Pre-salt Carbonate Reservoirs Research Articles

Impact of fracture set scales and aperture enlargement due to karstic dissolution on the fluid flow behavior of carbonate reservoirs: A workflow to include sub-seismic fractures in 3D simulation models

Carbonate reservoirs might contain a large number of critical heterogeneities, such as fractures and karstic features, although some the existing heterogeneities cannot be identified due to the lack of seismic resolution. Our main objective is to evaluate which fracture scales need to be included in the fluid flow simulation models and, in particular, how to model subseismic fractures. We also consider the presence of enlarged fractures in the reservoir models due to karstic dissolution. The fracture network used to compose the simulation models is based on the real karstified fracture sets of the Soledade outcrop, Jandaíra Formation (Potiguar Basin, Brazil). This formation is a large and well-preserved karstified carbonate platform of the Turonian-Campanian age related to the South Atlantic opening. Our work is divided into three phases: in Phase 0, we show how to upscale the fracture network of the Soledade outcrop to in-situ deep conditions. As a result, a semi-synthetic simulation model is composed from the Soledade Outcrop data and of a Brazilian Pre-Salt carbonate reservoir. In Phase 1, we evaluated the impact of the fracture scales and fracture enlargement due to karstic dissolution on the fluid flow behavior of the semi-synthetic reservoir. Finally, in Phase 2, we propose and validate a fluid flow simulation workflow allowing to estimate a statistically equivalent network of subseismic fractures, which might be used on multiple data assimilation methods or in risk analysis of green fields. Results from Phase 1 evidence that subseismic fractures can significantly impact the fluid flow behavior, depending on the producing well location in relation to the fracture network and on the presence of enlarged fractures due to karstic dissolution. On the other hand, the Phase 2 results provide the oil and gas industry with a workflow to better characterize sub-seismic fractures in fluid flow simulation models.

Tectono-Stratigraphic Modeling of the Wildcat Prospect (Gato do Mato), Santos Basin, Brazil Focusing on Presalt Carbonate Reservoirs

The Santos Basin is the largest Brazilian offshore basin and is currently the main hydrocarbon producer in the country, comprising an area of approximately 350,000 km2 . Given the importance of understanding and analyzing its carbonate reservoirs (Itapema and Barra Velha formations), a tectono-stratigraphic model of the Wildcat Prospect was created. This area is in the Outer High of the Santos Basin, main region of the presalt carbonate reservoirs in the Santos Basin. The 3D model is focused on the presalt reservoir and the understanding of the main tectonic structures in this area and their relationships that conditioned the carbonate successions deposition. Four steps were followed to analyze the depositional and structural framework and build the tectono-stratigraphic model: (i) generation and analysis of seismic attributes to identify and characterize seismic terminations (erosional truncation, toplap, and onlap), seismic facies (debris, build-ups, platform carbonates, and bottom lake), and assist in the tectono-stratigraphic analysis; (ii) seismic interpretation of horizons and faults, which were used as input for the modeling as surfaces and fault planes, creating compartmentalized blocks; (iii) construction of the stratigraphic column to classify the succession of tectonic and sedimentary events; and (iv) the construction of the model. This study helps to understand the depositional and structural evolution of the presalt and provides a three-dimensional understanding of the influence of faults on the reservoir geometry. Faults influenced the eroded zone observed in the Upper Barra Velha Formation, since this erosion occurs mainly at the edges of the large-throw faults. The build-up seismic facies, the main reservoirs in the study area, have a trend NW-SE to N-S following the large-throw faults.

Unlocking the properties of a presalt carbonate reservoir offshore Brazil with facies-constrained geostatistical inversion

A continuing concern regarding presalt carbonate reservoirs offshore Brazil is how to derive accurate quantitative estimates of reservoir properties. It is challenging to understand the link between the facies model and the variation in elastic properties, recover a reliable model of elastic properties from seismic, estimate porosities and permeabilities to use in reservoir simulations, and ultimately close the loop in integrated geology and engineering workflows. This case study describes our use of geostatistical inversion as a tool to unlock reservoir properties. We show how the integration of diverse information from various sources and at different scales is used to produce a meaningful range of probabilistic realizations of this Brazilian deepwater presalt reservoir. We do this while respecting the reservoir properties observed at the locations of drilled wells. We also present a workflow for an optimized implementation of the inversion results at the modeling stage, resulting in fast and geologically consistent history matching in an extremely challenging reservoir management environment. In this way, we achieve accurate history-matched cases on a field level and on a well-by-well basis while remaining within the uncertainty limits. Therefore, we produce geologically plausible and reliable scenarios.

E&P Notes (December 2022)

E&P Notes (December 2022)

Stratigraphic and sedimentary constraints on presalt carbonate reservoirs of the South Atlantic Margin, Santos Basin, offshore Brazil

Stratigraphic and sedimentary constraints on presalt carbonate reservoirs of the South Atlantic Margin, Santos Basin, offshore Brazil

Controls of fracturing on porosity in pre-salt carbonate reservoirs

This work aims to improve the understanding of how fracture zones affect carbonate reservoir properties based on observations of a pre-salt well located in the Santos Basin, Brazil. The identification of fracture zones allowed for the observation of a relationship between the occurrence of rock fractures and the silicification, as the latter plays an important role in determining porosity (higher silica content may increase brittleness of the rocks therefore increasing the likelihood of creating fractures zones and fractures may be filled up reducing the total porosity). To support the proposed observation, an integrated study was conducted using borehole imaging, spectroscopy logs, and sidewall core samples. The porosities were defined using nuclear magnetic resonance log analysis, alongside sidewall core samples, and thin sections. The integration of rock samples and well data with seismic analysis was performed to analyze the presence of a regional fault system that could explain high fracture densities as well as observed silica content characteristics. The results show how different types of cement filling up the formation pores affect fracture densities and total porosity. Furthermore, it was possible to infer that the amount of silica content observed in well logs and thin sections relates to hydrothermal fluids reaching out the reservoir through regional fault systems detected in the seismic section. Therefore, this paper supports the comprehension of how diagenetic processes can significantly affect the properties of pre-salt reservoirs.

Ensemble-based machine learning application for lithofacies classification in a pre-salt carbonate reservoir, Santos Basin, Brazil

Machine learning techniques have been widely used in the oil and gas industry to improve the qualitative and quantitative characterization of subsurface reservoirs. Because rock properties are strongly influenced by lithological and sedimentological information, lithofacies classification is an important step in 3D reservoir modeling. The aim of this study is to use supervised classification algorithms to predict the spatial distribution pattern of lithofacies classes using borehole and seismic data. In this study, lithofacies classes are distributed away from the wells using a machine-learning classifier. Seismic data attributes extracted from well locations are utilized as training data features in various supervised classification algorithms. Machine learning classifiers trained and evaluated for lithofacies classification include K-nearest neighbors, support vector machine, Gaussian naive Bayes, decision tree, Gradient Boosting, and Random Forests. A number of parameters are optimally determined in order to achieve the highest value of classification accuracy in the model. Comparing machine learning classifiers based on evaluation metrics reveals that ensemble-based decision tree approaches such as Random Forests and Gradient Boosting are the most effective for supervised classification. The results are validated using testing data and have an 80% classification accuracy. The predicted volume of lithofacies classes contributes to improved 3D reservoir modeling for the pre-salt carbonate reservoir.

Petro-seismic properties of Brazilian pre-salt carbonate reservoir: measurements and modeling at rock, log and seismic scales

Petro-seismic properties of Brazilian pre-salt carbonate reservoir: measurements and modeling at rock, log and seismic scales

Petrophysical characterization of Lagoa Salgada' stromatolites – A Brazilian pre-salt analog

Over the last decade, giant hydrocarbon reservoirs were discovered in lacustrine (Barremian-Aptian) and lagoonal (Aptian) carbonate reservoir systems, under Aptian evaporites, in the Santos and Campos basins. These pre-salt carbonate reservoirs contributed to placing Brazil among the world's top ten petroleum producers, and intense scientific interest has arisen to improve the characterization and understanding of these formations. The study of outcrop analogs is one of the most common techniques employed for such a goal. Several studies have pointed out that the Lagoa Salgada stromatolites are a potential analog of one of the important reservoir facies of pre-salt rocks. In this work, we used Mercury Intrusion Porosimetry (MIP) data obtained on these stromatolites for evaluating porosity, pore size distribution, surface area, tortuosity, permeability, and pore compressibility. These results provided insight into the pore system of these rocks and allowed a comparison to pre-salt reservoir data. This comparison highlighted that such permo-porosity and pore compressibility behaviors of two facies of these outcrop rocks are analogous to the Brazilian pre-salt stromatolites.

When distinction matters: Carbonate shrubs from the Aptian Barra Velha Formation of Brazilian's Pre-salt

Branched, bush- or shrub-like crystals are some of the most striking components of carbonate facies from the Cretaceous South Atlantic Pre-salt carbonate reservoirs. Many different analogues have been proposed to help to understand them, including travertines, lacustrine mounds and alkaline lake deposits, amongst others. Interpretation and genesis of these special facies involve discussions about biogenic vs. abiogenic formation of these branched components, relation with Mg-clays, role of microbes and growth of spherulites, amongst others. The present work shows some examples at microscale from a drilled core from Santos Basin (Brazil).The analyses of the shrub-like crystals present in the studied section allowed the distinction between four different types: (i) Type 1: fan-shaped fibrous-radial crystals; (ii) Type 2: fan-shaped, branched fibrous-radial crystals; (iii) Type 3: dendrite crystals; and (iv) Type 4: laminated crusts. All these components show similarities with their counterparts present in travertines, suggesting similar conditions controlling crystal growth in both Pre-salt and travertines. However, microfacies relationships show clear differences between the Pre-salt and those from travertine in the literature. Such Pre-salt microfacies presented here show a clear relationship between the development of the so called shrub-like crystals and the environment's energy, allowing the establishment of a proximal–distal pattern within a lacustrine environment as well as the identification of microfacies that could be related to (subaqueous?) hydrothermal vents.Distinction between the different types of branched components described here as well as their relationships with other microfacies are critical issues for the differentiation of the processes involved and the interpretation of the environments of formation (e.g., spring-related and proximal–distal environments within a lake). The observations made on samples from a drilled core of Brazilian's Pre-salt allow the discussion on some topics and interpretations involving the Pre-salt facies, providing potential research guidelines on these challenging continental carbonates.

Multi-objective optimization of CO2 recycling operations for CCUS in pre-salt carbonate reservoirs

This study evaluates the potential of recycling carbon dioxide (CO2) in the associated gas in ultra-deepwater pre-salt carbonate reservoirs for Carbon Capture Utilization and Storage (CCUS). We performed compositional subsurface modeling of a hypothetical field for multi-objective optimization, aiming at minimizing carbon emissions whilst maximizing the project's Net Present Value (NPV). Although no absolute reduction in total CO2 emissions was met with economic improvement, all simulated CCUS scenarios yielded a lower carbon footprint oil, with 43.5% reduction in operational emissions and up to 25.5% increase in NPV. The best CCUS case, a tapered Water Alternating Gas (WAG) design, presented break-even oil price of 39.5 USD/STB and operational CO2 abatement cost of negative 94.8 USD/tCO2 abated, also showing significant reduction in number of squeeze treatments and cost of calcite scale management.

Topological analysis of fault network in naturally fractured reservoirs: A case study from the pre-salt section of the Santos Basin, Brazil

The Santos Basin, located on the southeastern margin of Brazil, hosts giant hydrocarbon reserves of the pre-salt section that correspond to the carbonate sequences deposited during the syn-rift and transitional tectonic stages. The faults, which formed during the rift evolution, have proven to be crucial in controlling hydrocarbon accumulations and migration, not only as a pathway for fluid flow but as a factor controlling the deposition of sedimentary rocks. Thus, the topological analysis of fault networks is a key method to characterize the different fault sets, as it provides a way to describe the network arrangement of individual faults and their connectivity. In this work, the fault topology was analyzed in terms of nodes and branches for the main reservoir defined in the pre-salt section seismic units: Rift, Lower Barra Velha (Lower BVE), and Upper Barra Velha (Upper BVE). Four fault sets were characterized in these units with average orientations to NW-SE and E-W. The topology of this fault networks is characterized by single-phase rift network, featuring isolated systems with more than 50% of isolated branches and low connectivity (connections per branch being less than 0.5). The flow azimuth is NE-oriented, mostly oblique to the trend of the main faults, which suggests a low fault influence in the fluid flow. On the other hand, the paleotopographic highs developed during the Rift phase present the highest fault density and intensity, factors known to contribute to increased connectivity in reservoirs. A clear limitation of this type of study is the integration of the seismic-scale faults with high-frequency well log data, sparse but known to have great vertical resolution, and, in turn, assessing the vertical fluid flow, instead of lateral as is possible with this method. These limitations are to be known by one to apply this in other contexts. In this work, we seek to understand how the development of the rift fault system influences the generation of new fault sets, and fluid flow in a pre-salt carbonate reservoir.

A Carbonate Reservoir Prediction Method Based on Deep Learning and Multiparameter Joint Inversion

Deep-water carbonate reservoirs are currently the focus of global oil and gas production activities. The characterization of strongly heterogeneous carbonate reservoirs, especially the prediction of fluids in deep-water presalt carbonate reservoirs, exposes difficulties in reservoir inversion due to their complex structures and weak seismic signals. Therefore, a multiparameter joint inversion method is proposed to comprehensively utilize the information of different seismic angle gathers and the simultaneous inversion of multiple seismic parameters. Compared with the commonly used simultaneous constrained sparse-pulse inversion method, the multiparameter joint inversion method can characterize thinner layers that are consistent with data and can obtain higher-resolution presalt reservoir results. Based on the results of multiparameter joint inversion, in this paper, we further integrate the long short-term memory network algorithm to predict the porosity of presalt reef reservoirs. Compared with a fully connected neural network based on the backpropagation algorithm, the porosity results are in better agreement with the new log porosity curves, with the average porosity of the four wells increasing from 89.48% to 97.76%. The results show that the method, which is based on deep learning and multiparameter joint inversion, can more accurately identify porosity and has good application prospects in the prediction of carbonate reservoirs with complex structures.

Influence of halite particles on the stability and physical properties of inverse olefin emulsions

The study of synthetic drilling fluids, consisting of inverse emulsions, became essential to the routine of the oil industry in Brazil, due to the discovery of the pre-salt carbonate reservoirs. This work aimed to analyze the incorporation of halite in the physical properties and stability of inverse olefin emulsions, which composes synthetic drilling fluids formulation. Therefore, halite was characterized in relation to its wettability, chemical and mineralogical composition. The properties of the emulsions added with halite, in the contents of 5, 10 and 15% by volume, were monitored by microscopic images, rheological tests and gravitational separation, performed before and after dynamic aging of the emulsion. The results showed that the presence of the halite particles promotes an increase in the consistency index, interfering in the viscous nature of the emulsion. In addition, dynamic aging provide changes in the flow behavior of the emulsified system, once there are increases in the behavior index, proportional to the halite content, bringing it closer to Newtonian behavior. It is also noticed that, under these conditions, the addition of this mineral intensifies the separation of the continuous phase of the emulsion, together with the emulsifying agents, especially for the contents of 10 and 15%.

Mineralogy based classification of carbonate rocks using elastic parameters: A case study from Buzios Field

Brazilian presalt carbonate reservoirs are highly heterogeneous. This feature is mostly justified by the nature of the original depositional system and subsequently diagenetic processes. Consequently, reserve estimates and production forecasting are under large uncertainties. In this geologic context, it is of great relevance to develop techniques that helps to obtain a detailed description on the spatial distribution of these different rocks. In doing so, it contributes to the understanding of presalt carbonate sedimentary deposits, providing inputs for more predictive reservoir models. Traditionally, these carbonates are grouped into three classes, from which only one exhibits reservoir properties. Using a dataset from Buzios Field, this work proposes a characterization of presalt carbonate reservoir rocks by grouping them in terms of their mineral composition. Taking advantage of rock physics concepts, we aim to potentialize the use of elastic parameters for multiple rock type discrimination. We explored several attempts for rock classification by using a Bayesian approach. Among all the tested propositions, a two-step workflow for five lithotypes classification, emerges as the most appropriate for the Buzios Field. In this scheme, three lithotypes represent good-quality reservoirs and the other two are low-porosity and Mg-clay-rich carbonates. The average root-mean-square error of the most likely a posteriori rock proportions is around 8.4%, only approximately 1% higher than the conventional three lithotypes configuration. To support that, we compared different methodologies for Bayesian classification at well-log scale through acoustic impedance and compressional to shear velocity ratio. Potential applicability of the proposed methodology at field scale is reinforced by similar results achieved using well-logs filtered to the seismic bandwidth.

Fault and fracture study by incorporating borehole image logs and supervised neural network applied to the 3D seismic attributes: a case study of pre-salt carbonate reservoir, Santos Basin, Brazil

Fractures play a significant role in the development and production phases of carbonate reservoirs. Quantitative interpretation of fractures not only enhances reservoir models but also reduces the drilling risk and optimizes well design. In this study, we attempt to predict the fracture density map by integrating well and seismic data along with maximum horizontal stress identification. To this end, we propose a workflow with a set of machine learning approaches. First, 3D seismic data is conditioned after the migration processing sequence and the main faults and horizons are interpreted. Next, a number of curvature and coherence attributes are created for a supervised neural network technique to generate new seismic-based discontinuity attribute. Using a geostatistical method to incorporate the interpreted dip and azimuth attributes from well image logs and 3D seismic discontinuity attribute, the fracture density map is predicted and the results validated with a blind well. Finally, we evaluate the strike azimuth of possible open fractures based on the stress regime analysis, from which two distinctive zones are identified. There are, however, some limitations in this study. The predicted fracture density map can be employed to build a discrete fracture network, update dual porosity and permeability estimation, and identify sweet spots.

Compartmentalization and Stratigraphic-Structural Trapping in Pre-Salt Carbonate Reservoirs of the Santos Basin: A Case Study in the Iara Complex

Compartmentalization and Stratigraphic-Structural Trapping in Pre-Salt Carbonate Reservoirs of the Santos Basin: A Case Study in the Iara Complex

Water Alternative Gas (Wag) Optimization for a Heterogeneous Brazilian Pre-Salt Carbonate Reservoir

Water Alternative Gas (Wag) Optimization for a Heterogeneous Brazilian Pre-Salt Carbonate Reservoir

Geostatistics assisted by machine learning for reservoir property modeling: A case study in presalt carbonates of Buzios Field, Brazil

An embedded model estimator (EMBER) petrophysical modeling algorithm has been applied to obtain effective porosity and permeability within the presalt carbonate reservoirs of the Barra Velha Formation in Buzios Field, Santos Basin. This advanced methodology was used due to the heterogeneity and complexity of the reservoirs, which makes modeling by conventional geostatistical methodologies difficult. For effective porosity modeling, we chose one facies model, one stratigraphic seismic attribute (acoustic impedance), and one structural seismic attribute (local flatness) as secondary variables. Permeability was modeled by using the best effective porosity simulation result as a secondary variable. Our results demonstrate that average effective porosity and permeability were 0.10 v/v and 440 md, respectively, indicating good reservoir quality throughout the studied area. A vertical trend of high effective porosities and permeabilities for the basal and uppermost reservoir sections was identified in our results, as well as a trend with lower values for these reservoir properties for the intermediate reservoir section. The lower section of the formation presented more continuity, and we infer it to be the best reservoir interval. We observed two horizontal trends for these reservoir properties at the formation top: one of higher values aligned to the north–south direction at the structural highs and another of lower reservoir properties related to isolated structural lows within structural highs. Correlation between modeled results and the blind test ANP-1 well upscaled properties was high, and upscaled well-log property distributions were preserved in the EMBER simulations, proving the predictive capacity of the algorithm. Finally, conditional distributions analysis indicated that the basal section of the Barra Velha Formation presents higher uncertainty for the estimation of effective porosity. Even though this interval is considered to have the best reservoir characteristics, decision making should be done with caution for this section.

Impact of Fracture Topology on the Fluid Flow Behavior of Naturally Fractured Reservoirs

Fluid flow modeling of naturally fractured reservoirs remains a challenge because of the complex nature of fracture systems controlled by various chemical and physical phenomena. A discrete fracture network (DFN) model represents an approach to capturing the relationship of fractures in a fracture system. Topology represents the connectivity aspect of the fracture planes, which have a fundamental role in flow simulation in geomaterials involving fractures and the rock matrix. Therefore, one of the most-used methods to treat fractured reservoirs is the double porosity-double permeability model. This approach requires the shape factor calculation, a key parameter used to determine the effects of coupled fracture-matrix fluid flow on the mass transfer between different domains. This paper presents a numerical investigation that aimed to evaluate the impact of fracture topology on the shape factor and equivalent permeability through hydraulic connectivity (f). This study was based on numerical simulations of flow performed in discrete fracture network (DFN) models embedded in finite element meshes (FEM). Modeled cases represent four hypothetical examples of fractured media and three real scenarios extracted from a Brazilian pre-salt carbonate reservoir model. We have compared the results of the numerical simulations with data obtained using Oda’s analytical model and Oda’s correction approach, considering the hydraulic connectivity f. The simulations showed that the equivalent permeability and the shape factor are strongly influenced by the hydraulic connectivity (f) in synthetic scenarios for X and Y-node topological patterns, which showed the higher value for f (0.81) and more expressive values for upscaled permeability (kx-node = 0.1151 and ky-node = 0.1153) and shape factor (25.6 and 14.5), respectively. We have shown that the analytical methods are not efficient for estimating the equivalent permeability of the fractured medium, including when these methods were corrected using topological aspects.