scholarly journals Experimental and Theoretical Determination of Heavy Oil Viscosity Under Reservoir Conditions

2002 ◽  
Author(s):  
Jorge Gabitto ◽  
Maria Barrufet
Keyword(s):  
Heavy Oil ◽  
Oil Viscosity ◽  
Theoretical Determination ◽  
Reservoir Conditions

scholarly journals The Research and Application of Microbial Degradation Technology on Heavy Oil Reservoir in Huabei Oilfield

2018 ◽  
Vol 38 ◽  
pp. 01054
Author(s):  
Guan Wang ◽  
Rui Wang ◽  
Yaxiu Fu ◽  
Lisha Duan ◽  
Xizhi Yuan ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Heavy Oil ◽  
High Surface ◽  
Effective Rate ◽  
Water Flooding ◽  
Oil Reservoir ◽  
Displacement Efficiency ◽  
Oil Viscosity ◽  
Reservoir Conditions ◽  
Heavy Oil Reservoir

Mengulin sandstone reservoir in Huabei oilfield is low- temperature heavy oil reservoir. Recently, it is at later stage of waterflooding development. The producing degree of water flooding is poor, and it is difficult to keep yield stable. To improve oilfield development effect, according to the characteristics of reservoir geology, microbial enhanced oil recovery to improve oil displacement efficiency is researched. 2 microbial strains suitable for the reservoir conditions were screened indoor. The growth characteristics of strains, compatibility and function mechanism with crude oil were studied. Results show that the screened strains have very strong ability to utilize petroleum hydrocarbon to grow and metabolize, can achieve the purpose of reducing oil viscosity, and can also produce biological molecules with high surface activity to reduce the oil-water interfacial tension. 9 oil wells had been chosen to carry on the pilot test of microbial stimulation, of which 7 wells became effective with better experiment results. The measures effective rate is 77.8%, the increased oil is 1,093.5 tons and the valid is up to 190 days.

Experimental and Theoretical Determination of Equilibrium Interfacial Tension for the Solvent(s)–CO2–Heavy Oil Systems

2012 ◽  
Vol 26 (3) ◽  
pp. 1776-1786 ◽  
Author(s):  
Huazhou Li ◽  
Daoyong Yang ◽  
Paitoon Tontiwachwuthikul
Keyword(s):  
Interfacial Tension ◽  
Heavy Oil ◽  
Theoretical Determination

A Consistent and Accurate Dead-Oil-Viscosity Method

2009 ◽  
Vol 12 (06) ◽  
pp. 815-840 ◽  
Author(s):  
David F. Bergman ◽  
Robert P. Sutton
Keyword(s):  
Pressure Drop ◽  
Absolute Error ◽  
Process Conditions ◽  
Oil Viscosity ◽  
Characterization Factor ◽  
University Of Oklahoma ◽  
Fold Reduction ◽  
Reservoir Conditions ◽  
Absolute Viscosity

Summary The calculation of pressure drop resulting from the flow of oil through porous media or pipes requires the evaluation of viscosity. This is the single most important transport property necessary to calculate pressure drop accurately. The basis for oil-viscosity calculations using a traditional black-oil approach is the determination of dead- or gas-free-oil viscosity. A total of 23 dead-oil-viscosity calculation methods have been identified from the literature and evaluated in this paper. A large database consisting of data from conventional pressure/volume/temperature (PVT) reports, crude-oil assays, and the literature was compiled from more than 3,000 samples from around the world. The number of actual viscosity measurements exceeded 9,800. An evaluation of the correlations yielded unacceptable results largely because of the failure of the methods to properly account for the physics of the problem. In general, this results from the methods' failure to properly account for the change in viscosity with temperature and to address the chemical nature of the oil. A significant improvement in results can be realized through the use of the Watson characterization factor in addition to oil API gravity and temperature in the correlation of viscosity. This work has identified the character of the crude to have a significant effect on oil viscosity, especially for oils with gravities less than 25°API. Methods have been proposed in the literature that use the Watson characterization factor; however, these have been largely ignored in the upstream oil industry. Therefore, a new method has been developed that shows significant improvement over existing methods. At reservoir conditions, a 2- to 13-fold reduction in average absolute error was noted when compared with the error observed from traditional methods. At surface process conditions, this improvement ranged 3- to 60-fold. In addition, an updated correlation for Watson characterization has been developed. The ASTM density correction for varying temperature has been examined. Revised coefficients were developed that enhance the method's accuracy for both oils and pure components and provide a suitable means to convert kinematic viscosity to absolute viscosity. The pdf file includes an associated discussion by Faruk Civan, SPE, University of Oklahoma, and the authors' reply.

scholarly journals INVESTIGATION OF RHEOLOGICAL PROPERTIES OF HEAVY OIL DEPOSITS

2020 ◽  
Author(s):  
Sudad H Al-Obaidi ◽  
Smirnov VI ◽  
Kamensky IP
Keyword(s):  
Rheological Properties ◽  
Heavy Oil ◽  
High Viscosity ◽  
Oil Field ◽  
Natural Mode ◽  
Heavy Oils ◽  
Oil Viscosity ◽  
Temperature Range ◽  
Reservoir Conditions ◽  
Oil Deposits

High viscosity of heavy oils at reservoir conditions is one of the main causes of the low production rates of producing wells, and sometimes even their complete absence when trying to develop a field on a natural mode. The rheological properties of heavy oil deposits in a wide temperature range were studied in this work. Special attention was paid to the study of viscous and elastic components of oil viscosity as a function of temperature to justify the optimal conditions for the development of heavy oil fields. Heavy oil samples collected from Pechersky oil field (Russia) were used in this research. Dynamic viscosity tests were carried out on the heavy oil of this field. It was noticed that high values of viscous and elastic components of oil viscosity were observed over the entire temperature range. It has also been remarked that the values of oil viscosity components are inversely proportional to the temperature increase.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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