EN 
BR Three-dimensional seismic data provides valuable insights into structural interpretation when dealing with naturally fractured reservoirs. The faults or fractures may control the direction of fluid flow in the reservoirs, especially during the sensitive analysis in projects of carbon sequestration and reservoir development, since these features may impact the rock-fluid interaction and oil field production. However, characterizing faults and fractures in pre-salt reservoirs using conventional approaches is challenging due to a complex and heterogeneous overlying evaporitic unit which reduces the seismic-to-noise ratio. Therefore, seismic data conditioning targeted at improving the seismic image is necessary for quality subsurface investigations. This work provides a systematic series of filters to enhance and detect discontinuities in post-stack seismic volume, primarily by reducing migration artifacts and salt multiples, thereby maintaining the true geological characteristics of the data. We emphasize the delineation of the fault and potential fracture zones by computing seismic attributes in a post-stack time-migrated 3D seismic in the Santos Basin’s deep waters. Initially, the seismic signal-to-noise ratio was analyzed, and a sequence of structural-oriented filters and spectral balancing was applied to generate vertically refined images with enhanced seismic amplitudes. To assess the quality of the conditioned data, we employed the Similarity and Curvature attributes to identify the distribution of discontinuities and compare the before-and-after denoising approach. The data conditioning and analysis of seismic attributes improved our understanding of the distribution and compartmentalization of the reservoirs, highlighting predominantly NE-trending discontinuities. The systematic processing significantly improved the signal-to-noise ratio, allowing us to identify the relative direction of structural elements and insights into potential fractured zones over the reservoir.
Month: July 2023
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.
An efficient construction of divergence-free spaces in the context of exact finite element de Rham sequences
Exact finite element de Rham subcomplexes relate conforming subspaces in H1(Ômega), H(curl; Ômega), H(div; Ômega), and L2(Ômega) in a simple way by means of differential operators (gradient, curl, and divergence). The characteristics of such strong couplings are crucial for the design of stable and conservative discretizations of mixed formulations for a variety of multiphysics systems. This work explores these aspects for the construction of divergence-free vector shape functions in a robust fashion allowing stable and faster simulations of mixed formulations of incompressible porous media flows. The resulting schemes are verified by means of numerical tests with known smooth solutions and applied to a benchmark problem to confirm the expected theoretical and computational performance results.
A posteriori error estimator for a multiscale hybrid mixed method for Darcy’s flows
This article presents a computable and efficient procedure for a posteriori error estimations of approximate solutions of Darcy’s flows given by a Multiscale Hybrid Mixed method. Based on a partition of the domain by polytopal macro subregions, this is a strategy for efficient simulations of problems with strongly varying solutions. The flux approximations interacting with the skeleton (subregion boundaries) are strongly constrained by a given trace space. Whilst the dimension of the piecewise polynomial trace space is expected to be as low as possible, the small-scale features of the solution are supposed to be accurately resolved by completely independent local stable mixed solvers, the trace variable playing the role of Neumann boundary data. As the method already gives an equilibrated global H(div)-conforming flux approximation, the methodology for the error estimation only requires a potential reconstruction. In addition to usual residual errors and indicators associated with the potential reconstruction, the estimation also takes into account the effect of discretizations of practical nonhomogeneous Dirichlet and Neumann boundary conditions. Based on the proposed a posteriori error estimator, ℎ-adaptive algorithms are constructed to guide a proper choice of the trace space. The performance of the error estimator and the adaptive scheme is numerically investigated through a set of illustrating test problems.
Electrofacies definition and zonation of the lower Cretaceous Barra Velha Formation carbonate reservoir in the pre-salt sequence of the Santos Basin, SE Brazil
Lower Cretaceous carbonates in the pre-salt succession in the Santos Basin, eastern Brazil, are highly heterogeneous in terms of their reservoir characteristics as a result of depositional and diagenetic factors. Electrofacies have widely been used for reservoir zonation and, when allied with computer-based methods such as neural networks, may help with the study of such complex reservoir rocks and with the identification of high-quality reservoir zones. In this work, an unsupervised artificial neural network known as a self-organizing map (SOM) was used to carry out a zonation of the pre-salt carbonates in the Aptian Barra Velha Formation, the main reservoir unit in the Santos Basin. Available data included gramma-ray, neutron porosity, resistivity deep, sonic, density, photoelectric factor, total porosity and effective porosity profiles from 21 wells together with mineralogical models. Core descriptions and thin section images were used as additional data for the lithological characterization of the electrofacies and consequently for reservoir zonation. A total of four electrofacies were defined from the SOM application, and five reservoir zones were identified.
The characterization of the reservoir zones also considered the structural locations of the wells based on the relative depth to top- Barra Velha Formation; well locations were classified as structurally high, intermediate or low. Based on the reservoir zone characteristics, the results could be correlated with zonations in previous studies. A general tendency was noted for there to be an increase of finer-grained sediments in the formation in wells located in structural lows; packstone and mudstone facies were prevalent in these wells and were in general characterized as poor-quality reservoir rocks. By contrast, the shrubstones and grainstones which were more frequent in structurally high wells comprised higher quality reservoir rocks.
The basal reservoir zone showed wide lithological variation compared to the overlying reservoir zones. Grainstone-dominated facies were identified in the middle of the formation, and the uppermost reservoir zones were characterized by an upward increase in shrubstones and reworked grainstones which in general pointed to better quality reservoirs.
Evaluation of unsupervised machine learning frameworks to select representative geological realizations for uncertainty quantification
Ensembles of geological realizations (GR) are normally processed by numerical simulators to evaluate geological uncertainty during the decision-making process. Although different stochastic spatial algorithms can quickly generate hundreds to thousands of GR to capture the full uncertainty range, the simulation process applied to this number of realizations is computationally expensive. Hence, a small subset of representative geological realizations (RGR) that statistically represent the features of the full ensemble can be used for uncertainty quantification. In this study, unsupervised machine learning (UML) is applied by considering different (1) adjacency matrix construction, (2) dimensionality reduction, and (3) clustering and sampling algorithms to generate several RGR sets. Then, the mismatches between the distribution of different field and well indicators obtained from the RGR sets and the whole ensemble are measured using the Kolmogorov-Smirnov (KS) test to compare the uncertainty space of the subsets and the full set. Furthermore, to measure the pairwise adjacency between the realizations, we use a static reservoir feature called reservoir quality index (RQI). We performed extensive computational analyses to appraise the performance of the UML in two benchmark cases. Each case contains 500 GR. This study can provide a comprehensive assessment of the UML for the RGR selection due to the application of different algorithms. The results showed that the RGR set can be successfully selected without previous flow simulation runs, if an appropriate UML method is employed. This leads to a reduction in the computational cost during uncertainty quantification and risk analysis. Furthermore, we observed that the optimal number of RGR should be chosen due to the geological complexity of each case study. We also found that the type of recovery mechanism has no impact on the optimal number of RGR and on UML methods. The appropriate RGR set can be used for production forecasts and development planning support.
by Seyed Kourosh Mahjour, Luís Otávio Mendes da Silva, Luis Augusto Angelotti Meira, Guilherme Palermo Coelho, Antonio Alberto de Souza dos Santos, Denis José Schiozer, published at Journal of Petroleum Science and Engineering, February 2022, Vol. 209, 109822.
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.
Experimental investigation of the Electrical Submersible Pump’s energy consumption under unstable and stable oil/water emulsions: A catastrophic phase inversion analysis
The presence of water in crude oil exploitation by the Electrical Submersible Pump (ESP) systems may cause several problems in energy consumption and operational instabilities due to emulsion formation. Indigenous surfactants in crude oil also contribute to emulsion stabilization, which can exacerbate these problems. In this paper will be experimentally investigated the influence of the emulsion stability on ESP energy consumption and operational instabilities through an 8-stage ESP operating with unstable and stable emulsions, with and without a demulsifier. The experimental tests were performed for one oil viscosity, a constant total flow rate, and two ESP rotational speeds. Initially, the ESP relative dimensionless power (RDP) was analyzed along with the emulsion system and the droplet size distribution (DSD). An interesting difference regarding the presence of surfactants was observed experimentally in the RDP and phase inversion points. The relationship among the ESP dimensionless power, torque, and electrical current with maximum droplet size allowed to conclude that these parameters can be related to the start of the coalescence process, i. e, able to predict the catastrophic phase inversion (CPI) point.
Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm
The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.
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