# Integrated 3D forward stratigraphic and basin modeling of the Santos Basin, offshore Brazil: Implications of sedimentary depositional facies and CO2 migration for silicification of continental carbonate reservoirs

Alina-Berenice Christ, Marie-Christine Cacas-Stentz, Nathalie Collard, Fadi Henri Nader, Elisabeth Bemer, Rodrigo Brandão Bunevich, João Paulo Borges Gomes, Olinto Gomes de Souza, Maria Anna Abreu de Almeida dos Reis

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Alina-Berenice Christ, Marie-Christine Cacas-Stentz, Nathalie Collard, Fadi Henri Nader, Elisabeth Bemer, et al.. Integrated 3D forward stratigraphic and basin modeling of the Santos Basin, offshore Brazil: Implications of sedimentary depositional facies and CO2 migration for silicification of continental carbonate reservoirs. Marine and Petroleum Geology, 2025, 180, pp.107455. 10.1016/j.marpetgeo.2025.107455. hal-05138051

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# Integrated 3D forward stratigraphic and basin modeling of the Santos Basin, offshore Brazil: Implications of sedimentary depositional facies and CO₂ migration for silicification of continental carbonate reservoirs

Alina-Berenice Christ<sup>a, *, †</sup>, Marie-Christine Cacas-Stentz<sup>a</sup>, Nathalie Collard<sup>a</sup>, Fadi Henri Nader<sup>a</sup>, Elisabeth Bemer<sup>a</sup>, Rodrigo Brandão Bunevich<sup>b</sup>, João Paulo Borges Gomes<sup>b</sup>, Olinto Gomes de Souza Jr.<sup>b</sup>, Maria Anna Abreu de Almeida dos Reis<sup>b</sup>

## ABSTRACT

Silicification plays an important role in the quality of pre-salt reservoirs, resulting in significant heterogeneities that are difficult to predict. A better understanding of the distribution of silicified pre-salt reservoirs at the basin scale helps in mapping such heterogeneities and further optimizing responsible hydrocarbon exploration and production. Here we investigate a scenario involving the interaction of an internal silica source from Mg-rich clay (sedimentary depositional facies) and CO₂-rich fluid upwelling along deep-rooted faults. These are some of the main controlling factors of silicification, as confirmed with conceptual and numerical geochemical modeling. We present a new modeling workflow integrating forward stratigraphic modeling (FSM), basin/thermal modeling, quantitative chemical modeling, and map-based analysis to derive silicification probability maps of the Barra Velha Formation in the Santos Basin (Brazil). FSM simulates, with a process-based approach, the sediment distribution in the Barra Velha Formation including Mg-rich clay. Basin/thermal modeling simulates the rock-fluid systems evolution, CO₂ migration, accumulation, and concentration in the formation. Geochemical modeling provides Mg-clay transformation ratios depending on Mg-clay content of rock matrix and CO₂ concentration. Combining all elements together enables calculation of basin scale silicification probability maps across the study area. Highest probability of silicification is found above uplifted areas known to include Mg-rich clay, intense faulting, and trapping structures.

## 1. Introduction

Silicification is a diagenetic process that affects carbonate aquifers and reservoirs by concurrent dissolution of calcium carbonate and precipitation of silica, further complexifying the mineralogic heterogeneity of the host rocks and the associated flow properties (Bustillo, 2010). Such processes have been extensively reported in the pre-salt continental carbonate reservoirs of the Brazilian margin (Fig. 1), whereby not only calcium carbonate, but also the magnesium-clay (Mg-rich clay) rock constituents are demonstrated to be replaced by silica (Lima et al., 2020; Pereira et al., 2023). The silicified pre-salt carbonates are also known to host world-class petroleum systems (Beltrao et al., 2009; Jones and Chaves, 2011; Petersohn, 2019). Silicification is also believed to have affected other similar reservoirs, such as those of the Kwanza basin on the African continental margin (Saller et al., 2016). While previous studies aimed at better understanding the implications of diagenetic processes, such as silicification (Almeida Carvalho et al., 2022), on the associated evolution of the reservoir characteristics (Lima et al., 2020;

Pereira et al., 2023), often by means of various analytical and (static) modeling methods (Nader, 2017), no predictive process-based basin-scale mapping of silicification has been done. Previous approaches are essential to understand the controlling factors of such processes and provide the basics for constructing predictive numerical models to delineate the spatial and temporal distribution of silicification across sedimentary basins and reservoirs, especially when only few sub-surface data are available.

The Barra Velha Formation includes broadly silicified reservoir zones as well as major hydrocarbon reservoirs (Lima et al., 2020; Vieira, 2021; Vieira de Luca et al., 2017). Silicified intervals are also affected by important carbonate dissolution (e.g., vuggy, hypogenic karst), increasing considerably reservoir potentials. Previous studies demonstrated the relationship between silicification and dissolution processes in the Barra Velha Formation and its analogs, suggesting stratigraphically controlled fluid flow and fluid-rock interactions in deep burial diagenetic environments and/or under hydrothermal conditions (Basso et al., 2023; Souza et al., 2018). The sources of dissolved silica at

<footnote><sup>a</sup> IFP Energies nouvelles, 1 & 4 Av. de Bois Préau, 92852 Rueil Malmaison, France</footnote>

<footnote><sup>b</sup> Petrobras S.A., Av. Henrique Valadares 28, Rio de Janeiro, RJ, 20231-030, Brazil</footnote>

<footnote>* Corresponding author.
E-mail address: alina-berenice.christ@ifpen.fr (A.-B. Christ).</footnote>
the origin of the observed silicified rock facies are not well constrained and could be multiple. Some authors associated the silica supply with the alteration of volcanic products occurring in catchment areas, lakes, or the subsurface through hydrothermal activity (Pietzsch et al., 2020; Szatmari and Milani, 2016; Teboul et al., 2017, 2019; Tosca and Wright, 2015). Serpentinization of exhumed continental mantle has also been suggested as a source of solutes, including silica (Pinto et al., 2017; Teboul et al., 2019). Alternatively, the alteration of Mg-clays has been suggested to be the main source of Mg²⁺ and SiO₂(aq.) for the silicification and dolomitization of in pre-salt deposits (Deocampo, 2005; Tosca and Wright, 2015) since Mg-clays are known to be chemically labile and prone to rapid destabilization and dissolution during diagenesis (Tosca, 2014; Tosca and Wright, 2015). In the case of an internal silica source, understanding the initial sediment mineralogy distribution as well as the fluid properties and circulation via deeply rooted faults is essential to identify the most likely areas of silica precipitation.

Several phases of silicification are reported from petrographic analysis and proposed paragenesis (Sartorato et al., 2020; Vieira, 2021). In this contribution, we propose that the destabilization of Mg-rich clays was the major driver for this first and most significant phase, which is preceding hydrocarbon charge and the arrival of organic acids. The major CO₂ charge of the basin is assumed to be mantellic in origin (de Freitas et al., 2022). Based on this hypothesis, we deduce that the significant dissolution – particularly of carbonates – happened after the first phase of silicification. The CO₂ generated from organic matter maturation, hydrocarbons and organic acids may have also contributed to the dissolution of Mg-rich clays, silicification and carbonate dissolution. Their impact is considered minor compared to the first phase of silicification and consequently we did not take those processes into account. Hydrocarbon generation, migration and accumulation were still calculated in our model, but we did not consider hydrocarbon/fluid-rock interactions.

The main objective of this study is to apply a new integrative workflow to predict the spatial distribution of silicification and its timing at the basin scale (Fig. 2). Our approach combines different numerical modeling technologies to simulate, in three dimensions, the evolution of the basin over time, including stratigraphy, burial, pressure and temperature, salinity, CO₂ migration, hydrocarbon generation and charge, as well as diagenesis (i.e., carbonate silicification). We applied this workflow on the Santos basin (offshore Brazil) to demonstrate its feasibility and its ability to provide probable silicification maps across the basin. First, Forward Stratigraphic Modeling (FSM, which simulates

sedimentation processes) was applied together with sensitivity analysis modeling. Then Basin/Thermal Modeling was used to simulate the coupled transfers of fluids, heat, and chemicals. Results of the principal process-based simulators were combined with the output of a quantitative geochemical simulation of the main parameters controlling silicification (Collard et al., 2023) to process map-based analysis and produce silification probability maps (Fig. 2).

## 2. Geological setting

The Santos Basin is one of the largest sedimentary basins along the Brazilian continental margin with a surface area of 352 000 km² and a water depth of up to 3000m (Fig. 1). It is bounded to the south by the Florianópolis platform, which forms its natural boundary with the Pelotas basin. To the north, the Cabo Frio High separates it from the Campos basin. These passive-margin basins were formed in a Late Jurassic to Early Cretaceous extensional regime during the breakup of the Gondwana supercontinent, which led to the opening of the South Atlantic Ocean (Chang et al., 1990; Mohriak et al., 2008). They are therefore also related to the Kwanza and Namibe basins offshore southwest Africa (Saller et al., 2016).

The tectono-stratigraphic evolution of the Santos basin is represented by three mega-sequences that are associated to a rift phase (±138–123 My), a post-rift/sag phase (±123–113 My) and a drift phase (±113–0 My) (Moreira et al., 2007). These phases (and related mega-sequences) are also characterized by major unconformities: the Pré-Alagoas unconformity, which separates the rift and sag phases, the Intra-Alagoas unconformity, and the salt base which marks the transition from the sag to the drift phase (Fig. 3).

The early rifting phase was characterized by extension and asthenospheric uplift (Mckenzie, 1978; White and Mckenzie, 1988), giving rise to tholeiitic flood basalts (Mohriak et al., 2008; Torsvik et al., 2009; Turner et al., 1994) dated at 134–122 My (Mizusaki et al., 1988). The rift phase is dominated by fluvio-lacustrine deposits, talc-stevensite ooids with interlayered lacustrine coquinas and organic-rich shales of the Piçarras and Itapema formations (Moreira et al., 2007). These are overlain by the regional Pré-Alagoas unconformity, formed by previous uplift and erosion of the rift section (Dias, 2005; Karner and Driscoll, 1999; Winter et al., 2007). Thermal contraction of the crust resulted in subsidence and the collapse of crustal domes, creating sag-type basins (Beasley et al., 2010). This sag phase is characterized by the deposition of stromatolites, microbalites and Mg-rich shales in a lacustrine

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** a) Location of the Santos Basin, South Atlantic, offshore Brazil. b) Area of interest within the region of the outer high of the Santos Basin. c) Close-up of the area of interest with the well locations. Modified from Melo-Garcia et al. (2012). Satellite image from Google Earth™ (2025).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 2. The integrative modeling workflow designed and applied for this study. Rectangular boxes representing data and oval shapes representing modeling processes.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Lower Cretaceous stratigraphic chart of the Santos Basin, Brazil. Chart modified from Moreira et al. (2007).</figcaption></figure>

environment, forming the Barra Velha Formation (Mann and Rigg, 2012; Moreira et al., 2007; Terra et al., 2010). During the drift phase the Santos basin became a passive margin, and the development of oceanic crust established a marine environment (Aslanian et al., 2009; Chang et al., 1990, 1992; Moreira et al., 2007). The Ariri Formation, overlying the Barra Velha Formation, is characterized by transitional evaporites deposited in a gulf type environment with highly saline waters and a negative hydrologic balance (Chang et al., 1992). It forms a regional seal up to 2 km thick (Beasley et al., 2010; Mohriak et al., 2008; Moreira et al., 2007; Quirk et al., 2013).

Previous studies demonstrated that the Outer High area (central part of Santos basin; Fig. 1) was already a paleo-high at the time of the deposition of the Barra Velha Formation (Liechoscki de Paula Faria et al., 2017). The lake deposits in the Barra Velha Formation as well as the equivalent formations offshore Brazil are in a rift basin, whereby horsts and grabens prevailed with associated small islands, surrounded

by large or small lakes, whose connectivity may have varied trough time (Pietzsch et al., 2018). The shoreline through which detrital input took place was far away to the west (Dias, 2005), whereas the *in-situ* carbonate deposits appear to be laterally continuous for many kilometers (Carminatti et al., 2008). These carbonates are often characterized by three main textural components: shrubstones, spherulitestones and fine micritic material (Wright and Barnett, 2015).

## 2.1. Facies types of Barra Velha Formation (BVF)

Different facies types have been identified in the pre-salt deposits of the Barra Velha Formation in the Santos Basin and their equivalents can be found in the adjacent Campos and Pelotas basins (Lima and De Ros, 2019), as well as the Namibian and Kwanza basins (Saller et al., 2016). Gomes et al. (2020) distinguish three *in situ* formed end member facies types based on the detailed analysis of eight wells and 389 m of core
data: mud (dolomite, calcite, silica and Mg-rich clay), calcite spherulites, and fascicular calcite shrubs. When the spherulites and shrubs were subject to erosion and transport, their debris can form a fourth facies type, referred to as the reworked facies.

The mud end member is composed of different clay minerals – e.g., kerolite, stevensite, saponite, illite and mixed-layer minerals kerolite/ smectite and illite/smectite (Madrucci et al., 2019; Souza et al., 2018). Creamy to light brown in color, it shows massive beds or plane-parallel to smooth wavy laminations with thicknesses varying from a few mm to cm. Clay minerals (and carbonate mud) are locally replaced by dolomite and microcrystalline silica. Lighter laminas appear to be composed of microcrystalline calcite and darker laminas are composed of microcrystalline and rhombohedral dolomite and organic matter. Siliciclastic grains, ostracod and phosphatic particles are also found in the mud (Gomes et al., 2020).

Spherulites are spherical calcite aggregates with a radial structure that are commonly dolomitized or recrystallized. Gomes et al. (2020) measured spherulites on thin sections and calculated a diameter range of 0.1–4 mm with a mean of 1 mm and a mode of 0.6 mm. Due to the small variance in size, the same formation mechanisms are assumed for all examined spherulites. A nucleus in the center is rarely identified which may be cryptocrystalline micrite, clay minerals or ostracods. The original fibrous appearance is mostly preserved but it can be lost due to chalcedony and microquartz replacement. The spherulites seem to favor an upward growth but in special cases the spherulites can grow parallel to the bedding. Intermediate forms between spherulites and shrubs also exist.

The term “shrub” refers to very coarse-bladed calcite crystal crusts in a fan-like structure that show perpendicular grow along an axis pointing away from the substrate from which they originate (Folk et al., 1985). Gomes et al. (2020) found that the long axis of the shrubs in the Barra Velha Formation is between 0.15 and 6.5 mm. They grow in a parallel fashion with distances between individual shrub axes ranging from 0.2 to 4 mm, and occasionally contain fine detrital material. The shrubs form horizontal layers and can locally form dome-like structures (Rezende and Pope, 2015; Terra et al., 2010; Wright and Barnett, 2015). Occasionally shrubs can exhibit multiple growth phases resulting in composite shrubs as large as 20 mm.

## 2.2. Conceptual sedimentological model

The relief of the Outer High (central part of Santos basin, Fig. 1) was very similar to what can be seen today on seismic reflection profiles (Liechoscki de Paula Faria et al., 2017), allowing the definition of two distinct types of depositional environments (i.e., paleo-highs and adjacent paleo-lows) that are also supported by lithological information from well data (Fig. A1a). Due to such relief configuration, the conditions for sedimentary deposition differed in terms of bathymetry, but also in terms of other sediment control factors such as wave energy.

The depositional environment of the paleo-highs is strongly influenced by lake level fluctuations and even shows signs of exposure during lake level low stands (Gomes et al., 2020). The paleo-highs are dominated by the presence of shrubs that grew in an upward, vertical direction from a substratum (Basso et al., 2020). They are found in their in-situ position, indicating that they were not transported. Shrubs form horizontal layers and locally low domal structures (Rezende and Pope, 2015; Terra et al., 2010; Wright and Barnett, 2015). Biological factors may play a role in the initiation of shrub growth, invoking growth in a shallow water depth, in the range of the photic zone (Ceraldi and Green, 2017; Gomes et al., 2020). Shrubs also require wave energy for their development. In the Kwanza basin, very similar facies to shrubs have been observed and have been interpreted to have formed positive structures on paleo-highs, like modern reefs, requiring both light and wave energy for their development (Saller et al., 2016).

Spherulites are also present on the paleo-highs, but they indicate a higher lake level during the time of deposition, protected from wave

action and currents (Arienti et al., 2018; Artagão, 2018; Ceraldi and Green, 2017; Gomes et al., 2020; Herlinger et al., 2017; Liechoscki de Paula Faria et al., 2017; Lima and De Ros, 2019; Muniz and Bosence, 2015; Sartorato, 2018; Tanaka et al., 2018; Wright and Barnett, 2015, 2017). The presence of the mud matrix suggests that transport did not occur (Herlinger et al., 2017). Spherulites most likely formed at the sediment-water interface in an alkaline environment rich in dissolved organic acids (Mercedes-Martín et al., 2016). One possibility could be that the spherulites of the Barra Velha Formation formed around bacterial clumps present in the water column, and then slowly settled down until they reached the sediment-water interface (Gomes et al., 2020), where they continued their spherical incremental growth pattern like the mechanism proposed for other alkaline lake systems (Mercedes-Martín et al., 2017).

Reworked sediments are found at the paleo-highs, interfingering with intervals of shrubs and spherulites but also farther from the highs in the more basin dominated areas. The reworked sediments are the debris of shrubs and spherulites created during episodes of lower lake levels causing erosion and was transported away from the paleo-highs by gravitational currents.

Away from the paleo-highs, laminated micrite and Mg-rich clays dominate (in the paleo-lows). Due to their small grain size, both sediments require a low energy environment to settle down, below the wave base. They are interpreted to have formed in the deeper and undisturbed parts of the lacustrine environment. Near to the paleo-highs, some areas are locally characterized by some amounts of preserved Mg-rich clays. Protection from wave energy in localized smaller basins (paleo-lows) could have allowed Mg-rich clay to accumulate there, suggesting that they can still occur at shallow water depths but require a low energy environment.

## 2.3. Tectonic and climatic controlling factors

Tectonic and climatic cycles as well as sea level fluctuations are inter-related and have a tremendous impact on sediment erosion, transport, deposition, and accommodation space. They are the major controlling factors and drivers for the creation of different depositional environments in sedimentary basins (Catuneanu et al., 2009; Mack, 1978; Nichols, 2009). Tectonics and eustasy affect the accommodation space of the basin, which is the space available for sediments to accumulate over time. Climate on the other hand has a more indirect effect on the accommodation by controlling the sediment available for deposition. For example, it limits the amount of sediment that can be transported into the basin or the rate of growth of biogenic sediments. Other factors such as wave energy or currents are also determined by climate (Catuneanu et al., 2009). Eustatic sea level fluctuations are controlled by both tectonics and climate on different time scales. On the larger stratigraphic orders (third stratigraphic order and above), the driving factor is tectonic mechanisms such as continental formation and break up or regional plate kinematics. Time scales for sea level changes range from 200 to 400 My (first stratigraphic order), to 1–10 My (third stratigraphic order). For smaller stratigraphic orders cycles (fourth and fifth stratigraphic orders) global climatic changes (e.g., caused by Milankovitch cycles with duration of 0.01–1 My) are the main driving force (Catuneanu et al., 2009; Miall, 2000; Vail et al., 1977).

The cyclical deposition of the Barra Velha Formation facies (described above) has been observed and associated to the interplay between tectonic and climatic cycles affecting the Santos basin (Arienti et al., 2018; Artagão, 2018; Dias, 2005; Farias et al., 2019; Liechoscki de Paula Faria et al., 2017; Lima and De Ros, 2019; Muniz and Bosence, 2015; Sartorato, 2018; Wright and Barnett, 2015). The basin-wide unconformities that bound the Barra Velha Formation at the top (Salt base) and bottom (Pré-Alagoas), and internally (Intra-Alagoas) represent such interplay and were used to further subdivide the stratigraphic units. For example, the Intra-Alagoas Unconformity divides the Barra Velha Formation in a Lower Barra Velha Formation and an Upper Barra Velha
Formation. The sequences have been dated using ostracods and span a time frame of about 10 My (±123–113 My) as well as deposited sequence thicknesses of several 100’s m (Gomes et al., 2020; Moreira et al., 2007; Pietzsch et al., 2018). These sequences correspond to third-order stratigraphic cycles, suggesting a tectonic origin. A general change in lithology can be observed from the Lower Barra Velha Formation to the Upper Barra Velha Formation. The Lower Barra Velha Formation is largely dominated by Mg-rich clays, micrites and reworked facies, whereas the Upper Barra Velha Formation is mostly composed of *in-situ* formed sediments (Gomes et al., 2020). It is noteworthy that the Mg-rich clays increase just above the Intra-Alagoas Unconformity and decrease progressively towards the top.

Basic repeating high frequency cycles have been identified in the Barra Velha Formation representing fourth to fifth order cycles, with the climate as the driving factor behind the formation of these cycles. Fourth order cycles are in the range of 100–400 ky and have a thickness of 10–50m. Eccentricity is considered to be the underlying cause. Fifth order cycles are in the range of 20–50 ky and have a thickness in the meter scale (ranging between 1 and 10m). They are well identified on the lithology logs as shown by Gomes et al. (2020). Obliquity and precession are thought to be the underlying mechanism causing these repetitive cycles. In an idealized sequence, a fourth-order cycle in the Barra Velha Formation starts out at the base with laminated carbonates deposited during the maximum lake level (Fig. A1b). As the lake level decreases, alkalinity and microbial action increase, creating favorable conditions for the formation of mm-large spherulites (Gomes et al., 2020; Mercedes-Martín et al., 2017). These are followed by mm-cm

sized calcitic crystal shrubs and crusts (Wright and Barnett, 2015). When the lake level is at its lowest, previously deposited sediments may be exposed and reworked. Towards the top of an idealized cycle, grainstones composed mainly of fragments of previously deposited facies and absent of matrix complete the cycle, suggesting deposition in a high-energy environment (Liechoscki de Paula Faria et al., 2017). The reworked sediments due to their origin can occur at any time during a sequence and are therefore not a reliable component for calibration.

### 3. Data and methods

The selected study area covers a 48 km × 98 km rectangular zone on the Outer High in the Santos Basin, offshore Brazil (Fig. 1). The used dataset was compiled and provided by Petrobras. Hard input data for model construction were taken from three interpreted seismic horizons and seven wells with markers and lithological logs (Fig. 4). The seismic horizons available were the top and base of the Barra Velha Formation and the Intra-Alagoas Unconformity. The top and base of the Barra Velha Formation and the Intra-Alagoas Unconformity were also present as well markers. Formation boundaries were dated using the Moreira et al. (2007) stratigraphic chart which places the top of the Barra Velha Formation at 113 My, the Intra-Alagoas Unconformity at 117 My and the base of the Barra Velha formation at 122 My. The lithology classes were simplified by regrouping the 15 classes identified by Gomes et al. (2020) into five respective classes (Table 2).

The applied workflow integrates, as mentioned above, process based modeling software packages as well as geochemical analysis solutions

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Cross section for the seven available wells showing the lithology logs and the two unconformities bordering the Barra Velha Formation. Wells located at the flank of the paleo high show a predominance for Mg-rich clays, especially around the Intra-Alagoas Unconformity. The paleo high is dominated by shrubstone and spherulitestone. Reworked sediment occurs on the paleo high and flanks. BVF = Barra Velha Formation, SBU = Salt Base Unconformity, IAU = Intra-Alagoas Unconformity, PAU = Pré-Alagoas Unconformity.</figcaption></figure>
(Fig. 2). The forward stratigraphic model (FSM), based on the concep-
tual sedimentological model and input data, provided realistic maps of
the sedimentological facies distribution. These were used as an input
data for the basin/thermal model which provides a detailed prediction
of the other parameters that are believed to control silicification over
time, namely pressure, temperature, and CO2 concentration in water
(Collard et al., 2023). The simulated facies maps, the basin modeling
output maps – namely that of CO2 distribution –, together with the
transformation ratios of clays to silica (defined by means of geochemical
modeling, see Collard et al., 2023), were combined to produce through
map-based analysis the silicification probability maps (Fig. 2). These
steps are described in detail here below.

3.1. Forward stratigraphic modeling (FSM)

Forward stratigraphic modeling (FSM) is a 3D process-based
approach to modeling geologic processes through time and space.
Based on physical laws, FSM enables to model precise sedimentary facies
distribution which is an important input parameter to better constrain
basin/thermal modeling. DionisosFlow™, a software for stratigraphic
forward modeling developed by IFP Energies nouvelles and Beicip-
Franlab was used in this study (Granjeon, 1996; Granjeon and Joseph,
1999). This software can simulate the production, transport, and accu-
mulation of different sediment types on basin scale (10–100 km) and
over long-time intervals (100 Ka – 100 Ma). It was also used to model
sedimentary systems below the fifth order (Busson et al., 2019; Csato
et al., 2014; Rabineau et al., 2005), accounting for accommodation
space variation by a fluctuation of an eustatic sea level curve and/or by
subsidence. It allows to model siliciclastic, carbonate and mixed sedi-
ments as well as the distribution of marine and terrestrial organic matter
in different environments ranging from lacustrine and shallow marine to
deep marine (Andres et al., 2008; Bassant and Harris, 2008; Bruneau
et al., 2018; Burgess et al., 2006; Claps et al., 2009; Granjeon and Jo-
seph, 1999; Hawie et al., 2015; Kolodka et al., 2016; Liechoscki de Paula
Faria et al., 2017; Rabineau et al., 2005; Seard, 2010; Seard et al., 2013;
Williams et al., 2011). Sediment transport is simulated by an empirical
diffusion equation that can be driven by gravity and/or water (Granjeon,
1996; Granjeon and Joseph, 1999). Deposited sediments can be eroded
again, e.g., by wave action.

Once the process parameters are identified, the stratigraphic forward
simulation calculates the sedimentary properties in each cell of the

model for each time step at a given interval. The result is a high-
resolution geologic model showing sedimentary distribution and facies
architecture. The model can be used to quantify sedimentary processes
through time for a specific location, providing a deeper understanding of
geological evolution as was done in this study for the Barra Velha For-
mation of the Santos Basin, whereby the constructed model spatial
resolution is 1 km × 1 km with a time step of 0.25 million years (My).

Calibration is an essential part of numerical modeling to achieve a
realistic result with comparison to available data. In this study, seismic
reflection data (interpreted horizons and thickness maps) and well data
were used to compare and adjust the simulation results. An important
final step was the uncertainty and sensitivity analysis to identify most
influential simulation parameters.

3.1.1. Input data for FSM

The main input data for the forward stratigraphic model were
"sediment types" (with associated carbonate accumulation curves, see
Fig. 5), lake level variation curve, wave energy, gravitational sediment
transportation, paleo bathymetry and subsidence maps (Table 1). The
five "sediment types" were defined for the forward stratigraphic model
to represent the known dominant BVF facies. In a post processing step
after the initial simulation, facies types were defined based on pro-
portions of "sediment types" in the cells (Table 3). These facies types
were used for calibration with the original well data.

3.1.1.1. *From lithofacies to "Sediment types"*. Stratigraphic forward models are constructed at the larger, basin scale resolution in time and space, as mentioned above. Detailed, smaller scale facies definition (e.g., high resolution well-log description, microscopic analysis) may be less relevant than the dominant end-member groups of facies (e.g., mud-dominated, grain-dominated, *in-situ* sediments). This is important for stratigraphic modeling as it allows to focus the simulations on the dominant facies that ultimately create differential stratigraphic patterns.

The detailed BVF facies described from seven well-logs (Gomes et al., 2020) were regrouped into five dominant facies. The end-member facies of *in-situ* growing shrub and spherulite each containing more than 90 % of shrub or spherulite were grouped into shrubstone and spherulite-stone, respectively (Table 2). All facies containing less than 90 % shrub or spherulite and less than 90 % fine grain fraction were grouped into the facies Mg-rich mudstone (Table 2). Mud-dominated facies with a significant carbonate content (calcite and dolomite) were referred to as

<figure><img src="image_8.png" /><figcaption>Fig. 5. Normalized carbonate production curves for the 4 "sediment types" shrub, spherulite, Mg-rich clay, and micrite.</figcaption></figure>
<caption>**Table 1**</caption>

List of the principal input parameters and their values used for forward stratigraphic modeling (FSM). Carbonate accumulation and sediment transformation rates based on (Kinsey, 1985; Bosscher and Schlager, 1993; Tosca and Wright, 2014; Saller et al., 2016; Ceraldi and Green, 2017; Gomes et al., 2020), wave energy parameters based on (Gilbert, 1999; Saller et al., 2016), sediment transport parameters based on (Terra et al., 2010; Rezende and Pope, 2015; Wright and Barnett, 2015; Bruneau et al., 2017; Herlinger et al., 2017; Busson et al., 2019; Granjeon, 2019; Basso et al., 2020; Gomes et al., 2020).

<table><caption>**Table 2**</caption><thead><tr><th>Parameters</th><th>Values</th><th>Sediment type</th></tr></thead><tbody><tr><td>Model size</td><td>48 km × 98 km</td><td></td></tr><tr><td>Cell dimension</td><td>1 km × 1 km</td><td></td></tr><tr><td>Time step</td><td>0.25 My</td><td></td></tr><tr><td>Subsidence</td><td>30–80 m/My on<br>paleo high<br>50–250 m/my in<br>basin area</td><td></td></tr><tr><td>Minimum wave energy<br>threshold</td><td>20 kW/m</td><td></td></tr><tr><td>Wave base</td><td>10 m</td><td></td></tr><tr><td rowspan="4">Carbonate accumulation<br>rates</td><td>50–120 m/My</td><td>Shrub</td></tr><tr><td>50–100 m/My</td><td>Spherulite</td></tr><tr><td>60–80 m/My</td><td>Mg-rich clay</td></tr><tr><td>80 m/My</td><td>Micrite</td></tr><tr><td>Sediment transformation<br>rate</td><td>20 m/My</td><td>Shrub and spherulite<br>transformed into reworked<br>sediment</td></tr><tr><td rowspan="5">Gravitational diffusion<br>coefficient Kslope</td><td>0.0001 km²/ky</td><td>Shrub</td></tr><tr><td>0.0001 km²/ky</td><td>Spherulite</td></tr><tr><td>0.001 km²/ky</td><td>Mg-rich clay</td></tr><tr><td>0.001 km²/ky</td><td>Micrite</td></tr><tr><td>0.001 km²/ky</td><td>Reworked Sediment</td></tr></tbody></table>

Nomenclatures of the detailed well-log described facies of BVF, based on Gomes et al. (2020) and the corresponding five reregulated facies (this study).

<table><caption>**Table 3**</caption><thead><tr><th>BVF detailed Facies classification</th><th>Regrouped dominant facies for FSM</th></tr></thead><tbody><tr><td>Spherulitestone</td><td>Spherulitestone</td></tr><tr><td>Shrubby spherulitestone</td><td></td></tr><tr><td>Spherulitic shrubstone</td><td>Shrubstone</td></tr><tr><td>Shrubstone</td><td></td></tr><tr><td>Spherulitic shrubstone with mud</td><td>Mg-rich mudstone</td></tr><tr><td>Shrubby spherulitestone with mud</td><td></td></tr><tr><td>Muddy spherulitestone</td><td></td></tr><tr><td>Spherulitic mudstone</td><td></td></tr><tr><td>Mg-clay mudstone</td><td></td></tr><tr><td>Calci-mudstone</td><td>Micrite</td></tr><tr><td>Dolo-mudstone</td><td></td></tr><tr><td>Mixed mudstone</td><td></td></tr><tr><td>Grainstone</td><td>Reworked Facies</td></tr><tr><td>Packstone</td><td></td></tr><tr><td>Wackestone</td><td></td></tr></tbody></table>

Parameter definition for the lithofacies classes in the model.

<table><thead><tr><th rowspan="2">Lithofacies<br>classes</th><th colspan="5">Sediment types [%]</th></tr><tr><th>Shrub</th><th>Spherulite</th><th>Mg-rich<br>clay</th><th>Micrite</th><th>Reworked<br>sediment</th></tr></thead><tbody><tr><td>Shrubstone</td><td rowspan="2">>30</td><td></td><td></td><td></td><td></td></tr><tr><td>Spherulitestone</td><td rowspan="2">>30</td><td></td><td></td><td></td></tr><tr><td>Mg-rich<br>mudstone</td><td></td><td rowspan="2">>30</td><td></td><td></td></tr><tr><td>Micrite</td><td></td><td></td><td rowspan="2">>30</td><td></td></tr><tr><td>Reworked facies</td><td></td><td></td><td></td><td>>30</td></tr></tbody></table>

micrite (Table 2). Grainstone, packstone, wackestone facies that are not formed *in-situ* were combined into reworked facies (Table 2).

Shrubstone and spherulitestone characterize the paleo-high depositional environment, whereas reworked facies, Mg-rich mudstone and

micrite characterize the flanks and basins. The Lower BVF, where basin and flank depositional environments prevail, is dominated by Mg-rich mudstone, micrite and reworked facies. The Upper BVF is dominated by shrubstone, spherulitestone and Mg-rich mudstone. Mg-clay is especially abundant above the Intra-Alagoas Unconformity. For FSM, these dominant facies correspond to a mixture (with specific proportions) of “sediment types” that will be detailed below (Section 3.1.1.7).

### 3.1.1.2. Carbonate production/accumulation model and sediment transformation rates.
Carbonate *in-situ* accumulation rates were calculated based on the age limits and thicknesses of the Upper and Lower BVF. They are in the range of 50 m/Ma to 80 m/Ma for the Lower BVF and increase to 80 m/Ma to 120 m/Ma in the Upper BVF (see Table 1), which is in the same order of magnitude observed in other carbonate systems in the geological record (Bosscher and Schlager, 1993). Carbonate accumulation rates are not equivalent to carbonate production rates, which can be much higher and in the order of magnitude of 7–9 mm/yr of recent coral reefs (Kinsey, 1985). This is because carbonate deposits can be again eroded and transformed after deposition. In this study, we focused on adjusting the carbonate accumulation rates of individual “sediment types” to match the observed thicknesses, distribution patterns and well data of the Lower and Upper BVF. For the Lower BVF the following carbonate accumulation rates were used: 50 m/My for shrub, 50 m/My for spherulite, 60 m/My for Mg-rich clay, 80 m/My for micrite. For the Upper BVF the following carbonate accumulation rates were used: 120 m/My for shrub, 100 m/My for spherulite, 80 m/My for Mg-rich clay, 80 m/My for micrite. Normalized production curves as a function of water depth are shown in Fig. 5.

Shrub growth was influenced by biological factors indicating formation in shallow water depths in the photic zone requiring additionally wave energy for their development like modern reefs (Saller et al., 2016; Ceraldi and Green, 2017; Gomes et al., 2020). Therefore, the production zone was limited to the first 20m of the water column. An additional constraint in form of wave energy was added to the production of shrub and their production only happens in areas where wave energy exceeds 20 kW/m. Spherulites are found on the palo-high in association with shrub and a Mg-rich mud matrix, which is why the production windows was set between the two other sediment types and below the fair-weather wave base. The Mg-rich clay production window is situated in the sublittoral zone of the lake below the spherulite window. They require high pH > 9.5 for their formation and elevated methane levels and associated acidity due to organic sediment accumulation would prevent formation at deeper bathymetries (Tosca and Wright, 2014). Micrite is found mainly in the deeper bathymetries of the lake in the form of finely laminated clays, indicating deposition in a calm and undisturbed environment not influenced by waves or storms and representing a pelagic background sedimentation.

Reworked sediments originate as debris from the erosion and break down of shrub and spherulite during episodes of low lake levels. Shrub and spherulite get transformed at a rate of 20 m/My into reworked sediment to reflect this process.

### 3.1.1.3. Sea(lake) level curve.
In this study the lake level curve was constructed from the lithology logs of representative wells for the paleo-high and paleo-low areas in the model. For each lithology type present in the logs, a relative lake level was assigned according to the conceptual depositional model. Lithologies associated with a deeper bathymetry during formation were assigned a number close to 1, and lithologies associated with shallower bathymetries were assigned values closer to 0 (see Table A1). Consequently, a relative lake level variation curve was obtained which depicts the tendencies of relative lake level deepening and shallowing. The curve was then readjusted to account for the time required for sediment deposition. The depositional time for the different lithologies varies depending on grain size and mode of formation. A time
step of 0.1m was chosen and relative depositional rates were derived for each lithology (Table A2). Finally, the curve was normalized for a fluctuating lake level between 0 and 50m, where 50m is considered the maximum lake level to cover the production window for Mg-rich clays (Fig. A2a).

### 3.1.1.4. Wave energy.
Wave energy is a critical factor in the development of shrubstone and reworked facies (Saller et al., 2016). For comparable wind speeds, wave bases are shallower in lakes than in the ocean domain (Gilbert, 1999). A 1D wave model was chosen in which wave energy decreases from top to bottom and is a function of the bathymetry. The wave base was set at 10m and the wave energy is highest between 3 and 13m of bathymetry, reaching a peak of 40 kW/m at 5m bathymetry (Fig. A2b).

### 3.1.1.5. Paleo-topography.
Paleo-topographic surfaces were used as boundary conditions for the forward stratigraphic simulation. They were reconstructed from associated seismic horizons, as previous research showed that the Pre-Salt field high was also a paleo-high throughout the Barra Velha Formation deposition (Gomes et al., 2020; Liechoscki de Paula Faria et al., 2017). Lithology logs were analyzed for the Pre-Salt field high and flanks, and the paleo-bathymetries were deduced (Table A2). Using the derived paleo-bathymetry information, a normalization factor was applied to the seismic horizons to create a paleo-topographic surface for each horizon (Fig. A3).

### 3.1.1.6. Sediment transport.
Sediment transport processes and parameters were defined from an analysis of sedimentary paleoenvironments. The paleo-high is strongly influenced by lake level fluctuations and signs of exposure during lake level low stands can be found (Gomes et al., 2020). Shrub on the paleo-high grew from a substratum in an upward and vertical direction and forming horizontal layers with domal structures. Presence of in-situ position indicates no transport (Basso et al., 2020; Rezende and Pope, 2015; Terra et al., 2010; Wright and Barnett, 2015). The association of spherulite with a Mg-rich mud matrix also suggests that there was no transport after formation (Herlinger et al., 2017). Therefore, sediment transport is modeled following a linear diffusion gravitational transport law taking into consideration the local gradient of surface elevation $\Delta z$. The sediment flux in the model ($Q_s$) is calculated following the equation:

$$Q_s = -K_{\text{slope}} * \Delta z$$

The transport coefficient $K_{\text{slope}}$ defines the efficiency of sediment transport and varies with the sediment type. The values for the coefficient $K_{\text{slope}}$ were assumed very low to prevent the transport of shrub. In an iterative approach various order of magnitudes for the transport coefficient values were tested according to a range of values from the literature (Bruneau et al., 2017; Busson et al., 2019; Granjeon, 2019). The values for the coefficient $K_{\text{slope}}$ were selected by an iterative approach so that the modeled sediment distribution fits the observed lithofacies data in the wells (Table 1).

### 3.1.1.7. Facies definition.
In a forward stratigraphic model, each grid cell contains a mix of the simulated “sediment types” in percentage, which is a function of the parameters described above. Sediment percentage is a continuous parameter, and in this study, we are interested in lithofacies maps. Those maps show the distribution of lithofacies throughout the basin and at different ages - notably Mg-rich mudstone - which contribute to the characterization of silicification probability. We designed a facies definition matrix which assigns to each cell one discrete facies value, based on the percentages of the “sediment types” present in each cell (see Table 3). A value of minimum 30 % of the respective “sediment type” was chosen for the attribution of the lithofacies class.

### 3.1.1.8. Subsidence.
Subsidence maps were calculated in a twofold step based on seismic horizon data, which was considered as hard data and has therefore not been modified. First, the accumulated sediment thickness was computed by subtracting bottom seismic horizon from top seismic horizon for the Upper BVF and the Lower BVF, respectively. Then, the maps were converted to time (unit m/My) by dividing the thickness maps by their relative deposition time interval (Fig. A4). Subsidence is assumed linear for each modeled time interval. This simplification is minimizing the number of unknown factors and facilitates the correlation with the hard data. Calculated values for subsidence in the Lower BVF are 30 m/My on the paleo high and 100–150 m/My in the basin area. Calculated values for subsidence in the Upper BVF are 50–80 m/My on the paleo high and 100–250 m/My in the basin area. Sediment compaction was not considered, and no back-stripping was applied for the constructions of the incorporated subsidence maps in the stratigraphic model.

### 3.1.2. Uncertainty and sensitivity analysis of FSM

Sensitivity analysis aims to better understand the uncertainty in the output of model simulations and assesses the impacts of various uncertain input parameters on the modelling results. It allows for checking the robustness of the model when uncertainty is introduced, and for further understanding the relationship between the input parameters and the output of the model (e.g., unexpected relationships may indicate errors). Through such analysis the parameters with the highest impacts on the modelling results can be distinguished and investigated furthermore. In this study the software CougarFlow™ (Gervais et al., 2018) was used to get a global understanding on the sensitivity of the processes influencing the Mg-rich clay distribution.

Based on the achieved FSM simulation, meta models were built for each parameter potentially influencing the simulation result using a Latin Hypercube Design. These meta models are in a second step used for uncertainty quantification and sensitivity analysis of the critical input parameters for the simulation. Latin Hypercube Design was chosen instead of performing a Monte Carlo sampling, due to its advantages in terms of computation time and cost. The whole simulation area was selected as a zone of interest for the uncertainty and sensitivity analysis. The average percentage of “sediment type” Mg-rich clay present in each cell of the simulation has been selected as the result of interest. An uncertainty range $\pm 20\%$ of the original simulation parameter was chosen. The following (7) parameters were tested for their impact in average Mg-rich clay percentage: eustasy, wave energy, subsidence, sediment transport, carbonate production versus time, carbonate production versus depth, and sediment transformation rate.

The sensitivity analysis (to determine the most influential parameters) is done by creating a multidimensional response surface in the parameter space, which honors the data exactly. In a next step, a mathematical response surface is generated, which is a mathematically reliable representation of all the parameter combinations. The fit between the multidimensional and the mathematical response surface shows the predictivity of the model and is expressed with a number between 0 and 1. The better the fit and values close to 1, the higher the predictivity and therefore robustness of the model.

### 3.2. Basin/thermal modeling

Process-based forward numerical modeling of the major processes controlling heat and fluid transfer throughout the entire sedimentary basin and throughout its geological history was achieved using a prototype version of TemisFlow™ developed by IFP Energies nouvelles and Beicip-Franlab (Schneider et al., 2000). This technology has become a standard tool for oil and gas exploration and is now being used to address some of the challenges of the energy transition involving the deep subsurface. While stratigraphic modeling is used in this study to provide insight into the distribution of stratigraphic facies, basin modeling is used to predict the temporal evolution of other factors
thought to control silicification like pressure and temperature. Salinity and CO₂ concentration in water could also be simulated with a new prototype module for simulating the transfer of dissolved components and calculating the thermodynamic equilibrium between the aqueous and vapor phase. Special attention was given to the fault system which is precisely mapped in the study area, and which may be the pathways for the CO₂-rich fluids.

First, a basin-scale geomodel covering the area of interest is constructed (Fig. 6), which is calibrated against pressure and temperature data referred to as “original model”. The original model was updated to include results from FSM and is referred to as “new model”. The new model measures 60 km × 100 km and is discretized with 250000 cells divided into 21 layers, each one corresponding to a depositional event. The pre-salt formations are discretized by 8 layers corresponding to the following time intervals: [125Myrs; 123 Myrs], [123Myrs; 121 Myrs], [121Myrs; 119 Myrs], [119Myrs; 117 Myrs], [117Myrs; 116.3 Myrs], [116.3Myrs; 115.2 Myrs], [115.2Myrs; 114.1 Myrs] and [114.1Myrs; 113 Myrs]. The bounding box of the stratigraphic model described above delineates a subdomain of the new basin model and is hereafter referred to as the “FSM sub-model”.

### 3.2.1. Lithofacies distribution

A lithofacies map was assigned to each layer of the model (Fig. 7). Lithofacies from the original model were not modified outside of the FSM subdomain. Five new lithofacies were used to model the spatial distribution of petrophysical properties within the FSM subdomain, corresponding to the five simplified facies used in the DionisosFlow™ model, namely spherulitestone, shrubstone, Mg-rich mudstone, micrite and reworked facies.

Faults were added to the model according to available observations in a restricted area around the Pre-Salt field reservoir. Six other new facies were created to model the cells which are intersected by these faults: Spherulitestone_fRACTured, Shrubstone_fRACTured, Mg-rich-mudstone_fRACTured, Mudstone_fRACTured, Micrite_fRACTured and Reworked_fRACTured. It is very likely that other faults exist in other areas of the basin, but no data were available.

The dominant facies maps of the 8 pre-salt layers were then exported from DionisosFlow™ model and re-sampled onto the basin modeling

<figure><img src="image_9.png" /><figcaption>Fig. 7. Facies map of the top pre-salt layer, deposited during the [114.1Myrs; 113 Myrs] time interval. The black dotted line represents the Pre-Salt field anticline axis.</figcaption></figure>

grid. The resulting lithofacies maps were completed by assigning the fractured lithofacies to the cells intersected by fault lineaments or above the Outer-High regional fault. Finally, a highly permeable lithofacies (referred to as previous boundary in Fig. 7) was applied in one-cell-thick surfaces along the domain lateral boundaries and in all layers of the model to allow the expulsion of compaction fluids without the development of overpressure as observed in the area. This method of preventing overpressure was preferred to a simple hydrostatic boundary condition because it also prevents inflow of external fluid that could disturb the simulated salinity and CO₂ concentration fields.

### 3.2.2. Petrophysical properties

Assigning petrophysical properties to the different lithofacies of the model was a challenging problem because petrophysical measurements are made on samples that have been altered by diagenesis making them poorly representative of the rock properties before silicification. We assigned the petrophysical properties of the two porous sandstones of the original model to all lithofacies of the FSM subdomain, to preserve the good calibration of the original model. The Schneider model (Schneider et al., 1996) is used for compaction, and the Kozeny-Carman model is used for the permeability versus porosity relationship. A correction factor of 0.001 was then applied to the permeability of the argillaceous lithofacies.

Since faults were assumed to be the vectors for the upward migration of CO₂, they were modeled as privileged flow paths, even though no data is available to confirm this hypothesis except that the CO₂ content of the water observed in the Pre-Salt field reservoir is indeed high (Jia et al., 2020). We increased the vertical permeability of the cells intersected by fault lineaments or along the Outer-High regional fault by a factor of 10, considering that the damage zone fills 10 % of the cell volume, with a permeability 100 times higher than in the undamaged rock. Fig. A5 shows the permeability maps of the pre-salt layer at 112 Myrs, in the original and in the new model. Once completed, the geomodel was back-stripped automatically according to the porosity versus depth curves assigned to each lithofacies.

### 3.2.3. Basin/thermal simulations

Physical processes included in the simulations were thermal

<figure><img src="image_10.png" /><figcaption>Fig. 6. General presentation of the basin model, a: contour of the FSM sub-model, b: 2 cross-sections of the basin model colored by facies showing the model base (in grey), pre-salt, salt (in pink) and post-salt (in green), c: basal surface of the basin model colored by the crustal CO₂ flux.</figcaption></figure>
evolution, two-phase fluid flow and compaction, as well as petroleum maturation, expulsion, and migration. These processes are part of the state-of-the-art basin/thermal modeling, whereas modeling the transport of dissolved salt and CO₂ was achieved through an in-house prototype (described separately below).

### 3.2.3.1. Forward simulations of temperature, two-phase flow and compaction, and the petroleum system.
Temperature is an important parameter to simulate because it controls CO₂ solubility and most geochemical reactions at depth including thermogenic hydrocarbon production. It also controls fluid viscosity and fluid density, and hence, fluid flow. For this study, we applied a time-varying heat flux map at the base of the model, extracted from the original model. In the latter, the thermal model simulated the thermal coupling between the sediments and the crust during the rifting sequence, following the McKenzie-type crustal model implemented in the TemisFlow™ advanced basement tool. This full thermal model was based on a calibrated map of the crustal stretching factor assumed to prevail during the rifting.

The two-phase fluid flow is simulated using the Visco calculator of TemisFlow™ with a fully implicit generalized Darcy flow migration scheme. The gaseous phase consists of hydrocarbons and CO₂. The simulation provides predictions of porosity, pressure, and saturation of the two phases, as well as flow velocity in all cells of the models at the end of each depositional event. Flow and compaction are largely controlled by the petrophysical properties assigned to each lithofacies. In our simulations, compaction is the sole driver of the water flow as the bottom boundary and the lateral boundaries were prescribed as no water flow boundaries. The source rock is located below the reservoir layers, in layer #2 (numbered from bottom to top). It is assigned a Type I kerogen, and a uniform TOC of 4 %. A 5-class compositional kinematic scheme is applied.

### 3.2.3.2. Forward simulation of dissolved salt and CO₂.
A prototype version of the Visco simulator was also used to simulate dissolved salt and CO₂ transport and the CO₂ equilibrium between the water and gas phases. Salt solubility versus temperature is calculated using the polynomial function proposed by Harvie et al. (1984). Thermodynamic parameters used to calculate CO₂ solubility in brines are taken from Duan and Sun (2003). A uniform salinity at the time of sediment deposition is assumed in all layers of the model. The depositional salinity of all layers above the salt layer is assumed to be 35 g/l. In the salt layer and in the sediments deposited between 117 and 113.10⁶ years, the depositional salinity is prescribed to be 220 g/l according to the salinity data collected in the reservoir which still shows a very high salinity at present. At the base of the model (six bottom layers), a depositional salinity value of 10 g/l is prescribed.

The CO₂ content in the layers at the time of deposition is set to 0 throughout the model. The CO₂ source is assumed to be located at the base of the model and is constrained by a basal CO₂ flux map expressed in kg/m²/10⁶ years. A realistic scenario would be that the CO₂ flux at the base of the sediments is controlled by crustal thickening during the rifting. We have developed a mathematical transformation of the crustal thickening map that yields a normalized CO₂ production map. It is consistent with the global hypothesis that CO₂ is produced by the regional Outer-High fault and by the magmatic intrusion located beneath the center of the modeled area, where β(x,y) represents the beta factor map used in the McKenzie-type crustal thermal modeling:

$$CO2_{production}(x,y) = \left( \frac{\beta(x,y) - 3}{\frac{Max}{x,y} (\beta(x,y) - 3)} \right)^2$$

This transformation was not based on any geodynamic theory but designed to peak where the stretching factor is the highest and to fall to 0 away from these areas.

We applied a multiplication factor to the normalized CO₂ production map. The CO₂ production at the bottom of the sediments was held constant from the initial time of basin formation until 113.10⁶ years, the time when rifting has ceased. After some trial and error, we set the maximum production to 7000 kg/m²/10⁶ years to observe a significant CO₂ concentration in the Pre-Salt field reservoir, as is currently observed. This CO₂ production is the maximum production flux we could find in the literature. It was obtained by combining the maximum mantle flux of crustal ³He proposed by Torgersen (1993) and the mantle ³He/CO₂ ratio proposed by Marty and Jambon (1987). The final CO₂ production flux at the base of the sediments is shown in Fig. 6.

### 3.2.4. Map-based analysis

The distributions of Mg-rich clay content (output of DionisosFlow™ simulations; Fig. 8a) and the aqueous CO₂ concentration that was calculated by TemisFlow™ were post-processed to populate the pre-salt reservoir layers with an indicator of the probability that clay has been converted to silica. Collard et al. (2023), demonstrated that Mg-rich clay dissolution and quartz precipitation are directly controlled by aqueous CO₂ content with no significant first order effect of pressure and temperature. These authors further quantified the effect of aqueous CO₂ concentration and Mg-rich clay content on the rate of silicification by means of a dedicated geochemical modeling workflow.

We have embedded the results of geochemical modeling (Collard et al., 2023) in our post-processing workflow by deriving specific intervals of CO₂ concentration and Mg-rich clay content and assigning to each interval the corresponding estimate of the transformation ratio of Mg-rich clay after 10 years (Table 4). We then used this transformation ratio and the Mg-rich clay content to calculate a net transformation ratio (NTR) which represents the fraction of the total rock matrix that has been converted to silica. Since many of the uncertainties discussed below were propagated throughout the workflow, we felt that the relative values of the NTR were more meaningful than the absolute values. We present the result in a qualitative manner by assigning a silicification probability indicator to each cell according to the following thresholds: i) Very low probability (NTR < 1 %); ii) Low probability (NTR in the range [1 %, 6.7 %]); iii) Medium probability (NTR in the range [6.7 %; 13 %]); and iv) High probability (NTR > 13 %).

Thresholds were chosen so that the cumulative area of high silicification probability across the 8 pre-salt layer maps is approximately 10 % of the cumulative area of low to high probability, and the threshold between low and medium probability was chosen so that the cumulative area of “very low probability” is approximately half of the total area.

## 4. Results

### 4.1. Forward stratigraphic modeling

#### 4.1.1. Lower Barra Velha Formation

A depositional environment with paleo-bathymetries from 20 to 50m around the paleo-highs, about 70m on the nearby flanks, and up to about 200m in the offshore basin areas was configured to enable the model the formation of the extensive lacustrine carbonates of the lower Barra Velha Formation. Sedimentation occurred in the early post-rift stage of the Santos Basin, where subsidence on the paleo highs was up to 30 m/ Ma and in the offshore area about 100 m/Ma, reaching locally high values of 150 m/Ma. Additional accommodation space was generated due to the variation of the eustatic lake level curve, which reached a local maximum at about 121 Ma, then dropped 40m at 119 Ma, before reaching a maximum again at the end of the Lower BVF deposition. Best results for thicknesses from well data and seismic horizons as well as facies distribution patterns were obtained with sediment production rate values of 50 m/My for spherulite and shrub, 60 m/My for Mg-rich clays and 80 m/My for hemipelagic micrite (Fig. 5, Table 1). Transformation rates for spherulite and shrub into reworked sediment were fixed at 20 m/My. Wave energy had to exceed 20 kW/m for shrub production to
<figure><img src="image_11.png" /><figcaption>Fig. 8. Maps calculated in the upper pre-salt layer, a: Mg-rich clay content, b: net transformation ratio, c: salinity.</figcaption></figure>

<table><caption>**Table 4**
Estimated Mg-rich clay transformation ratio after 10.10⁶ years, per range of CO₂ concentration of the water and clay content of the rock matrix.</caption><thead><tr><th rowspan="2">Class of CO₂ content (ppm)</th><th colspan="4">Class of clay content</th></tr><tr><th>[67,5 %; 100 %]</th><th>[45 %; 67,5 %]</th><th>[25 %; 45 %]</th><th>[0 %; 25 %]</th></tr></thead><tbody><tr><td>[0; 2500]</td><td>-1.5 %</td><td>-1.3 %</td><td>-5 %</td><td>-11 %</td></tr><tr><td>[2500; 8500]</td><td>-7 %</td><td>-8 %</td><td>-15 %</td><td>-50 %</td></tr><tr><td>[8500; 37500]</td><td>-25 %</td><td>-30 %</td><td>-60 %</td><td>-100 %</td></tr><tr><td>Above 37500</td><td>-99 %</td><td>-100 %</td><td>-100 %</td><td>-100 %</td></tr></tbody></table>

occur.

Modeling results show that the paleo-highs are dominated by shrubstone with a surrounding fringe of spherulitestone (Fig. 9). Reworked facies are present on the paleo-highs in permanently exposed areas. They also occur on the paleo highs during high lake level and in alternating sequences with micrite and Mg-rich mudstone near the paleo-highs, with a higher frequency at the beginning of the lower Barra Velha Formation. The intermediate bathymetry of the lake, between 30 and 50m near the paleo-highs is dominated by Mg-rich mudstone. The deeper offshore areas of the lake are dominated by hemipelagic micritic sediments.

Mg-rich mudstone occurs predominantly near the paleo-highs in lake bathymetries between 30 and 50m (Fig. 9). They surround the flanks of the paleo-highs. Concentrations of “sediment type” Mg-rich clay can reach up to about 60 % (Fig. 10). They are particularly high to the west of the paleo-high, but highest concentrations are found to the south of the paleo-high. The maximum distribution of Mg-rich clay is reached in the Lower Barra Velha Formation at about 119 My when a lowering of the lake level occurred.

Thickness calibration around the paleo-highs where well data were available shows a mean thickness error of -13m. For the entire modeled area, the thickness error is -75m (Fig. A6).

### 4.1.2. Upper Barra Velha Formation

Paleobathymetries at the base of the Upper BVF are almost identical to those of the Lower BVF, but the relief gradually becomes flatter (cf. Fig. A3). At the top of the Upper BVF, the relief is only up to 50m for most of the study area which looks like a shallow lake, prior to salt deposition. The subsidence has a higher range compared to the Lower

<figure><img src="image_12.png" /><figcaption>Fig. 9. Evolution of facies development and correlation with the lake level throughout the Barra Velha Formation. Mg-rich mudstones get predominantly deposited at the flanks of the paleo high, whereas the paleo highs are dominated by shrubstones and spherulitestones. BVF = Barra Velha Formation, SBU = Salt Base Unconformity, IAU = Intra-Alagoas Unconformity, PAU = Pré-Alagoas Unconformity.</figcaption></figure>

BVF with values around 50–80m around the paleo-highs and between 100 and 250 m/My in the basin area. The best results in terms of thickness and facies distribution patterns were obtained by increasing the carbonate production rates of the sediments especially of those presented on the paleo-highs (Fig. 5, Table 1). The values were 120 m/My for shrub, 100 m/My for spherulite and 80 m/My for Mg-rich clay.
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Average Mg-rich clay content for the Lower (a) and the Upper Barra Velha Formation (b). Cross section of the model in E-W direction showing the average Mg-rich clay content (c) and facies distribution (d) (vertical exaggeration = 10).</figcaption></figure>

Production rates for hemipelagic micrite remained constant at 80 m/My. Sediment transformation rates and wave energy are identical to the Lower Barra Velha Formation. The lake level curve has two minima for the Upper BVF at about 116 My and at 113.5 My, decreasing by about 40m each time. A maximum is reached at 115 My.

The boundary between the Lower and Upper BVF is characterized by a Mg-rich mudstone interval, which is also visible in the well data from the flanks of the paleo-highs (Fig. 11). Modeling shows that the shrubstones and spherulitestones are more abundant during the lake level maximum, at about 115 Ma. They appear to transgress from the highs onto the flanks and finally into the basin area, especially on the east side of the paleo-high. Mg-rich clays are less abundant compared to the Lower Barra Velha Formation, but locally reach higher concentrations, especially on the eastern flanks of the paleo-high with values of up to 67 % (Fig. 10).

Thickness calibration around the paleo-highs where well data is available shows a mean thickness error of –47m. For the entire modeled area, the thickness error is +80m (Fig. A6).

### 4.1.3. Comparison of FSM results and well data

Two representative wells, one located on the paleo-high (well D) and the other one on the paleo-flank (well C), were selected to compare the simulation results with the original facies log from well data (Fig. 11). An important consideration for calibration is the difference in resolution between the simulation result and the well data. The model has a time step of 0.25 My compared to a much finer resolution of the lithology log,

which explains the relatively more varying lithological distribution visible on the facies log. Shrubstones, spherulitestones and reworked facies that were simulated on the paleo-high, correspond to the main facies types present on the well-log well. The reworked facies was simulated mainly below the Intra-Alagoas Unconformity, which is consistent with the log data. Besides, shrubstones appear to be more dominant than spherulitestones based on modelling, an observation that is also confirmed by the log data (Figs. 4 and 11). The simulation for well C on the paleo-flank shows an alternation of Mg-rich mudstone and micrite up to the Upper BVF, with a maximum of Mg-rich mudstone around the Intra-Alagoas Unconformity. Reworked facies are present at the base of the Lower BVF. The top of the Upper Barra Velha Formation is dominated by spherulitestones. The simulation results are consistent with the general trend visible in the lithology log of well C, which shows a transition from micrite to Mg-rich mudstone around the Intra-Alagoas Unconformity to an alternation of spherulitestones and shrubstones at the top. The lithology log shows many alternations of Mg-rich mudstone and micrite with reworked facies that could not be reproduced in the simulation. Besides, the shrubstone intervals at the top of the BVF could not be reproduced for this well.

### 4.1.4. Uncertainty and sensitivity analysis of FSM

A total of 30 simulations were run, following the Latin hypercube design to parameterize the selected uncertain parameters of the model. The calculated mathematical response surface shows a predictivity of 0.967, which is close to 1 and therefore shows high predictivity. The average percentage of Mg-rich clay for the 30 simulations varies between 7.43 and 14.57 %. Fig. 12 shows that four parameters have a significant contribution to the average Mg-rich clay percentage in the model (contribution >10 %); the most influential of which is subsidence (50.1 %). Carbonate production vs. water depth (19.1 %), carbonate production vs. time (15.9 %) and lake level variation curve (11.7 %) have a significant influence. The rest of the parameters tested have a relatively small effect (<0.5 %).

## 4.2. Basin/thermal modeling

The lithofacies distribution of the new model has been modified from the calibrated original model (see above), therefore, we first ensured that the new model remained calibrated by comparing the pressure, temperature, and hydrocarbon saturation over time of the two models. There was no significant difference in these quantities: the model remains hydrostatic, and the hydrocarbon filling history of the traps is unchanged.

### 4.2.1. Paleo-hydrodynamics

The general paleo-hydrodynamics in the layers of interest is controlled by the impermeable boundaries – the upper boundary consisting of the salt layer, and the bottom and lateral surfaces - and the quasi-hydrostatic pressure at the lateral boundaries, which is obtained by imposing a high permeability on a 1-cell layer at the lateral boundaries. It follows that the general flow pattern is centripetal and parallel to the stratigraphy (Fig. 8c), with the highest flow velocity in the upper layers as compaction and permeability increase upward. The normal to bedding component of the flow velocity is generally much lower, but we can observe localized vertical fluid exchange controlled by local permeability heterogeneities (Fig. 13). Fluid flow velocity is highest immediately after salt deposition with maximum velocities of about 5000 m/Myrs in the upper reservoir layer and decreases thereafter. After 100 Myrs, maximum fluid velocities rarely exceed 50 m/Myrs in the area of interest.

With the new distribution of lithofacies given by the FSM study, the increase in permeability along the major faults and the new lateral boundary conditions, the paleo-hydrodynamics are likely to have been modified from the original model. Fig. 13 confirms the difference between the fluid flow velocity fields of the two models. This is further
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Comparison between the facies logs of the hard data and the facies logs of the simulated data for representative wells on the paleo flank (C) and on the paleo high (D). BVF = Barra Velha Formation, SBU = Salt Base Unconformity, IAU = Intra-Alagoas Unconformity, PAU = Pré-Alagoas Unconformity.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Graphical representation of the parameter contribution on Mg-rich clay percentage in the model.</figcaption></figure>

illustrated by an upward flow in the two base layers of the new model below the anticline, whereas no significant hydrodynamic connection between the base layers and the upper reservoir layers could be observed in the original model (see Fig. 13).

### 4.2.2. Salinity, CO₂ concentration, and fluid evolution

The salinity of the lower pre-salt layers remains constant throughout the simulation while the salinity of the upper pre-salt layers decreases in areas where mixing with low salinity water from below occurs. In these areas, the rate of salinity decrease is controlled by the velocity of the water flow. It follows that the salinity stabilizes during the [106; 93,5] Myrs time interval. The upper pre-salt salinity map in Fig. 8c shows two main NE-SW trending low-salinity plumes, one crossing the northern part of the Pre-Salt field anticline, and the other one overlying the southern part of the Outer High structure.

CO₂ can migrate both as an advected solute and as a component of the gaseous phase in cells where the water is saturated with CO₂. In our simulations, where CO₂ is injected into the bottom layer at a constant rate until 113 Myrs, we observe that a gaseous phase of CO₂ appears at 119 Myrs in the bottom layer and starts to migrate upward due to buoyancy and hydrodynamics. This upward migration is localized in “gas chimneys” feeding CO₂-rich traps located in the pre-salt layer, especially along the Pre-Salt field anticline (Figs. 13 and 14). In addition, hydrodynamics generate plumes of CO₂-rich water that emanate from these chimneys and spread horizontally. The upper pre-salt layer shows two areas of high aqueous CO₂ concentration, along the central part of the Pre-Salt field anticline, and above the southern part of the Outer High structure.
<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Flow velocity field calculated with TemisFlow™ at t = 112 Myrs along the reference EW vertical cross-section of the pre-salt layers, with the lithology distributions of the original model (a) and the new model (b). The cross-sections are colored with facies index (see Fig. 7), the black ellipse focuses on the upward flow that brings the deep fluids to the upper layers in the new model.</figcaption></figure>

High structure.

The basin simulation also provides insight into the filling history of the Pre-Salt field reservoir. The CO₂ started to form a gas phase between 113 and 112 Myrs, during salt deposition, while hydrocarbons appear soon after between 112 and 106 Myrs (Fig. A8). Maximum saturation is reached at 106 Myrs. Thereafter, we observe a that the CO₂ concentration decreases slightly in favor of the hydrocarbons.

**5. Discussion**

This study investigates the potential outcomes of the interactions between Mg-rich clays (host facies) and the CO₂-rich fluid upwelling, and the resulting silicification of lacustrine carbonate reservoir of the BVF at the basin scale. While previous studies have extensively explored the implications of the controlling diagenetic processes involved at the well scale (Basso et al., 2023; Lima de Jesus et al., 2023) and at the reservoir scale (Almeida Carvalho et al., 2024), they have not addressed the predictability of silicification beyond well control at the basin scale. To fill this gap, we apply a new integrative workflow based on 3D process simulation coupled with quantitative geochemical analysis to understand the silicification probability of the BVF in space and time at

<figure><img src="image_17.png" /><figcaption>**Fig. 14.** CO₂ concentration calculated along the reference cross-sections in the pre-salt layers (map view of the cross-sections traces on Fig. 6). The black line separates the cells with no free gas (top) from the cell containing free gas (bottom).</figcaption></figure>

the basin scale.

**5.1. Implications of the integrated stratigraphic and burial/thermal modeling workflow**

The conceptual geological model as described in the literature (Gomes et al., 2020; Herlinger et al., 2017; Liechoscki de Paula Faria et al., 2017) with shrubstones and spherulitestones present on the paleo-high, Mg-rich clay on the paleo flanks and micrite in the basin part was tested with the FSM approach and could be confirmed. The FSM simulation results show good calibration in terms of lithology and sequence thickness for the Lower and Upper BVF when compared with well log lithology and sequence thickness data (Fig. 9, Fig. A6, Fig. 11). Therefore, values such as sediment production, lake level curve, subsidence, paleobathmetry can be considered calibrated for the time at deposition.

The sedimentary system changes from a mud dominated system in the Lower BVF to a shallow water dominated system in the Upper BVF, which is especially recognizable in the well data from the wells on the flanks of the paleo high (Fig. 4). To achieve a match of the modeled results with the well data, carbonate production curves for the “sediment types” for the Upper BVF needed to be increased by 33 % for Mg-rich clay, 100 % for spherulite and 140 % for shrub compared to the Lower BVF. Deposited thicknesses vary for the Lower BVF between the paleo high and the flanks but are much more homogenous for the Upper BVF (Fig. 4). In the Upper BVF shrubstones and spherulitestone are also present at the flanks of the paleo high and extend further into the basin (Figs. 9 and 10). Shrub and spherulite “sediment types” depend on biological factors for their growth (Ceraldi and Green, 2017; Gomes et al., 2020). A potential change in lake water chemistry or a change in lake currents delivering more nutrients could have led to more favorable growth conditions during the Upper BVF. During the Lower BVF several deeper lakes might have existed around the paleo high which merged into a more continuous larger lake during the Upper BVF. A larger connected lake could favor the distribution of nutrients. Those changes could be related to underlying tectonic origins shown by the presence of unconformities.

Introducing uncertainty into the model by varying the input parameters of the simulation showed a high predictivity of the facies
distribution. It shows that the model is robust as there is minor variation in the average percentage of “sediment type” Mg-rich clay between 7.43 and 14.57 % across the study area. The result of thickness calibration between the seismic horizon data and the simulation is well achieved in zones with well data around the paleo-high ranging from -13 to -47m (Fig. A6). Mismatches are localized in the Northwest (simulation thickness too thin) and the Southeast (simulation thickness too thick) far away from well data in the basin area. Getting a better calibration result in those areas was not without losing the thickness calibration in other parts of the model. Sensitivity analysis identified subsidence with a contribution of over 50 % as the most influential parameter on the percentage of Mg-rich clay in the model (Fig. 12). This parameter comes from the seismic horizons, a type of data that already has a higher uncertainty in the area. The BVF is covered by a 2000–2500m thick evaporite layer (Chang et al., 1990; Dias, 1998; Pereira and Macedo, 1990). Low contrast rock densities in the formation plus the coverage by thick evaporites complicate seismic interpretation and time-depth conversion. While the seismic horizons did not affect the primary outcome of this study, future work on seismic interpretation and depth-migration in the study area could reduce the uncertainty.

The workflow used in this study differs from most standard basin analyses by first introducing a preliminary stratigraphic forward modeling stage (Fig. 2). FSM simulates geological processes through time to reconstruct lithology distribution. Coupled with basin modeling it has been shown to reduce uncertainties in petrophysical parameter distributions by better constraining lithofacies (Barabasch et al., 2019; Christ et al., 2016; Dubille et al., 2017).

The FSM approach provided 8 detailed lithology distribution maps for the pre-salt formations. In particular, the spatial distribution of Mg-rich clays is an essential element for the understanding of the silicification intervals in this part of the Santos Basin (Pereira et al., 2023). Coupled with the burial and thermal model the probability of silicification can be predicted. Mg-rich clays occur predominantly in a bathymetry window of 20–50m because of the associated carbonate production curve. The Mg-rich clay production is supposed to cease with increasing bathymetry, as it requires a high pH value of >9.5. At deeper bathymetries elevated methane levels and associated acidity due to organic sediment accumulation is supposed to prevent the formation of Mg-rich clays (Tosca and Wright, 2014). Therefore, the production curve was adapted to follow this trend. This production window results in a widespread Mg-rich clay accumulation in the basin but elevated values in proximity to the highs and on the paleo-flanks, which offered favorable conditions for accumulation below the wave base (Fig. 10). With the FSM approach and its validation by means of to the calibration at the well locations, average Mg-rich clay content could be calculated at the basin scale. FSM results also provided basin-scale spatial distributions for other lithologies present in the BVF, which constrained the porosity and permeability distributions for the burial/thermal model (Fig. A5). Shrubstones and spherulitestones were assigned a higher permeability (increased by a factor of 1000) than micritic and Mg-rich mudstones, resulting in a contrast in rock properties. Compared to the first-generation burial/thermal model, a more differentiated flow velocity field becomes apparent (Fig. 13). Areas of shrubstones and spherulitestones act as pathways for CO₂ migration from deeper layers along faults and along stratigraphic layers forming chimney and tree-like structures (Fig. 13).

The same phenomenon has been observed at the well scale (Basso et al., 2023) and at the reservoir scale (Almeida Carvalho et al., 2024). This result highlights the importance of the lithology distribution as one of the key drivers of fluid migration across different observation scales. Therefore, it is important to include a detailed lithology distribution to correctly predict the silicification probability. Around the Pre-Salt field anticline, FSM predicts a lithology dominated by shrubstones and spherulitestones (Figs. 9, 10 and 13). These act as conduits for CO₂ migration and CO₂ can rise to the salt base, along which it begins to migrate laterally (Fig. 14). Where the FSM predicts a predominantly

micritic lithology, such as north of the Pre-Salt field anticline, low permeabilities act as a seal for CO₂ migration, resulting in high CO₂ concentrations being trapped in the pre-salt layers (Fig. 14). The effect on general hydrodynamics is difficult to measure but is probably of secondary importance compared to the role of faults. Comparison of the hydrodynamics of the original model with the new model (Fig. A7) shows that the combined effect of the decrease in permeability in the shale facies and the increase in permeability along faults leads to local differences in CO₂ concentration due to flow modifications, but the regional trends are preserved.

## 5.2. Quantitative assessment of the dynamics of CO₂ migration in the basin

Results of this integrated basin modeling workflow not only provided insight into the probability of silicification, but also highlighted some interesting features regarding the CO₂ system. Analysis of the basin simulation results shows that the CO₂ can only begin to migrate from the deepest layers when the permeability is sufficient to allow the flow, i.e., in the relatively shallowest parts of the deepest layer (Fig. 14). In the deepest areas, the permeability of the rock is close to the irreducible permeability after salt deposition and is no longer compacting, trapping the dissolved and free CO₂ in the porosity. As the CO₂ accumulates over time, it forms a gas phase and the overpressure begins to rise, but not enough to exceed the capillary pressure of the source rock sitting above the bottom layer (Fig. A8).

Upward migration of CO₂ occurs in areas where the basement is shallower, making the lowest layers more permeable, particularly around the Pre-Salt field anticline and the western compartment of the Outer High lineament where faults were modeled by 10 times more permeable facies (Fig. 14). These areas of relatively higher permeability provide flow paths for the upward migration of gaseous CO₂ by buoyancy and hydrodynamics generated by the low-salinity and CO₂-rich fluids expelled from the deepest layers by compaction (Fig. 14). As the deepest fluids flow into the upper pre-salt layers, they are locally mixed with the fluids in place, thus reducing local salinity, increasing CO₂ solubility and CO₂ concentration, and ultimately feeding the gaseous phase with CO₂. Therefore, the uncertainty in predicting the CO₂ concentration in the subsalt layer is primarily due to the uncertainty in the sediment thickness which controls the distribution of permeability and rock compactability in the deepest layers, and to a lesser extent to the position and permeability of major faults which provide localized flow paths connecting the basement to the upper pre-salt layer. The sensitivity of the distribution of CO₂ concentration to the geological scenario is illustrated in Fig. A7, where a comparison between the two maps of CO₂ concentration predicted with the original – with no fault and a basic lithological distribution – and the updated model at 112 Myrs in the pre-salt layer. The two maps show the same trends with local differences, notably sharper concentration peaks in the new model such as the concentration peak located at the northeast end of the Pre-Salt field anticline. The CO₂ production flux map may affect the relative concentration peak at the top of the deepwater inflows but not their location. We expect it to play a secondary role in the overall trends of the silicification probability maps obtained in this study. This analysis shows that the basin simulation contributes to the workflow mainly by predicting a distribution of the CO₂ concentration that is quite robust, since it is controlled to the first order by the sediment thickness which shows little uncertainty. This conclusion is very specific to the geochemical system considered here where pressure and temperature play only a secondary role.

The basin simulation also provided insight into the temporal evolution of the composition of the non-water phase - including hydrocarbons and CO₂ - trapped in the Pre-Salt field reservoir. CO₂ appeared first, but very shortly before hydrocarbons (Fig. A9). This supports the assumption that the diagenetic episode was very short in time, if the arrival of hydrocarbons stopped the process, or that diagenesis continued after the
arrival of hydrocarbons, possibly with slower kinetics. Finally, this analysis also showed a slight decrease in the amount of CO₂ trapped in the reservoir over time, which can be interpreted as a continuous supply of fresh hydrocarbons while the supply of CO₂ from the deepest layers has decreased due to reduced compaction and the interruption of CO₂ production.

Our study focusses on an internal source of silica associated to a first phase of silicification and CO₂ rich fluid upwelling along deep-rooted faults. However, there are other possible scenarios regarding silicification. For example, hydrothermal activity can also be a source of silica and the interplay of burial diagenesis and hydrothermal fluids can lead to the precipitation and dissolution of minerals impacting the reservoir quality in the region (De Ros, 2021). Dissolution of Mg-rich clays and silicification could also come from maturation of organic matter, which can generate CO₂ and organic acids, which can in turn also dissolve Mg-rich clays. Future studies could investigate alternative scenarios regarding the CO₂ origin and silica source to calculate silicification probability with our described workflow.

### 5.3. Consequences of the interactions of CO₂-fluids and the carbonate host rocks: silicification

Collard et al. (2023) investigated the mechanisms driving the silicification of continental carbonates using geochemical batch modelling constrained by a large set of samples from the Barra Velha Formation, Santos Basin (Brazil). They evaluated the contribution of different silica sources, including the contribution of Mg-rich clay alteration to silicification, and the physicochemical conditions that trigger silicification. Their models showed that pH is the first-order parameter controlling the stability of Mg-rich clay. pH can be influenced by several processes, including local CO₂ inputs in sedimentary basins. They also showed that the water-to-rock ratio and secondary mineral formation may constrain the dissolution of Mg-rich clay and the precipitation of secondary phases such as quartz, dolomite and magnesite by renewing the source of proton more effectively and acting as a sink for Si and Mg which affects the saturation state of the solution.

The dissolution of carbonates and their cementation or replacement by silica are supposed to be a driving factor on the permeability and porosity. High silicification probability can be an indicator for reservoir quality. For this reason, the understanding of the silicification processes and the modeling of the spatial distribution of silicification probability are of great importance in the characterization of pre-salt carbonates.

The integrated process-based modeling workflow presented above, provided therefore the two essential input data types for the assessment of silicification probability on the Outer High of the Santos Basin (Fig. 2). FSM simulated the spatial heterogeneity of sedimentary facies, notably the Mg-rich clays. Basin modeling simulated the temporal evolution and spatial distribution of controlling factors for silicification, notably pressure, temperature, salinity, and CO₂ concentration. Combining these with the results of process-based geochemical modeling (Table 4; Collard et al., 2023) allowed the computation of Mg-rich clay transformation probability across the study area.

Silicification probability maps were calculated for the 8 pre-salt layers. Silicification probability appears to be very low in most parts of the Outer High (Fig. 15). It is higher close to the Pre-Salt field anticline and in the northeast and southeast parts of the Outer High, where the CO₂ concentration and Mg-rich clay content reached sufficient levels to dissolve Mg-rich clay and precipitate quartz in the host-rocks. In more details, medium to high silicification probability is observed in a few small spots situated to the east and at the Pre-Salt field anticline where it peaks at the northern end of the anticline crest (Fig. 15). These areas coincide with those showing relatively high Mg-rich clay contents (Figs. 8a and 7), as well as zones of high CO₂ concentrations (Fig. A7). Consequently, the produced silicification maps are coherent with the input data and the process-based calculations. They point out to those areas (and depth intervals) where the continental carbonate rocks have

<figure><img src="image_18.png" /><figcaption>Fig. 15. Silicification probability map calculated for the Upper Barra Velha Formation pre-salt layer.</figcaption></figure>

been silicified and affected by inherent dissolution (of remaining carbonates), resulting in highly porous and permeable pre-salt reservoirs that are produced in the various fields offshore Brazil.

Quantitative silicification data from the other reservoirs in the study area were not provided, so the predicted silicification probability maps cannot be compared with in-situ observations. An interesting perspective of this study would be a regional analysis of silicification to evaluate the validity of our prediction.

## 6. Conclusions

To better understand and predict reservoir quality in the Barra Velha Formation at the larger, basin-scale, we focused our work on the spatial and temporal prediction of the silicification probability due to the interaction between CO₂-rich fluids and Mg-rich clays. For this purpose, we have developed an innovative workflow that integrates forward stratigraphic modeling (FSM), basin/thermal modeling and quantitative geochemical modeling. FSM simulated with a process-based modeling approach the Mg-rich clay distribution and associated uncertainties at the time of deposition of the Barra Velha Formation. Basin modeling simulated the factors that control the diagenetic reactions responsible for silicification over time, namely the CO₂ flow, salinity, thermodynamic conditions, and fluid composition. Geochemical modeling allowed calculation of the Mg-rich clay transformation ratio to silica. The combination of Mg-rich clay distribution, diagenetic control factors, and chemical kinetics allowed the calculation of silicification probability maps.

This study explored the scenario of an internal silica source and transformation of Mg-rich clays by CO₂-rich waters, which are expelled from the deepest parts of the Santos Basin via a deep-rooted fault system. The highest probability of silicification was observed at the northeastern end of the Pre-Salt field anticline. Other high silicification probabilities were identified above the Outer-High lineament and to a lesser extent in the northeast of the study area.

With respect to reservoir quality, it is essential to understand not
only the silicification process, but also the dissolution of the matrix and replacement with dolomite, at both the well and basin scales. An integrated process-based modeling workflow, as demonstrated in this study, coupled with geochemical modeling that also focuses on dissolution and dolomitization could help in understanding reservoir quality.

## CRediT authorship contribution statement

**Alina-Berenice Christ:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. **Marie-Christine Cacas-Stentz:** Writing – review & editing, Writing – original draft, Validation, Software, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Nathalie Collard:** Writing – original draft, Validation, Supervision, Software, Methodology, Investigation, Formal analysis. **Fadi Henri Nader:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Elisabeth Bemer:** Supervision, Project administration. **Rodrigo Brandão Bunevich:** Validation, Supervision, Methodology, Investigation, Formal analysis. **João Paulo Borges Gomes:** Validation, Supervision, Investigation, Formal analysis. **Olinto Gomes de Souza:** Validation, Supervision, Methodology, Investigation. **Maria Anna Abreu de Almeida dos Reis:** Validation, Supervision, Methodology.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The results and interpretations of this paper were part of a collaboration between IFP Energies nouvelles and Petrobras. The authors greatly acknowledge Petrobras for the data release and permission to publish this research. We also thank Dr. Didier Granjeon for reviewing the manuscript. We extend our appreciation to the journal's reviewers and editors.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.marpetgeo.2025.107455.

## Data availability

The data that has been used is confidential.

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