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Month: January 2026
Experimental study of electrical submersible pump performance under water-in-oil emulsion flow
Electrical Submersible Pump (ESP) systems are currently one of the best artificial lift methods in terms of production rate. The presence of complex fluid systems such as stabilized emulsions difficult its performance, causing the increase in required power and operational instabilities. Both the emulsions stability and the catastrophic phase inversion (CPI) are related to the droplet size distribution. This work aims to investigate how the droplet size distribution of water-in-oil flow within ESP systems is affected by rotational speed and flow rate. An ESP flow loop was designed to test emulsion flow with several rotational speeds and a range of flow rates that covers almost the entire performance curve. The droplet size distribution was evaluated by the Focused Beam Reflectance Measurement (FBRM) technic. The Sauter mean diameter shown a decreasing trend as the dimensionless flow rate increases.
Wax Deposition Experiment Under Cold Flow: A Transient Analysis
“Cold flow” refers to the pipeline flow of a “waxy” crude oil at a temperature, which is below its wax appearance temperature (WAT) and above its pour point temperature (PPT), whereby precipitated wax crystals remain suspended in the flowing crude oil. It has been suggested as an alternative technology for decreasing solids deposition (Merino-Garcia, D.; Correra, S. Pet. Sci. Technol. 2008, 26, 446). An experimental investigation was undertaken to study solids deposition under cold flow in a flow-loop apparatus, incorporating a small double-pipe heat exchanger. The experiments were performed using 3 and 6 mass % mixtures of a petroleum wax dissolved in Norpar13 (a paraffinic solvent comprising C9−C16) at different wax−solvent mixture temperatures, Th, and two flow rates over a deposition time of 1 h. Two sets of deposition experiments were performed: cold flow with {WAT ≥ Th > PPT} and “hot flow” with {Th > WAT}. The deposit mass decreased with a decrease in wax concentration and with an increase in the coolant temperature. However, the deposit mass decreased with a decrease in the mixture temperature, under cold flow, but it increased with a decrease in the mixture temperature, under hot flow. Also, the deposit mass, under cold flow, was not affected by flow rate. Predictions from a pseudosteady-state heat-transfer model were in good agreement with experimental results, indicating the deposition process to be thermally driven. The liquid−deposit interface temperature in all cases was equal to the WAT of the liquid phase. Variations in both the wax content and the carbon number distribution in deposit samples are discussed.
Chemical Characteristics of Asphaltenes: An Insight into Aggregation and Precipitation BehaviorClick to copy article link
Asphaltene-related issues, such as emulsion stability and solid deposition in long tie-backs, represent major flow assurance challenges in the petroleum industry. This study compares the aggregation behavior and precipitation tendencies of two structurally distinct asphaltenes─one extracted from heavy crude oil (AH) and the other from light crude oil (AL)─through an integrated analysis combining LUMiSizer optical centrifugation, DLS, CHN, XPS, FTIR, and NMR. Dynamic light scattering (DLS) and LUMiSizer optical centrifugation were performed under varying n-heptane concentrations (0–40 vol %), revealing that both aggregate size and sedimentation index increase with dilution, indicating reduced dispersion stability. Importantly, a key new insight revealed is that the destabilization kinetics are strongly controlled by the molecular architecture. Complementary CHN, XPS, FTIR, and NMR analyses showed that asphaltene AL exhibited larger and more polydisperse aggregates (up to 4 μm) and higher sedimentation indices, linked to its higher aromaticity, greater heteroatom-related polarity, and shorter alkyl chains, which facilitate π–π stacking and reduced steric stabilization. In contrast, AH showed smaller aggregates and slower destabilization, consistent with more hydrogen-rich and less condensed structural motifs. These results establish a structure–kinetics relationship where specific molecular features predict aggregation behavior. The findings enable more targeted stability screening and support the mechanistic development of models and additives for preventing asphaltene deposition and emulsion stability.
TR-PIV and CNN-based analysis of liquid–liquid two-phase flow in a centrifugal pump impeller
Centrifugal pumps are widely used in engineering applications, consuming a considerable amount of energy across the globe. However, in many cases they operate under off-design conditions, such as multiphase flows, implying in an even higher energy consumption. A prominent example is the mixture of water and viscous oil, where the phases may exhibit a dispersed flow pattern depending on superficial velocities. Understanding the flow dynamics within the impeller during two-phase liquid–liquid operation is crucial for grasping the mechanisms underlying energy dissipation and pump performance. In this work, we investigate experimentally oil–water flows in dispersed regime within a centrifugal pump impeller, and propose a framework for automatically identifying the dispersed phase and measuring the velocity field of the continuous phase. For that, we carried out time-resolved particle image velocimetry (TR-PIV) in a transparent pump operating under two-phase flow conditions. An image processing technique based on deep learning was developed to dynamically mask oil droplets (dispersed phase) and distinguish them from water-seeded particles (continuous phase) in the raw TR-PIV data. Additionally, a method to evaluate the phase-ensemble average velocity was designed and implemented. The results revealed that neglecting dynamic masking in the TR-PIV images caused an inversion of velocity values between the pressure and suction blades, driven by the accumulation of oil droplets in recirculation zones near the suction blade. This result highlights the importance of accurately tracking the dispersed phase. Our findings indicate higher turbulent kinetic energy (TKE) values at lower flow rates when the dispersed phase consists of larger oil droplets. These findings expand our understanding of multiphase flows in centrifugal pumps, which can be proven useful for validating numerical simulations, proposing new mathematical models, and contributing to the design of improved and energy-saving impellers.
Time-resolved PIV measurements of an unsteady viscous oil flow in a centrifugal pump
Centrifugal pumps are essential for many human activities, accounting for a considerable portion of the global electricity consumption. However, despite decades of study, the flow within the pump’s impeller and its effects on the performance are far from being fully understood, particularly when the flow involves fluids more viscous than water. In this context, this paper reports experiments using time-resolved particle image velocimetry (TR-PIV) for investigating the flow of a 14-cP-viscosity mineral oil in a transparent pump with radial impeller. We found that: (i) at low flow rates, the positions of vortices depend on the fluid properties; (ii) at higher flow rates, the oil flows aligned in the radial direction, while the water flows following closely the blade curvature; (iii) the velocity profiles for the oil are approximately parabolic, whereas those for water are flatter; (iv) the average deflection angle of the velocity vectors relative to the blade curvature changes significantly with viscosity; (v) contrary to common expectation, the turbulent kinetic energy is up to four times higher for oil than for water; (vi) vortices are periodically formed and dissipated with a frequency proportional to the rotational speed. Our results provide new insights into the flow of viscous fluids in pumps, with valuable information for their design and installation.
Petrographic image classification of complex carbonate rocks from the Brazilian pre-salt using convolutional neural networks
Machine learning (ML) algorithms have been widely applied across geosciences for tasks such as data conditioning, resolution enhancement, and image classification. The use of ML enables the analysis of large datasets, the identification of complex patterns, and can save time and reduce costs compared to conventional approaches. Among these techniques, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification in various geoscientific applications. In the context of the carbonate reservoirs of the Brazilian Pre-salt, the sedimentological complexity of these deposits, combined with the vast amounts of data produced, drives the need for automated image classification approaches. Although several recent studies have explored ML methods for petrographic image analysis in diverse geological settings, few have focused specifically on the complex carbonates of the Brazilian Pre-salt reservoirs. In this study, we present a fully automated and modular machine learning workflow for petrographic image classification of thin sections from the Aptian Barra Velha Formation, Santos Basin, Brazil. Our approach includes the direct integration of paired plane-polarized light (PPL) and cross-polarized light (XPL) images as raw inputs to deep learning models, allowing for a more comprehensive representation of petrographic features. Additionally, we implement a hierarchical classification scheme, based on facies upscaling, encompassing three levels of classification granularity: a simplified scheme with 5 classes, an intermediate with 9 classes, and a complete scheme with 23 classes, a dimension not systematically explored in previous studies. Our dataset comprises 800 thin sections, corresponding to 1,600 high-resolution scanned images (6,400 dpi), from six wells across three different oilfields, strategically selected to ensure representativeness across distinct structural domains of the reservoir. We evaluated five computational models: EfficientNet, MobileNet v3, RegNet, ResNet, and ShuffleNet v2. The models MobileNet v3 large, RegNet x 800mf, and RegNet y 400mf achieved the highest F1-scores for the simplified (0.795), intermediate (0.768), and complete classifications (0.528), respectively. Notably, the intermediate classification with nine classes offered the best balance between detail and accuracy. This work presents a promising approach for automatic petrographic image pre-classification, favoring efficient database organization in the challenging exploratory settings of the Brazilian Pre-Salt.
Interfacial Tension as a Parameter to Assess Demulsifier Efficiency on Heavy Crude Oil Emulsions
The formation of water-in-crude oil (w/o) emulsions during the lifting and pipelining of crude oils is a common issue in petroleum production. In oilfields, emulsions are undesirable due to the increase of fluid viscosity, which consequently drops the production rate. Demulsifiers may be injected at electrical submersible pumps, production lines, and/or the crude oil processing station to deal with the impacts. In a laboratory, emulsion stability and demulsifier efficiency are commonly evaluated by bottle tests, but this approach does not consider the main factors controlling the kinetic stability of water–oil emulsions such as interfacial tension. This study aims to evaluate how the interfacial tension can influence the stability of these w/o emulsions, seeking a microscopic understanding of their phase separation as a function of temperature and demulsifier concentration. The Central Composite Rotatable Design (CCRD) methodology was used to evaluate the effects of the independent variables (temperature and demulsifier concentration) on the interfacial tension and emulsion stability at 50% water-cut. The interfacial tension was determined by a Spinning Drop Tensiometer, and the emulsion stability was determined by measuring the phase separation under a centrifugal field rather than the classical bottle test. We found an intrinsic inverse correlation between the yield of phase separation and interfacial tension, suggesting interfacial tension is an important parameter to assess the demulsifier’s effectiveness and the emulsion’s stability. Our studies surface important new findings for understanding the stability of emulsions for complex and more realistic crude oil–water systems with commercial demulsifiers.
Integrated Multi-Scale Pore Characterization of Carbonate Rocks in the Barra Velha Formation, Santos Basin, Brazil
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