EN 
BR For almost a century, humans have relied on centrifugal pumps for the transport of low-viscous fluids in commercial, agricultural, and industrial activities. Details of the fluid flow in impellers often influence the overall performance of the centrifugal pump and may explain unstable and inefficient operations taking place sometimes. However, most studies in the literature were devoted to understanding the flow in the midaxial position of the impeller, only with a few focusing their analysis on regions closer to solid walls. This paper aims to study the water flow in the vicinity of the front and rear covers (shroud and hub) of a radial impeller to address the influence of these walls on the fluid dynamics. For that, experiments using particle image velocimetry (PIV) were conducted in a transparent pump at three different axial planes, and the PIV images were processed to obtain the average velocity fields and profiles, as well as turbulence levels. Our results suggest that: (i) significant angular deviations are observed when the velocity vectors on the peripheral planes are compared with those on the central plane; (ii) the velocity profiles close to the border are similar to those in the middle, but the magnitudes are lower close to the hub than to the shroud; (iii) the turbulent kinetic energy on the periphery is up to eight times greater than that measured at the center. Our results bring new insights that can help propose mathematical models and improve the design of new impellers. A database and technical drawings of the centrifugal pump are also available in this paper so that other researchers can perform numerical simulations and validate them against experimental data.
Author: João Lucas Braga Da Silva
Particle image velocimetry in the impeller of a centrifugal pump: A POD-based analysis
The flow field within the channels of a centrifugal pump impeller is usually complex, containing turbulent structures in a wide range of time and length scales. Identifying the different structures and their dynamics in this rotating frame is, therefore, a difficult task. However, modal decomposition can be a useful tool for detecting coherent structures. In this paper, we make use of proper orthogonal decomposition (POD) of time-resolved flow fields in order to investigate the flow in a centrifugal pump. For that, we carried out experiments using time-resolved particle image velocimetry (TR-PIV) in a pump of transparent material operating at different conditions and obtained the statistical characteristics of the turbulent flow from phase-ensemble averages of velocities and turbulent kinetic energy. The results reveal that at the pump’s best efficiency point (BEP) the flow is well-organized, with no significant flow separation. For flow rates below the BEP, flow separation and vortex structures appear in the impeller channels, making the flow unstable. At flow rates above the BEP, intense jets appear close to the suction blades, while small instabilities occur on the pressure side. The POD analysis shows that at low flow rates, the flow is dominated by large-scale structures with intense energy levels, while at the BEP and higher flow rates, the flow is dominated by small-scale structures. Our results shed light on the turbulence characteristics inside the impeller, providing relevant information for reduced-order models capable of computing the flow in turbomachinery at much lower costs when compared to traditional methods.
A low-order preconditioner for high-order element-wise divergence constant finite element spaces
Mixed finite element problems are a class of problems that arises when modeling several physical phenomena, such as in computational fluid dynamics, structural analysis, optimization, etc. Designing efficient iterative schemes for such a family of approximations has been the subject of several works in the past decades. However, its success is intimately related to the proper definition of a preconditioner, i. e., the projection of the original algebraic system to an equivalent one with better spectral properties. In recent work, we have proposed a new class of H(div)-conforming finite element spaces with element-wise constant divergent. This family of elements was designed to improve reservoir simulation computational cost and are obtained by choosing the lower order space with piece-wise constant normal fluxes incremented with divergence-free higher-order functions. In this work, we propose an iterative scheme to solve problems arising in the context of the above mentioned element- wise constant divergence approximation spaces. The strategy consists on using the matrix of linear fluxes as a preconditioner to solve the higher-order flux problem. The latter is solved iteratively by means of a conjugate gradient scheme. In the presented numerical tests, this strategy has shown to be convergent in a few iterations for different problems in 2D and 3D. In addition, as internal fluxes are condensed, only boundary variables need to be computed. This strategy relates to the MHM technique and can be efficiently used to access fast multi-scale approximations in future work.
Recent advances in a multiscale flux-based method for simulating flow in fractured porous media.
Computational simulation of reservoir flow is an important tool that provides valuable insight into the decision process in oil extraction. Several types of commercial software have been developed over the years for this application, the majority using low-order schemes, which can become prohibitive for very large models. This issue becomes more apparent since, nowadays, the accuracy of a simulator is dominated by the accurate simulation of the multiscale characteristics of a reservoir such as permeability heterogeneity. To capture these multiscale features in low-order schemes, very refined models are required. Therefore, developing a high-order scheme able to simulate fractured reservoir flow that is accurate and can efficiently capture the multiscale features of the reservoir is of great value for the field. With this motivation, this presentation reports on recent advances in a methodology to simulate flow in highly heterogeneous fractured porous media using the Multiscale Hybrid-Mixed (MHM) method with H(div)-confirming flux approximations. This method is particularly appealing because of its inherent properties such as local mass conservation, multiscale features, and strong divergence-free enforcement for incompressible flows. Flow in the porous media is modeled with traditional Darcy’s equations and the coupling between flow in the porous media and fractures is based on the conceptual Discrete-Fracture-Matrix representation, where the fractures are idealized as lower-dimensional elements at the interface of matrix elements. The methodology is compared with benchmark examples to demonstrate its robustness, accuracy, and efficiency.
Rheological behavior of the stable water-in-oil emulsion associated to water droplets arrangement
Water-in-oil emulsion is a flow pattern that may occur during the oil production and generate flow assurance issues due to its rheological behavior. In addition to the increase of effective viscosity with water content increment, the emulsion has a complex rheological behavior regarding the fluid’s physicochemical properties, and surfactant presence. In this work, the rheology of stable water-in-oil emulsion formed by the shearing of a multistage centrifugal pump is investigated. The emulsion analyzed was composed of a mineral oil, SPAN 80 and tap water for different water cut and temperatures. The shear rate hysteresis analysis on emulsion flow curve were performed to analyze the rheological behavior. From this analysis, the steady-state shear stress condition was investigated regarding water droplet arrangement based on the Peclet number. Furthermore, the droplet arrangement was observed during acquisition of flow curve using a microscopy coupled on rheometer. To investigate the clustering mechanics in emulsion during rheometry, we present a surface boundary to separate the suspending and clustering droplets based on critical shear stress, droplet Reynolds number, and the water cut for different temperatures.
Influence of integration between reservoir and production systems considering polymer injection
In this work we evaluate the impact of integration between reservoir and production systems considering scenarios of polymer injection in a heavy-oil reservoir. We used a reservoir model named EPIC001 with characteristics from a Brazilian Sandstone offshore heavy-oil field. A Black-oil fluid model was used, considering heavy viscous oil (13° API). The production system is composed by 4 producers and 3 injectors wells. To integrate reservoir with production system, we use decoupled integration approach using vertical flow performance tables. Additionally, we propose an alternative approach to estimate a revised BHP for the integration. Simulation using the decoupled integration approach yields lower production compared to non-integrated scenarios based on initial conditions. The reduction was 22% for water injection and 41% for polymer injection, at concentration of 2.49 kg/m3. Sensitivity analysis of polymer concentration revealed that 1 kg/m³ was the most favorable concentration for the non-integrated case and 0.5 kg/m3 considering the integration. Revised BHPs approach lead to a production compatible with integrated case with differences reaching 2.46%. The results presented in this paper provide new insights into the importance of considering integration for accurate prediction, particularly in scenarios involving polymer injection in a heavy-oil reservoir. We also show that the best polymer injection concentration can change depending on the modelling approach and the revised BHP approach could be an alternative to integration.
Analysis of different objective functions in petroleum field development optimization
Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.
Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models Author links open overlay panel
In the past years, there is a growing interest in the applications of Generative Adversarial Networks (GANs) to generate geological models. Although GANs have proven to be an effective tool to learn and reproduce the complex data patterns present in some geological models, some challenges still remain open. Among others, a well-noticed problem is the need for a large number of samples to ensure high-quality training, which can be prohibitively expensive in some cases. As an attempt to offer a (possibly partial) solution to the aforementioned challenge, in this study, we investigate the feasibility and effectiveness of a zero-centered discriminator regularization technique for improving the performance of a GAN. Additionally, we evaluate an adaptive data augmentation technique to overcome the potential issue of limited training data, for the purpose of generating geologically feasible realizations of hydrocarbon reservoir models. Our findings demonstrate that a combination of the two techniques lead to notable performance improvements of a GAN. Particularly, it is observed that using the adaptive data augmentation technique in a GAN can yield similar results to those obtained by the GAN with a much larger dataset.
Visualization of Multiple Production Variables of Petroleum Field and Wells to Support the Selection of Representative Models
Petroleum engineers usually create hundreds of models of a reservoir to deal with its uncertainties. Since running flow simulations in all models is time-expensive, selecting a subset of “representative models” (or RMs) for simulations can reduce total simulation time without compromising analysis quality. However, judging the representativeness of the RMs and choosing the best model are hard tasks that visualization techniques can help to improve. This paper explores visualization techniques to aid engineers in evaluating and comparing the representativeness of a “solution” (i.e., a set of RMs) and of multiple solutions. We propose an interactive dashboard featuring: (a) a representativeness heatmap of multiple solutions, and (b) a set of crossplots of production variables of a solution, enhanced with convex hulls of representative and represented models. Experienced petroleum analysts evaluated the proposed visualizations positively, indicating the potential of these visualizations to enhance the process of choosing representative models.