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BR 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.
Month: November 2024
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.
Event will Bring Together Experts and Professionals to Discuss Innovations and Challenges in Reservoir Management and Energy Technologies
On November 11 and 12, the Energy Production Innovation Center (EPIC) at Unicamp will host the 6th EPIC Conference, an event for researchers, academics, and professionals in the energy industry. The meeting will take place at Instituto Eldorado in Campinas (SP) and will focus on presenting the results of the center's new phase of Research and Development (R&D), as well as discussing technological advancements and key challenges faced by the energy sector.
Find out more at: https://cenarioenergia.com.br/2024/10/02/6a-epic-conference-apresentara-avancos-em-pd-no-setor-de-energia/
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.