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Fluidized bed reactor systems are widely used due to excellent heat and mass transfer characteristics followed by uniform temperature distribution throughout the reactor volume. The importance of fluidized beds is further demonstrated in high exothermic reactions such as combustion and gasification where fluidization avoids the hot spot and cold spot generation. A bed material, such as sand or catalyst, is normally involved in fluidized bed combustion and gasification of biomass. Therefore, it is vital to analyze the hydrodynamics of bed material, especially the minimum fluidization velocity, as it governs the fluid flowrate into the reactor system. There are limitations in experimental investigations of fluidized beds such as observing the bed interior hydrodynamics, where CFD simulations has become a meaningful way with the high computer power. However, due to the large differences in scales from the particle to the reactor geometry, complex interface momentum transfer and particle collisions, CFD modeling and simulation of particle systems are rather difficult. Multiphase particle-in-cell method is an efficient version of Eulerian-Lagrangian modeling and Barracuda VR commercial package was used in this work to analyze the minimum fluidization velocity of particles depending on size, density and size distribution. Wen-YU-Ergun drag model was used to model the interface momentum transfer where default equations and constants were used for other models. The effect of the particle size was analyzed using monodispersed Silica particles with diameters from 400 to 800 microns. Minimum fluidization velocity was increased with particle diameter, where it was 0.225 m/s for the 600 microns particles. The density effect was analyzed for 600 microns particles with seven different density values and the minimum fluidization velocity again showed proportionality to the density. The effect of the particle size distribution was analyzed using Silica. Particles with different diameters were mixed together according to predetermined proportions as the final mixture gives a mean diameter of 600 microns. The 600 microns monodispersed particle bed showed the highest minimum fluidization velocity. However, some particle mixtures were composed with larger particles up to 1000 micron, but with a fraction of smaller particles down to 200 microns at the same time. This shows the effect of strong drag from early fluidizing smaller particles. The only variability for pressure drop during packed bed is the particle size and it was clearly observed in all three cases.

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively.

In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse^® reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%.

The efficiency of a fluidized bed reactor depends on the bed fluid dynamic behavior, which is significantly influenced by the bubble properties. This work investigates the bubble properties of a bubbling fluidized bed reactor using computational particle fluid dynamic (CPFD) simulations and electrical capacitance tomography (ECT) measurements. The two-dimensional images (along the reactor horizontal and vertical planes) of the fluidized bed are obtained from the CPFD simulations at different operating conditions. The CPFD model was developed in a commercial CPFD software Barracuda Virtual Reactor 20.0.1. The bubble behavior and bed fluidization behavior are characterized form the bubble properties: average bubble diameter, bubble rise velocity, and bubble frequency. The bubble properties were determined by processing the extracted images with script developed in MATLAB. The CPFD simulation results are compared with experimental data (obtained from the ECT sensors) and correlations in the literature. The results from the CPFD model and experimental measurement depicted that the average bubble diameter increased with an increase in superficial gas velocities up to $4.2 U_{mf}$ and decreased with a further increase in gas velocities due to the onset of large bubbles (potential slugging regime). The bubble rise velocity increased as it moved from the lower region to the bed surface. The Fourier transform of the transient solid volume fraction illustrated that multiple bubbles pass the plane with varying amplitude and frequency in the range of 1–6 Hz. Further, the bubble frequency increased with an increase in superficial gas velocity up to $2.5U_{mf}$ and decreased with a further increase in gas velocity. The CPFD model and method employed in this work can be useful for studying the influence of bubble properties on conversion efficiency of a gasification reactor operating at high temperatures.

A cold-flow lab-scale cross-flow fluidized bed classifier was simulated using the CFD software Barracuda VR^®. The purpose of the study was to identify the most suitable drag model and make the model adjustments that provide the best representation of the flow situation in the classifier when comparing the results with the experimental data. Two particle types were used in the simulations and in the experiments: zirconia (median diameter 69 µm, skeletal density 3830 kg/m³) and steel (290 µm, 7790 kg/m³). Ten different cases, with different solids loading values, were investigated: three with pure zirconia particles, three with pure steel particles, and four with a mixture of zirconia (28%) and steel (72%). Several different drag models were tried out in the simulations. However, none of the available models were able to predict the classification efficiency observed in experiments with their default settings. Although most of the drag models correctly predicted the inversely proportional behavior of the classification efficiency vs. solids loading, the classification efficiency was overpredicted. It was observed that a combined WenYu/Ergun drag model gave a wide range of accuracy, by being able to capture the behavior of both dense and dilute particle systems. Even though the predictions of the classification efficiency for steel particles were acceptable, a larger deviation was observed with Geldart A zirconia particles. CFD simulations with the WenYu and Ergun combined drag model were used for further validation against the experimental observations. In this case, previously published experimental data for fluidization of pure Zirconia particles were used. The fluidization of zirconia was modelled in Barracuda VR^® with adjustment of the combined WenYu/Ergun drag model parameter (k₁), to obtain a suitable validation. Furthermore, the effect of adding the blended acceleration model (BAM) for the fluidization simulations is discussed. It was observed that the fixed bed pressure drop was very accurate compared to the experimental observation, but the pressure drop after the fluidization was slightly overpredicted.