Oil Volume Research Articles

Strategy of directional oil transport for splash lubrication systems

Supplying oil to bearings rapidly over long distances presents a challenge for splash lubrication systems in drive axle main reducers (DAMRs), mainly because of inadequate driving forces. To mitigate this issue, a novel strategy was introduced to control the splash oil flow via a shroud, which enabled directional oil transport. We developed a transient modeling framework to comprehensively investigate the temporal and spatial evolution mechanisms of splash oil. This framework, validated through empirical studies, navigates the uncertainties and multiscale properties of the multifaceted physics induced by the dynamic boundaries of a shrouded gear. Several operating scenarios were simulated to investigate the driving mechanism of directional oil transport within the shroud, as well as the effect of shroud design and rotational speed on splash lubrication performance. Results demonstrate that the pressure gradients inside and outside the shroud govern the directional transport of the splash oil flow. The bearings located distally on the sump receive a considerable volume of oil (maximum flow rate exceeding 11 L/min) from the upper passage. Concurrently, the churning losses in the DAMR are reduced by >30 % when the wheel is shrouded in comparison to an unshrouded scenario. Moreover, interactions involving friction and collision between the gear and fluid particles intensify with increasing speed and enhance shroud sealing. This study provides a valuable mechanism for investigating oil transport in splash-lubricated DAMRs.

Numerical and experimental study of a multiphase flow inside a self-contained drum motor drive system

The multiphase Taylor-Couette flow of air and oil enclosed within the annular space between two concentric cylinders has been studied experimentally and numerically. The outer cylinder rotates at a constant speed and exchanges heat by convection and radiation with ambient air. The inner cylinder is stationary and subjected to a constant uniform heat flux. Such flow can be found in a self-contained drum motor drive system (SCDMDS). The heat flux imposed on the inner cylinder mimics the amount of heat generated due to several thermal losses within the SCDMDS. A layer of lagging material is sometimes added to the outer cylinder to enhance belt traction.The effect of the inner-to-outer cylinder radius ratio (RR), Reynolds number (Re), oil volume (OV), outer cylinder surface emissivity (ε), and lagging material thickness (δ) on the flow and heat transfer between the two cylinders and the ambient has been investigated considering a quasi-steady state analysis. The current investigation considered the following ranges found in a typical SCDMDS: RR = 0.35–0.85, Re = 200–3000, OV = 50–100 %, ε = 0–0.95, and δ = 0–100 mm. The numerical part of the present study has been carried out using ANSYS-CFX software package. The numerical results have been validated using experimental data. Resultsindicated that increasing RR, Re and ε enhanced the heat transfer within the SCDMDS. The rate of heat transfer within the annular space reached its highest value at OV = 65 % above which the rate of heat transfer is deteriorated. Because the lagging material is acting as an insulation over the outer cylinder, a critical thickness of δ = 35 mm resulted in the minimum overall resistance above which the rate of heat transfer is deteriorated.The parametric study revealed that the best overall thermal performance was attained at RR = 0.85, Reynolds number = 3000, OV = 65 %, ε = 0.95 and δ = 35 mm. In this case, the heat transfer coefficient between the inner cylinder and the oil reached its highest value of 410 W/m2 °C, giving the lowest possible inner cylinder temperature. A correlation of Nusselt number in terms of the geometrical and operating parameters of a SCDMDS considered in the current study has been developed and validated. The developed correlation gave a maximum deviation of ±8 %.

Clarifying the murk: unveiling bacterial dynamics in response to crude oil pollution, Corexit-dispersant, and natural sunlight in the Gulf of Mexico

The 2010 Deepwater Horizon (DwH) Oil spill released an enormous volume of oil into the Gulf of Mexico (GoM), prompting the widespread use of chemical dispersants like Corexit® EC9500A. The ecological consequences of this treatment, especially when combined with natural factors such as sunlight, remain unexplored in the context of marine bacterial communities’ dynamics. To address this knowledge gap, our study employed a unique metaproteomic approach, investigating the combined effects of sunlight, crude Macondo surrogate oil, and Corexit on GoM microbiome across different mesocosms. Exposure to oil and/or Corexit caused a marked change in community composition, with a decrease in taxonomic diversity relative to controls in only 24 hours. Hydrocarbon (HC) degraders, particularly those more tolerant to Corexit and phototoxic properties of crude oil and/or Corexit, proliferated at the expense of more sensitive taxa. Solar radiation exacerbated these effects in most taxa. We demonstrated that sunlight increased the dispersant’s toxicity, impacting on community structure and functioning. These functional changes were primarily directed by oxidative stress with upregulated proteins and enzymes involved in protein turnover, general stress response, DNA replication and repair, chromosome condensation, and cell division. These factors were more abundant in chemically treated conditions, especially in the presence of Corexit compared to controls. Oil treatment significantly enhanced the relative abundance of Alteromonas, an oil-degrading Gammaproteobacteria. In combined oil-Corexit treatments, the majority of identified protein functions were assigned to Alteromonas, with strongly expressed proteins involved in membrane transport, motility, carbon and amino acid metabolism and cellular defense mechanisms. Marinomonas, one of the most active genera in dark conditions, was absent from the light treatment. Numerous metabolic pathways and HC-degrading genes provided insights into bacterial community adaptation to oil spills. Key enzymes of the glyoxylate bypass, enriched in contaminant-containing treatments, were predominantly associated with Rhodobacterales and Alteromonadales. Several proteins related to outer membrane transport, photosynthesis, and nutrient metabolisms were characterized, allowing predictions of the various treatments on biogeochemical cycles. The study also presents novel perspectives for future oil spill clean-up processes.

Demonstration of magnetic resonance Z-spectral imaging for fatty acid characterization of bone marrow at 3 T.

Magnetic resonance Z-spectral imaging (ZSI) has emerged as a new approach to measure fat fraction (FF). However, its feasibility for fat spectral imaging remains to be elucidated. In this study, a single-slice ZSI sequence dedicated to fat spectral imaging was designed, and its capability for fatty acid characterization was investigated on peanut oil samples, a multiple-vial fat-water phantom with varied oil volumes, and vertebral body marrow in healthy volunteers and osteoporosis patients at 3 T. The peanut oil spectrum was also recorded with a 400-MHz NMR spectrometer. A Gaussian-Lorentzian sum model was used to resolve water and six fat signals of the pure oil sample or four fat signals of the fat-water phantom or vertebral bone marrow from Z spectra. Fat peak amplitudes were normalized to the total peak amplitude of water and all fat signals. Normalized fat peak amplitudes and FF were quantified and compared among vials of the fat-water phantom or between healthy volunteers and osteoporosis patients. An unpaired student's t-test and Pearson's correlation were conducted, with pless than 0.05 considered statistically significant. The results showed that the peanut oil spectra measured with the ZSI technique were in line with respective NMR spectra, with amplitudes of the six fat signal peaks significantly correlated between the two methods (y = x + 0.001, r = 0.996, p< 0.001 under a repetition time of 1.6 s; and y = 1.026x - 0.003, r = 0.996, p< 0.001 under a repetition time of 3.1 s). Moreover, ZSI-measured FF exhibited a significant correlation with prepared oil volumes (y = 0.876x + 1.290, r = 0.996, p< 0.001). The osteoporosis patients showed significantly higher normalized fat peak amplitudes and FF in the L4 vertebral body marrow than the healthy volunteers (all p< 0.01). In summary, the designed ZSI sequence is feasible for fatty acid characterization, and has the potential to facilitate the diagnosis and evaluation of diseases associated with fat alterations at 3 T.

The Effect of Crude Oil Stripped by Surfactant Action and Fluid Free Motion Characteristics in Porous Medium.

The surfactant solution is crucial in facilitating the spontaneous imbibition process for the recovery of oil in tight reservoirs. Further investigation is required to examine the fluid flow in porous mediums and the process of crude oil stripping by a surfactant solution during spontaneous imbibition. Hence, this study aims to determine the free motion properties of oil and water in porous mediums using the finite-element approach to solve the multiphase flow differential equation, taking into account the capillary pressure. An investigation was conducted to examine the impact of oil viscosity and interfacial tension on the mean liquid flow rate and oil volume fraction. An experimental study was conducted to investigate the impact of surface tension, interfacial tension, and wetting angle on crude-oil-stripping efficiency. The findings indicate that the stripped crude oil migrated through porous mediums as individual oil droplets, exhibiting a degree of stochasticity in its motion. When the interfacial tension is reduced, the average velocity of the fluid in the system decreases. The crude oil exhibited a low viscosity, high flow capacity, and a high average flow rate within the system. Once the concentration of the surfactant solution surpasses a specific threshold, it binds with the oil to create colloidal aggregates, resulting in the formation of micelles and influencing the efficiency of the stripping process. As the temperature rises, the oil-stripping efficiency also increases. Simultaneously, an optimal range of wetting angle, surface tension, and interfacial tension could enhance the effectiveness of removing oil using surfactant solutions. The research results of this paper enrich the enhanced oil recovery mechanism of surfactant and are of great significance to the development of tight reservoirs.

Developing biopolymer-stabilized emulsions for improved stability and bioaccessibility of lutein

Lutein is essential for infant visual and cognitive development but has low stability and solubility. This study aimed to enhance the stability and bioaccessibility of lutein using oil-in-water emulsions stabilized with biopolymers. Commercially available octenylsuccinylated (OS) starches, including capsule TA® (CTA), HI-CAP®100 (HC), and Purity Gum® 2000 (PG), along with gum Arabic (GA) variants Ticaloid acacia Max® (TAM), TICAmulsion® 3020 (TM), and pre-hydrate gum Arabic (PHGA), were chosen as emulsifiers. By screening the effect of biopolymer concentration and oil volume fraction (Φ), emulsions stabilized with CTA, HC, or TM at 20% and 30% (w/v) concentration and 70% Φ exhibited a gel-like structure and were selected for further assessments. After a week at 25 °C, emulsions stabilized by CTA and HC showed no significant change in droplet size, while TM emulsion exhibited a 1.58-fold increase. At 45 °C, all emulsions exhibited increase in droplet size. Lutein retention is higher in CTA emulsions at both storage temperatures than free lutein. In vitro bioaccessibility of all lutein emulsions was higher than that of free lutein. These findings highlight the superior stability and bioaccessibility of the lutein emulsion stabilized by OS starch, positioning it as a promising carrier to broaden lutein applications in infant foods.

Methods to Measure Viscosity of Tiny Volumes of Oil With Blotter Paper

This article describes a method for measuring the viscosity of oils that behave as Newtonian at low shear rates, by using only 80 μl of oil. This method is particularly useful when only such small quantities of oil are available—for example, when oil is extracted from grease taken from bearings. The method consists in placing an oil drop on blotter paper where an oil stain will grow in time. The rate of growth is then a measure of viscosity. Master curves were generated expressing the relation between viscosity and oil stain growth, both at a given time and as a function of time, each with its own accuracy. The viscosity measurements with this method were compared with those obtained in a conventional viscometer using poly-alpha-olefin (PAO), ester, and mineral oils.

Prediction of essential oil content in C. sintoc Leaves based on the direction of vegetation slope in Mount Ciremai National Park using ANFIS Artificial Neural Network.

C. sintoc is a plant that has a high essential oil content. Essential oils have many health benefits. Mount Ciremai National Park is an area that has abundant vegetation, especially C. sintoc. The purpose of this study was to predict the volume of oil contained in the leaves of C. sintoc based on its growing location in Mount Ciremai National Park (TNGC), West Java. Sampling was carried out in two stages, namely field samples and laboratory samples. Field samples with a single plot measuring 20 x 20 meters. Extraction of laboratory samples by steam distillation method. Data analysis using ANFIS method to predict the volume of essential oil. The results showed that the volume of essential oil in the leaves was largest on the western slope (1,96 ml), northern slope (2,01 ml), eastern slope (1,55 ml) and southern slope (1,37 ml) while the essential oil yield of extract oil in the leaves is found on the western slope (0,08%), northern slope (0,07 %), eastern slope (0,06 %) and southern slope (0,04), On the western slope (1,82 ml), northern slope (1,73 ml). The ANFIS analysis results showed a prediction accuracy of 90,99% with the highest tree productivity when C. sintoc BL grows at an altitude of 650-700 meters above sea level, tree diameter of 42 cm, height of 12 m, growing on sand-textured land and at a humidity of 75 and at a growing temperature range of 25-270C. The value of essential oil production volume is influenced by each variable such as tree diameter, tree height, soil texture, altitude, temperature and humidity.

Mixed oil control and solution of mountain product oil pipeline

At present, the mixing oil will be produced in the sequential transportation of various refined oil inescapable. The mixing oil is not qualified product which is needed to be dealt with. The Volume of mixing oil is less the better. How to reduce the volume of mixing oil in the transportation of product oil is the key to reduce the cost and improve pipeline efficiency. This paper discusses the cause and mechanism of mixed oil in refined products and the characteristics of mixed oil in mountain pipelines. Then, it is providing a calculation method of mixed oil quantity, and a detecting method of mixed oil section, and a treatment method after receiving mixed oil.

Results of Quantitative Estimation of Unconventional Oil Resources of the Russian Federation

Abstract —The estimation results of the initial total unconventional resources of hydrocarbon in Domanik type sediments, the Khadum and Bazhenov formations are provided. The reasonability of estimating hydrocarbon resources in these deposits by the volumetric method is substantiated. The stratigraphic interval of their distribution is given. The technique of oil and gas geological zoning based on the mapping of structural and facies zones, the degree of catagenetic transformation of organic matter, and the boundary values of the thickness of carbonaceous rocks in the section is described. The criteria for the allocation of pay intervals that make up the effective part of the section are determined. The choice of parameters such as porosity coefficient, oil recovery coefficient, oil saturation coefficient, correction factor and oil density is justified. For each complex of deposits, adjustments related to the features of the geological structure of the estimation targets, the conditions of their occurrence and the degree of geological and geophysical study have been introduced into the estimation of total geological resources. The volumes of total geological and recoverable resources of unconventional hydrocarbon sources for the Volga– Ural, Timan–Pechora, Black Sea–North Caucasus, West Siberian and Lena–Tunguska oil and gas provinces are given. The conducted research has rationalized extensive resources, the recovery of which can compensate for the decrease in oil volumes from traditional deposits.

The Influence of Clove Leaf Size Variations on the Characteristics of Clove Leaf Essential Oil Production

Clove leaves are waste products from clove plantations in Kulon Progo, Yogyakarta. Most clove utilization is limited to the buds, while clove leaves have the potential to be a source of essential oil. Essential oil from clove leaves can be extracted using steam-water distillation. One factor influencing the characteristics of clove leaf essential oil production is the variation in material size. In this study, essential oil extraction was conducted using the steam-water distillation method with different clove leaf sizes, namely 2 cm, 3 cm, 4 cm, and 5 cm. The differences in clove leaf size were intended to determine the most suitable leaf size for extraction using the steam-water distillation method. This study showed that a clove leaf size of 2 cm produced a volume and yield of clove leaf oil of 53.5 ml and 5.45%, respectively. The density value of clove leaf oil obtained from this study was 1.019 grams/mL. The clove leaf oil produced was clear yellowish and had the characteristic aroma of clove oil. These criteria meet SNI 06-2378-2006 for clove leaf oil.

Capillary flow of two immiscible liquids initially separated by a Taylor gas bubble

We investigate the capillary displacement of silicone oil columns by water in tandem with a Taylor gas bubble in horizontally aligned tubes that initially separate the two immiscible liquids. The experiments show that the gas slug's motion forces the drainage of the prefilling liquid through a thin film deposited on the tube's wall. The displaced oil is then recovered at the advancing water meniscus. The dynamics of the capillary displacement and drainage are studied as functions of the silicone oil and gas bubble volumes. We find that the Taylor bubble's length has a small influence on the capillary displacement and drainage of the prefilling liquid. The study presents an adapted model for the capillary drainage and oil recovery using Taylor's law for the deposited film thickness on the tube's wall, which collapses all experimental data onto a single curve.

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band.

The accurate monitoring of oil spills is crucial for effective oil spill recovery, volume determination, and cleanup. Oil slicks become emulsified under the effects of wind and waves, which increases the consistency of the oil spills. This phenomenon makes oil spills more challenging to handle and exacerbates environmental pollution. In this study, the variation of the solar-blind ultraviolet (UV) fluorescence spectra obtained from simulated oil spills with different oil types and oil-water ratios was investigated. By designing and constructing a multi-angle excitation and detection system, an apparent fluorescence peak of the oil emulsions was observed at around 290 nm under 220 nm excitation. By utilizing competitive adaptive reweighted sampling (CARS) and multi-output neural network algorithms, both the types and concentrations of the emulsified oils were obtained simultaneously. The classification accuracy for identifying the oil type exceeds 98%, and the mean absolute percentage error (MAPE) for concentration regression is around 2%. The results indicate that active solar-blind UV fluorescence could become a supplementary method for on-site oil spill detection to achieve comprehensive monitoring of oil spills. This study provides potential applications for UV-induced fluorescence spectrometry in oil spill on-site monitoring during the daytime.

«Золотой век» геологии и организация морских геологоразведочных работ в СССР

The features of the organization of geological exploration (GE) in the USSR at government and legislative levels in the “Golden Age of Geology” 1955-1985 are considered. The main achievements of the USSR are noted, including the discovery of the giant West Siberian oil and gas province and a large raw material base for the oil and gas industry on the shelf of the Western Arctic. The graphical dependences of the shares of Russia, the USSR-CIS and the Yamal-Nenets Autonomous District in the world volume of oil and gas production for the period 1900-2022 are presented. The necessity for a radical increase in the volume of GE from state budget funds is substantiated. It is recommended to revive the fund for the reproduction of the country's mineral resource base.

Comparative analysis of solvent extraction of rice bran oil from various sources

This study investigated oil extraction from rice bran a by-product of rice gotten from different sources (Afikpo, Abakaliki, and Emene). The oil was extracted from different particle sizes (150 µm, 300 µm, and 420 µm) of the rice bran using three different solvents namely chloroform, petroleum ether, and n-hexane at constant time of 120 minutes via soxhlet extractor. The physiochemical properties of the extracted oil were boiling point, the saponification values and refractive index were 77 &lt;sup&gt;o&lt;/sup&gt;C, 185.130, and 1.334, respectively. Also the acid value, surface tension, iodine value, and relative density were found to 15.040 g, 6.966×10&lt;sup&gt;2 &lt;/sup&gt;N/M, 99.480, and 0.779, respectively. The obtained result based on source revealed that the maximum oil yield of 35.05 g was achieved from Abakaliki rice bran using chloroform solvent and particle size of 420 mm, whereas Emene and Afikpo gave 32.62 g and 14.18 g of oil, respectively from same solvent and particle size. Again, the highest volume of oil obtained in terms of solvent used were chloroform, petroleum ether, and n-hexane, in descending order. The physicochemical properties investigated, showed that the extracted rice bran oil (RBO) from the above three sources in Nigeria confirms RBO to be of good quality and can be utilized in many food industries after refining in form of additives/or industrial purposes as well as in generation of energy.

Failure prediction in the refinery piping system using machine learning algorithms: classification and comparison

Pipelines play a pivotal role in transporting large volumes of oil and gas within refineries. However, over time, they are susceptible to deterioration, leading to potential failures. Effective monitoring is imperative to maintain their optimal performance and safety. This research introduces a machine learning (ML) approach to pinpoint failure sources in oil and gas pipelines. Analysing an industrial dataset, we compared six ML models to predict failures in refinery pipelines. Leakage sources are predicted based on three operational parameters: transported fluid, temperature, and pressure. The models are evaluated and compared in terms of precision, recall, F1-score, accuracy, and the ROC-AUC. Remarkably, the XGBoost classifier exhibited a 99.7% accuracy, outperforming other algorithms in predicting the failure source. Emphasizing the value of Industry 4.0 solutions, this study underscores the potential of advanced ML in enhancing pipeline monitoring. Such predictions empower operators to pre-empt failures, reinforcing industry safety and sustainability.

Application of Artificial Intelligence to Monitor Leaks from Pumps

During the production of hydrocarbons, offshore platforms frequently leak large volumes of oil and gas. Traditional methods for detecting these leaks are prone to errors, making them inefficient at detecting leaks precisely. These leaks, however, have damaging effects on the environment and humans, posing economic risks to companies as well. This study explores the application of artificial intelligence to monitor pump leaks. The data used in this work was obtained from Kaggle, which contained sensor readings from pumps. Supervised (random forest, support vector machine, and naïve bayes) and unsupervised (isolation forest) machine learning algorithms were employed for leak detection. The results showed that supervised machine learning algorithms were more accurate, with random forest having the greatest F1-score (0.993). Leveraging artificial intelligence for leak monitoring proved effective, offering a promising alternative to traditional methods.

Parameter optimization design of an aero-engine bearing chamber based on active learning Kriging

The air-oil two-phase flow in the aero-engine bearing chamber is highly complex and unsteady. To obtain the mapping relationship between the oil volume fraction in the chamber and the oil inlet flow rate and rotational speed for further design optimization, the air-oil two-phase flows are simulated using the level set method in COMSOL Multiphysics software. To avoid frequent calls to the simulation model during the optimization process, which consumes huge computational costs, the Kriging model is applied to approximate the true response of the computational fluid dynamics (CFD) simulations. The point addition process is assisted by introducing the active learning function EI to enhance the efficiency of constructing the Kriging model. Particle swarm optimization (PSO) is applied to optimize the Kriging-based optimization objective function. With the optimal parameters’ combination, the performance of the oil return in the aero-engine bearing chamber is improved. This study has guiding significance for the lubrication design of the aero-engine bearing chamber.

Impact of water content on minimum miscibility pressure during CO2 flooding in high water-cut reservoirs

This study focused on the X block stratum oil, specifically addressing the application of CO2 displacement technology in enhancing oil recovery under high water content conditions. It systematically investigates the variation of the minimum miscibility pressure (MMP) between CO2 and formation oil at water contents of 0%, 60%, 70%, 80%, and 90%. The research employed slim-tube model simulation experiments and theoretical analysis to explore the impact of different water contents on the physical properties of formation oil and the changes in the interaction mechanism between CO2 and crude oil. The results indicate that the MMP of CO2 flooding is the lowest at 31.11 MPa in the absence of water, reaching the highest value of 37.94 MPa at 90% water content. The MMP of the CO2-formation oil system shows a significant upward trend with increasing water content in the formation, which is related to the stability of the water film, the solubility of CO2, and the interfacial tension between oil and water. Meanwhile, high water content leads to the expansion of formation oil volume, increased viscosity, and compositional changes, collectively influencing the results. This study provides theoretical support for the field application of CO2 flooding and offers critical references for optimizing the oil displacement process.

Reservoir Characterization of Jeribe and Euphrates Formations in Qaiyarah OilField

Three-dimensional static model is a crucial key for a reservoir heterogeneity representation and an initial evaluation of the hydrocarbon in place, thus for a success field development, an accurate reservoir characterization is a fingerprint for the decision making. The initial goal of this study is to generate a static model for Jeribe and Euphrates formations in Qaiyarah oil field to investigate the distribution of the discrete and continuous proprieties such as, permeability and water saturation, in sequence, recognizing the value of the reserve to identity the ability of developing this reservoir as a final decision for future investment. The sever hetrogeneity and complexity of Qaiyarah oil field were the main challenges confronted through the static model building, in consequence, this paper repesents a fingerprint for the future studies in this area. The conceptual methodology in this research includes the integration of the well log data and final well reports to construct the three-dimensional model through log interpretation, sequential simulators, and properties upscaling. Initial volumetric calculations were performed mainly based on the petrophysical properties distribution of eight wells and oil water contact level. Accordingly, the hydrocarbon volume in place were estimated in the two pay reservoirs under study Jeribe and Euphrates formations. The results showed that Euphrates Formation has dominated by the largest portion of reverse (554*106 Sm3), while Jeribe Formation has lower volume of oil initially in place about 194 *106 Sm3.