties. In the second half of 2013, during the IST (Instituto Superior Técnico) Internship, a synthetic reservoir was conceived and modeled using Namorado Field’s (Campos Basin, Rio de Janeiro, Brazil) as reference. During this intern public data, knowledge, papers, books and dissertations were gathered. In order to validate and certify this outcome, a new synthetic reservoir was proposed, but this time using real data for this field provided by the Brazilian Oil & Gas Agency (ANP). This dissertation addresses the comparison between the theoretical and real synthetic reservoir results, validating the first reservoir step-by-step. The major conclusion reached confirms that the theoretical synthetic reservoir outputs reliable results, however with caution in some of the modelled properties. Keywords: Hydrocarbon synthetic reservoir, Reservoir Modeling, Rock Physics Model, Petrophysical properties, Namorado Field, Campos Basin (Brazil). 2. Introduction The objective of this work is to compare the results obtained by two different synthetic reservoirs models: a theoretical-approached built during the internship at CERENA, using public papers and books; and the real-approached using real data provided by ANP (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis). This paper will present both reservoir modeling procedures step-by-step, including all related theory and parameters used. At last, all the obtained results from both models will be presented and compared to each other. Finally, the pertinent conclusions will be presented. The real data, necessary for modeling the second reservoir, were provided by The Brazilian Oil and Gas Agency (BDEP/ANP). The request was made through the Department of Mines and Petroleum Engineering of the Escola Politécnica (University of São Paulo - USP). 1 2.1. ANP/BDEP Data ANP provided the data used in this study to model the real-approached reservoir. The request involved core plugs’ descriptions and the following loggings: sonic (DT), gamma ray (GR), neutron (NPHI) and density (RHOB) from six wells: 1RJS 0019 RJ, 1RJS 0387 RJ, 3NA 0005A RJS, 3NA 0021B RJS, 3RJS 0393D RJ, 4RJS 0042 RJ. It is important to note that not all wells have continuous measurements or all the four loggings at the same depth. It was applied a data conditioning: first, it was correlated the loggings measurements and the Namorado’s Sandstone and shale. Other lithologies were not considered within this study. The second filter applied was the logging depth. In order to keep it as much real as possible it was used only data from the Namorado Fields depth, which ranges from 2940 to 3300 meters (Barboza, 2005). 3. Campos Basin Campos Basin is located between the northern coast of Rio de Janeiro (Brazil) and the southern coast of Espírito Santo (Brazil). This basin has approximately 100,000 km² and more than 1,600 wells drilled over nearly four decades. This Basin was formed during the breakup of the supercontinent Gondwana (about 140 Ma or millions of years ago) creating the Atlantic Ocean and dividing the current South American and African continent. This rupture results from the action of distensive forces producing a rift valleys system and developing horsts, grabens and half-grabens, bounded by synthetic and antithetic faults (Gabaglia, 1991). Two structural highs surrounds Campos Basin - one from the North (Vitória) and another from the South (Cabo Frio). Figure 1 exhibits the Campos Basin’s and Namorado Field’s location and shape. Figure 1 - Location of the Campos Basin and Namorado Field (from Bacoccoli et al., 1980). 3.1. Namorado Field Namorado field (Figure 1) is located in west-central Campos Basin portion with an estimated area of 20 km2, approximately 80 km away from the coast and its water depth ranges 140 to 250 meters. Discovered in 1975, oil production began in June 1979 employing two platforms (PNA-1 and PNA-2) and the development itself began in December 1982. Its vertical depth ranges 2.940 and 3.300 meters (Barboza, 2005) with an average thickness of approximately 300 meters. Furthermore, according to Cruz (2003) the average porosity is 26%, the average oil saturation is 75% and the average permeability is 400 mD. The oil API degree is 28° and the viscosity is close to 1 centipoise (cP). Regarding the depositional evolution and according to Barboza (2005), Namorado Field has four different turbidite systems. In general, the depositional model proposes a turbidite stack, with narrow 2 channels stack at the base section, tending to amalgamated geometry channels stack at the top. Figure 2 presents the depositional evolution diagram. Figure 2 - Namorado Field's depositional evolution diagram (from Barboza, 2005). 4. Synthetic Reservoir Workflow 4.1. Structural Model The first step of the reservoir modeling comprised building the structural model. Given the lack of real seismic, a structural model was developed from the start with the following attributes: siliciclastic channels system, anticlinal geometry and a normal fault (45° dip) perpendicular to the anticline’s axis plane. The model was divided into 151 x 200 x 300 cells grid (x, y, z), each one with 25 m x 25 m x 1 m resulting in a 3.75 Km x 5.00 Km x 300 m grid. The synthetic reservoir has three main layers (same 100-meter height). Figure 3 presents the structural model’s bulk volume. Figure 3 - Reservoir's structural model. 4.2. Facies Model Lithofacies modeling was the next step. In order to reach maximum Namorado Field’s characteristics, the synthetic model tried to reproduce its depositional evolution. The sedimentary model was conceived using three different siliclastic channels pattern accordingly to the Figure 4 and Table 1 in which is presented the channels attributes parameters. Sand represents reservoir rock while shale, the non-reservoir rock. Figure 5 presents the final Facies model that comprises sand and shales. Table 1 - Silicilastic channels attibutes divided in layers Attribute/Layer Top Intermediate Base Amplitude (m) 500 600 800 Wavelength (m) 1500 1500 750 Width (m) 1200 500 150 Figure 4 - Channel’s attributes diagram Thickness (m) 10 10 10 3 Figure 5 - Both reservoir's facies model (yellow represents sand and grey represents shale). The same structural and facies model were used to both modeling approaches. However, the following properties, accordingly to the theoretical and real approach and to the Facies Model, were differently calculated or simulated. In other words, cells filled with sand or shale suffers different simulation or calculations. The following sections will present each procedure and its results. 4.3. Properties Model 4.3.1. Porosity Both reservoir’s porosity were simulated through DSS (Direct Sequential Simulation) (Soares, 2011) algorithm, although, the conditional distributions used were different. The theoretical reservoir, or reservoir A, used statistical parameters data (mean, standard deviation, maximum and minimum) extracted from Fonseca (2011) while the real approach reservoir, or B, used effective porosity calculated employing RHOB log data provided by ANP (Equation 1). ρ푚푎푡푟푥 − ρ푅퐻푂퐵 푙표푔 ρ푚푎푡푟푥 − ρ푠ℎ푎푙푒 휑푒푓푓푒푐푡푣푒 = − V푠ℎ푎푙푒 ( ) (1) ρ푚푎푡푟푥 − ρ푓푙푢푑 ρ푚푎푡푟푥 − ρ푓푙푢푑 Table 2 organizes their main statistical parameters. Table 2 - Porosity Conditional Distribution's Statistical parameters Reservoir A Reservoir B Porosity (%) Sand Channel Shale Sand Channel Shale Fonseca Distribution Fonseca Distribution Average 24,96 24,86 3,81 5,44 14,52 8,26 Std. Deviation 2,80 4,85 4,88 3,30 6,92 4,74 Minimum 0,01 12,22 0,01 0,01 0,10 0,06 Maximum 33,53 33,30 20,42 16,05 36,27 14,99 4.3.2. Permeability Both reservoirs’ permeability were simulated through Co-DSS (Direct Sequential Co-Simulation with Joint Distribution) (Horta et al., 2010) algorithm using porosity as secondary variable. Assuming the correlation between permeability and porosity, the Kozeny-Carman equation was used to calculate 4 permeability using porosity (as shown on Equation 2). The reservoir A used statistical parameters data (mean, standard deviation, maximum and minimum) from Fonseca (2011) study. 1 Φ3 퐶 Φ3 퐾 = 푑2 퐾 = 푑2 (2) 72 (1 − Φ)2 ∙ 휏 72 (1 − Φ)2 ∙ 휏 Where Ф is the porosity, d is the average grain diameter and 휏 is the tortuosity. Regarding both reservoirs, a coefficient C was added to infer more realism (Equation 2) to the simulation, according to parameters extracted from Fonseca (2011) (Table 3). Table 3 - Permeability Conditional Distribution's Statistical parameters Reservoir A Reservoir B Permeability Sand Channel Shale (mD) Sand Channel Shale Fonseca Distribution Fonseca Distribution Average 562,40 562,75 1,39 1,42 242,09 1,40 Std. Deviation 418,98 321,48 3,61 3,56 328,58 1,64 Minimum 0,10 37,12 0,10 0,01 0,01 0,01 Maximum 3000 1530,14 58,98 22,07 3264,08 5,92 4.3.3. Density The reservoir A’s density was calculated using the Equation 3 and Table 4. 휌 = Φ ∙ 휌푓푙푢푑 + (1 − Φ) ∙ 휌푚푎푡푟푥 (3) Table 4 - Mineral, density and proportion used on reservoir A Mineral Density (g/cm3) Sand Channel (%) Shale (%) Clay 2,40 4 70 Quartz 2,65 56 20 Feldspar 2,63 40 10 On the other hand, the reservoir B’s density was simulated through DSS (Direct Sequential Simulation) algorithm, using the RHOB data provided by ANP as conditional distribution (Table 5).
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