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  Turbidite Reservoir Characterization at Marlim Field,Campos Basin, Brazil Tatiane M. Nascimento ∗† , Paulo T. L. Menezes † and Igor L. Braga ∗∗ Invision Geophysics,Rua Barra do Pira´ı, 387 c1,Rio das Ostras, RJ, 28895-895  † DGAP/FGEL/UERJ,Rua S˜ ao Francisco Xavier 524, 4006a Rio de Janeiro, RJ, 20550-013 email: ptarsomenezes@pq.cnpq.br  (August 8, 2013) GEO-Example Running head:  High-Resolution AI inversion ABSTRACT Deep-water turbidite sedimentary systems can form prolific hydrocarbon plays. However,due its high complexcity and heterogeinity .1  INTRODUCTION High-resolution reservoir characterization studies allow a more detailed lithology-fluid pre-diction in the various stages of the reservoir life cycle. Commonly this is a complex task,where interpreters usually have to integrate a huge amount of data with different degreesof accuracy and spatial resolution, like 3D seismic data (uniform sampled areal informa-tion with low vertical resolution), well logs (high resolution distributed along irregularlydistributed well paths) and geological conceptual models.Inversion of seismic data is a widely used tool to estimate reservoir geometry and petro-physical characterization (Kelamis et al., 1995). One benefit of the seismic inversion is toincrease the frequency content of the seismic data, which is band-limited (Latimer et al.,2000). Furthermore, the result of inversion also allows the lithology differentiation and esti-mation of the fluid content, not only the interface geometry as given the amplitude attribute(Avseth et al., 2005; Contreras et al., 2005). Since the acoustic impedance is a propertyrelated to the layer, the inversion process adds information to the seismic interpretation,enabling greater inference of subsurface geology (Goodway et al., 1997).Seismic inversion procedures can be divided into two main categories (Bosch et al.,2010): deterministic and stochastic methods. Geoscientists tend to use the deterministicmethods as their first choice (Tetyukhina et al., 2010). The incorporation of structural andstratigraphic features in the inversion process decrease the inherent nonuniqueness of theinversion results (Merletti and Torres-Verd´ın, 2006). These inputs allow to differentiatebetween similar mathematical solutions of the basis of their geologic viability (Pendrel andVan Riel, 1997).A major issue of the seismic inversion procedure is the inherent nonuniqueness of the2  obtained solution. This is mainly due to the band-limited frequency content, missing thehighest and lowest frequencies even for noise-free data, of the registered seismic waves. Thiscan be partially mitigated by introduction of external independent  a-priori   information, e.g.coming from the wells, to estimate the low frequency and highest frequency content to beincorporated into the inverse model. In practice, the inversion of seismic data is morecomplicated (Bosch et al., 2010) due to; (1) presence of noise in real data (2) forward-modeling simplifications needed to estimate the solutions in a reasonable time, and (3)uncertainties in well-to-seismic ties (velocities used in depth to time conversions), waveletextraction, and in the links between reservoir and elastic parameters.Standard inversion workflow commonly use stratal slicing technique for building low-frequency model in model-based inversions (Russell and Toks¨oz, 1991). In some areas,the construction of this low-frequency model is hampered due to a wide variation in thelithology distribution throughout the investigated area. Examples in offshore Brazil includedeep-water channels fan complexes, switching delta lobe deposits, structural highs causedby faults and irregular deposition due to the movement of salt layers (salt tectonics). Thedescribed scenario corresponds exactly to what is found in the giant Marlim field (Fig. 1),where Oligocene and Miocene sand-rich turbidites form important reservoir rocks. Theseturbidites were deposited in deep-water settings associated with slope and continental risedeposits (Freire, 1989). The Marlim sandstones occur dominantly as tabular bodies withmore than 95% of massive sand with excellent porosity, up to 30% (Souza et al., 1989). Theporosity is controlled by carbonatic cementation, Carvalho et al. (1995) identified four dif-ferent diagenetic reservoir facies within Marlim sandstones. Deep-water turbidite reservoirs,like Marlim, usually show a complex distribution of sand bodies with thicknesses many timessmaller than the vertical resolution limit of the seismic data (Ribeiro, 2012). Therefore, a3  more detailed description of the reservoir can only be achieved through inversion procedure.In the present paper we show the results of a high-resolution deterministic inversionworkflow to recover acoustic impedance and predict porosity distribution of the Marlimturbidite reservoirs. The main advantage of the proposed workflow is to better estimate thelow-frequency model, the so-called HorizonCube (Brouwer et al., 2012), that incorporatestratigraphic knowledge of the main sedimentary sequences of Campos Basin in the studiedarea. Independent  a priori   information given by four available wells in the area playsa fundamental role in different steps of the proposed workflow. Besides the traditionalwell-to-seismic ties for wavelet estimation, well information were used to help in the seismo-stragraphic interpretation and build the high resolution low frequency model, and finallyproviding constraints to the absolute impedance and porosity estimations, calibrating ourinverted models. MARLIM FIELD DATASET The giant Marlim field is located in the northeastern portion of the Campos Basin, offshoreBrazil, in water depths ranging from 600m to 1200m (Fig. 1). The field was discovered in1985 with an initial estimate of over 6.4 billion STB with 1.97 billion barrels recoverable(Candido and Cora, 1978). Production started in 1991 and Marlim shortly became thelargest producing field in Brazil with daily production of 610,000 barrels (Johann et al.,2009).The reservoirs in Marlim field (Fig. 1) consist of an Oligocene/Miocene deep-waterturbidite system. They form a series of amalgamated sandstone bodies that are informallycalled “Marlim Sandstone” (Peres, 1993). The turbidite sedimentation occurred under the4
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