Effective viscosity prediction of crude oil-water mixtures with high water fraction

Wen, Jiangbo; Zhang, Jinjun; Wei, Min

doi:10.1016/j.petrol.2016.09.052

Cited by 42 publications

(24 citation statements)

References 20 publications

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“…In addition to being small, the impact of emulsifier content appears irregular and is hard to predict. Actually, the emulsifier content will affect the degree of dispersion of oil and water in mud, and its effect varies to muds with different OWR . In general, it is a very complex process.…”

Section: Resultsmentioning

confidence: 99%

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Investigation of methane/drilling mud phase behavior and its influence to hydrocarbon drilling activity

Feng

Chen

et al. 2019

Energy Science & Engineering

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Knowledge of methane dissolution behavior in drilling mud is of major importance to early kick detection and well control operation in oil and gas drilling industry. In this work, methane solubility experiments in water‐based and oil‐based muds were conducted over wide ranges of temperature and pressure conditions. The influences of mud compositions and methane phases were investigated accordingly. It is found that methane solubility in water‐based mud is minimal and can be ignored. The data in oil‐based mud were analyzed using different equation of state and mixing rules. Our results show that the combination of our novel binary interaction parameter equations, Peng‐Robinson equation, and van der Waals2 mixing rule provides a very good representation of our data as well as those available in the literature. With the proposed model, the annulus methane solubility profile of a deepwater well in South China Sea was predicted, which provides a better understanding of the annulus flow behavior and further improve the safety of drilling operation.

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Section: Resultsmentioning

confidence: 99%

“…Actually, the emulsifier content will affect the degree of dispersion of oil and water in mud, and its effect varies to muds with different OWR. 28 In general, it is a very complex process. Based on the experimental results, the influence of emulsifier content will be ignored in this study.…”

Section: Effects Of Mud Compositionsmentioning

confidence: 99%

Investigation of methane/drilling mud phase behavior and its influence to hydrocarbon drilling activity

Feng

Chen

et al. 2019

Energy Science & Engineering

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“…Oil viscosity is a measure for the internal friction factor of the fluid flow (Zhang et al, 2017). The accurate value of viscosity is needed in various analyses in petroleum engineering including studying the fluid properties (Ilieva et al, 2016;Kleinhans et al, 2016), fluid flow (Abubakar et al, 2015;Al-Sarkhi et al, 2016;Norouzi et al, 2017;Zhang et al, 2017), mixing properties (Wen et al, 2016) and asphaltene precipitation (Ilyin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modeling viscosity of crude oil using k-nearest neighbor algorithm

Mahdiani

Khamehchi

Hajirezaie

et al. 2020

Adv. Geo-Energy Res.

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Oil viscosity is an important factor in every project of the petroleum industry. These processes can range from gas injection to oil reservoirs to comprehensive reservoir simulation studies. Different experimental approaches have been proposed for measuring oil viscosity. However, these methods are often time taking, cumbersome and at some physical conditions, impossible. Therefore, development of predictive models for estimating this parameter is crucial. In this study, three new machine learning based models are developed to estimate the oil viscosity. These approaches are genetic programing, k-nearest neighbor (KNN) and linear discriminant analysis. Oil gravity and temperature were the input parameters of the models. Various graphical and statistical error analyses were used to measure the performance of the developed models. Also, comparison study between the developed models and the well-known previously published models was conducted. Moreover, trend analysis was performed to compare the predictions of the models with the trend of experimental data. The results indicated that the developed models outperform all of the previously published models by showing negligible prediction errors. Among the developed models, the KNN model has the highest accuracy by showing an overall mean absolute error of 8.54%. The results show that the new developed models in this study can be potentially utilized in reservoir simulation packages of the petroleum industry.

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“…Their main advantage over the other ones lies in the ability to carry out continuous measurements in the ease of their automation. However, the existing types of rotational viscometers have a limited lower value of the measured viscosity and a high measurement error due to the inability to take into account the flow of the fluid at the end of the movable cylinder [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Method and instruments to measure dynamic viscosity of oil products in pipeline

Belogusev

Kozlov

Егоров

et al. 2018

Engineering for Rural Development

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Abstract. The purpose of this paper is to develop a new method to determine the dynamic viscosity of oil products in a pipeline. Its main difference in comparison with the existing methods is that the viscosity can be determined directly in the pipeline, which allows excluding the human factor that occurs, when using the existing methods and measuring instruments, and improving the accuracy of measuring the dynamic viscosity of oil products due to taking into account the cavitation processes inside a pipeline. The gist of a new method lies in the fact that the rotating element -an ellipse that has the form of a spindle -is placed inside a pipeline in a horizontal position and set in motion by an asynchronous electric motor. Determination of the dynamic viscosity of oil products is carried out by measuring the moment of fluid resistance against the motion of an asynchronous electric motor: the higher the dynamic viscosity, the greater the moment of resistance is developed when the ellipse rotates. The main criteria for estimating the dynamic viscosity of oil products are the moment of inertia of the rotating masses of the drive mechanism and their angular accelerations. The developed hardware-software complex with a rotation angle sensor allows registering and processing the measured parameters. The experimental data obtained in the course of the study demonstrated sufficient convergence of the dynamic viscosity values obtained by the developed method and by the existing methods. In future, it is planned to obtain the dependences of the moments of resistance of various types of oil products on the angular velocity of an asynchronous electric motor and, based on the results obtained, determine the range of speeds, at which the greatest measurement accuracy is achieved.

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Effective viscosity prediction of crude oil-water mixtures with high water fraction

Cited by 42 publications

References 20 publications

Investigation of methane/drilling mud phase behavior and its influence to hydrocarbon drilling activity

Investigation of methane/drilling mud phase behavior and its influence to hydrocarbon drilling activity

Modeling viscosity of crude oil using k-nearest neighbor algorithm

Method and instruments to measure dynamic viscosity of oil products in pipeline

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