Proposing a novel and accurate model for forecasting dynamic viscosity of n-alkane in operational conditions

Abstract

The viscosity of fluid is known as resistivity of fluid to flow and straightly affected by temperature and pressure. As it is obvious, the viscosity of reservoir fluid is known as one of the critical parameters which extensively effect on production. Therefore, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel and accurate model to predict dynamic viscosity of normal alkanes in the operational conditions. To this end, 228 dynamic viscosity points as function of carbon number of n-alkane, temperature, and pressure were collected from a reliable paper. The comparison between MLP-ANN outputs and experimental dynamic viscosities is performed in graphical and statistical manners. The calculated coefficients of determination 0.99739 and 0.99051 for training and testing phases express the great ability of MLP-ANN algorithm in prediction of dynamic viscosity of n-alkane. According to the analysis, MLP-ANN has enough accuracy and potential to be used as software for which applicable in petroleum industry.     

Neuro-Fuzzy Modeling for the Prediction of Below-Bubble-Point Viscosity

Abstract

Accurate prediction of reservoir fluid is one of the important factors that needs to be determined due to it usefulness in fluid characterizations, material balance calculations, and general management of reserves. Below-bubble-point viscosity is one of the important variables that has been determined either experimentally or empirically.

This work focuses on the use of neuro-fuzzy techniques to develop a below-bubble-point viscosity model using 1,693 data obtained from different oil fields in the Niger Delta, Nigeria. The data set was randomly divided into three parts with 56.3% used for training, 18.7% for validation, and 25% for testing. The accuracy of the developed model in this study was compared with some published correlations. The statistical analysis results show that the developed model outperformed existing published correlations.     

Effect of Temperature and Solids on the Rheology of Water-in-Oil Emulsions

Abstract

This article reports on an experimental study on the effect of temperature on the viscosity behavior of water-in-oil emulsions with added solids. The experimental conditions consisted of combinations of values of temperature (T) and solids volume fraction (φ _s ). The temperature had the values of 10, 20, 30, 40, 50, and 70°C; whilst the solids volume fraction had values between 0 and 0.025. All the treatments had been chosen such that each value of φ _s was used once in conjunction with each value of T. In order to obtain an estimate of the experimental error in the experiment, it was necessary to replicate the entire set of experiments a total of three times. Statistical analysis of the experimental data pointed to the existence of an interaction of solids and temperature and to the relative viscosity being a function of these two factors. A model based on the data has been developed to evaluate the relative viscosity as a function of T and φ _s for shear rates above 270 s^?1.     

The Viscosity of Nontraditional Lightweight Completion Fluid at Elevated Temperature and Pressure

Abstract

Experimental viscosity values of nontraditional lightweight completion fluid at pressure and temperature ranges of 0.1 MPa to 4.48 MPa, and of 25°C to 100°C, respectively, were reported. To establish the relationship among viscosity, pressure, and temperature, experimental data were fit to the modification of Mehrotra and Svrcek's equation. The result shows that the model could be used to describe the fluid viscosity over a wide range of pressure and temperature. The calculated what is sum of square error and root mean square error are 0.2135 and 0.08892, respectively. It is also shown that the predicted values from the model are in a good agreement with both the experimental values and field data.     

VISCOSITY OF WATER IN OIL EMULSIONS

ABSTRACT

Increase in water cut in oil fields generally calls for an increase in the capacity of transport pipelines. Proper design and operation of the latter requires good knowledge of the thermophysical properties of flow resistance of crude-oil water mixtures. An experimental program aimed at measurements of oil-water emulsion viscosity for water cuts prior to the inversion point was conducted.

The present work reports on measurements of Nimr crude oil-water mixtures viscosity for different water cuts and a typical range of temperatures representative of field conditions (20°-50°C). Three mixing intensities of 10⁶, 5×10⁶ and 15×10⁶ erg/cm-sec generated by a dynamic coalescer and directly relevant to field conditions were used.

The results suggest that the inversion point occurs around a value of water cut of 35%. Both Newtonian and non-Newtonian (pseudo-plastic) behaviour were observed, and the ASTM viscosity model is found to be applicable to the emulsions. The effect of the mixing intensity on the resulting emulsion viscosity was found to be important at low temperatures and decreased at high temperatures. The experimental data fitted the available correlations in the literature.     

A Generalized Formula for Estimating Pure Alkenes Viscosity

Abstract

The importance of liquid viscosity in chemical process design makes it one of the most measured transport properties. Nevertheless, in the pure-component database, no experimental data on liquid viscosity for nearly 50% of the compounds are available. Therefore, prediction methods for liquid viscosity of alkenes over a wide range of absolute temperature for each components are necessary. Moreover, experimental data measured at lower temperatures are often extrapolated to higher temperatures with erroneous results. To improve liquid viscosity prediction of experimental data to temperatures and carbon numbers, we propose an empirical rule for estimating the viscosity of alkenes compounds. A predictive method, based physical properties (absolute temperature and carbon numbers) as its inputs, to correlate liquid viscosity by the statistical analysis is proposed. For a group of 19 compounds, the mean average absolute deviation was 4.6% for 118 data points. These values are better than other predictive methods and show that the statistical analysis model is stable and can be used to obtain good predictions for compounds that were not used in the model calibration.     

The Prediction of Undersaturated Crude Oil Viscosity: An Artificial Neural Network and Fuzzy Model Approach

Abstract

Viscosity is one of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. So it is important to use an accurate method to calculate the oil viscosity at various operating conditions. In the literature, several empirical correlations have been proposed for predicting undersaturated crude oil viscosity. However these correlations are not able to predict the oil viscosity adequately for a wide range of conditions. An extensive experimental data of undersaturated oil viscosities from different samples of Iranian oil reservoirs was applied to develop an Artificial Neural Network (ANN) model and fuzzy model to predict and calculate the undersaturated oil viscosity. Validity and accuracy of these models has been confirmed by comparing the obtained results of these correlations and with experimental data for Iranian oil samples. It was observed that there is acceptable agreement between the ANN model and fuzzy model results with experimental data.     

Data-driven modeling of heavy oil viscosity in the reservoir from geophysical well logs

Viscosity is the most crucial fluid property on recovery and productivity of hydrocarbon reservoirs, more particularly heavy oil reservoirs. In heavy and extra heavy oil reservoirs e.g. bitumen and tar sands more energy is required to be injected into the system in order to decrease the viscosity to make the flow easier. Therefore, attempt to develop a reliable and rapid method for accurate estimation of heavy oil viscosity is inevitable. In this study, a predictive model for estimating of heavy oil viscosity is proposed, utilizing geophysical well logs data including gamma ray, neutron porosity, density porosity, resistivity logs, spontaneous potential as well as P-wave velocity and S-wave velocity and their ratio (Vp/Vs). To this end, a supervised machine learning algorithm, namely least square support vector machine (LSSVM), has been employed for modeling, and a dataset was provided from well logs data in a Canadian heavy oil reservoir, the Athabasca North area. The results indicate that the predicted viscosity values are in agreement with the actual data with correlation coefficient (R²) of 0.84. Furthermore, the outlier detection analysis conducted shows that only one data point is out of the applicability of domain of the develop model.     

An accurate model to predict viscosity of heavy oils

The viscosity of heavy oils is a crucial factor in estimating oil recovery. Viscosity plays a vital character in reservoir simulations as well as in estimating the easiness of fluid flow, estimating oil recovery, and choosing a production model. The authors present a new intelligent model, a GA-LSSVM, used for predicting the viscosity of heavy oils. The experimental data employed in this work are the product of searching in many heavy oils data gathered from the literature. Development of robust predictive models to predict the viscosity of heavy oils is of immense help in many process engineering applications. The outcomes revealed that GA-LSSVM is able to capture the complicated and nonlinear relation between the input and output variables. GA-LSSVM model resulted in R² and mean absolute error values of 0.9999 and 0.9924, respectively.     

A novel MLP-ANN approach to predict solution gas-oil ratio

Abstract

The better understanding and estimation of reservoir fluids properties have straight effects on accuracy of different processes such as simulation, well testing, and material balance calculations, so importance of accurate estimation of PVT properties such as solution gas-oil ratio is obvious. To this end, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel predictive tool to estimate solution gas-oil ratio in terms of temperature, bubble point pressure, oil American Petroleum Institute gravity API, and gas specific gravity. Therefore, a total number of 1,137 experimental solution gas-oil ratios has been gathered from reliable databank for evaluation of model outputs. The different graphical and statistical analyses such as determination of average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the performance of MLP-ANN algorithm. The comparisons show that MLP-ANN algorithm has great accuracy in prediction of solution gas-oil ratio, so it can be used as a simple tool to predict phase behavior of reservoir fluids.     

Evaluation of isothermal kinetics of the thermal decomposition of guanidine nitrate in constant volume

ABSTRACT

In this paper, thermal decomposition characteristics of guanidine nitrate (GN) are studied by both dynamic and isothermal differential scanning calorimetry (DSC) tests in constant volume. The dynamic curve (1°C/min) shows that at least two overlapped peaks exist in the decomposition curve of GN, whereas the decomposition peaks of each isothermal experiment (305–320°C) can be clearly taken as a combination of three decomposition peaks. Besides, an isothermal kinetic model is studied. AKTS is used to decouple the isothermal decomposition peaks, to obtain three separate peaks, i.e., one decelerating model peak and two sigmoidal model peaks. An Nth order kinetic model is established for the first decomposition stage, and an autocatalytic model is established to describe the left two stages. The kinetic parameters of each stage are calculated by nonlinear fitting. The equation describing the reaction rate for the Nth order kinetic model as below, dα/dt = exp(8.92)exp(?67730/RT)(1?α)^1.020, the second and the third steps can be described by the equations of dα/dt = exp(26.15)exp(?165570/RT)(1-α)^0.508+ exp(23.87)exp(?159360/RT)(1-α)^0.508α^0.150 and dα/dt = exp(24.03) exp(?166070/RT)(1-α)^0.082+exp(23.28)exp(?160000/RT)(1?α)^0.082α^0.210, respectively. The results calculated by these models fit well the experimental data, proving that the kinetic parameters are reliable and accurate.     

THERMAL EFFECT ON THE PERMANENT VISCOSITY OF A SASKATCHEWAN HEAVY OIL

ABSTRACT

Future energy demands will likely cause increased activity towards the recovery of heavy oil using non-conventional means. Most non-conventional attempts to recover Saskatchewan's heavy oil resources have utilized thermal techniques.

This report discusses the permanent viscosity changes which occur when heavy oil.is subjected to thermal processes from 220 to 425°C. It was observed that under closed operating conditions, the oil viscosity drops in a manner which can be modeled by a first order, kinetic reaction model. The rate constant for this reaction varied from 0.3 × 10^?3 to 6.0 × 10^?3 h^?1 depending on temperature and the assumed molecular weights of the model components. These findings closely parallel earlier results.

Experimental observations on thermal effects during opened operating conditions indicate a dramatic and rapid rise In the remaining crude oil viscosity. The oil was observed to increase its permanent absolute viscosity by a factor as high as 21 times its original absolute viscosity. The single product, first order kinetic model was not capable of predicting this rise in oil viscosity. A simple, two product, first order kinetic model was developed and found to fit the data satisfactorily with a rate constant of 0.6 h^?1 for heavy product formation and a rate constant of 0.03?h^?1 for light product formation at 275°C.     

Prediction of the Kinematic Viscosity of Crude Oil Fractions

Abstract

A generalized equation based on modified Eyring's theory for predicting kinematic viscosity of petroleum fractions is proposed in this work. The equation uses two reference fluids including a pair of (C6 and C10), (C10 and C14), or (C14 and C20) for petroleum fractions of molecular weight higher than 70 and lower than 300.

Validity and accuracy of this equation have been confirmed by comparing the obtained results of this equation with experimental data. In contrast to other correlations that require so many specific parameters for oil viscosity prediction, this type of equation requires only molecular weight and true boiling point. The results obtained in this work are in agreement with experimental data with an average absolute deviation (AAD) of less than 5%.     

Application of the PR-EOS with C7+ Splitting to Model Fluid Properties in a Large Two-Phase Petroleum Reservoir

Abstract

The use of equations of state (EOS) to model fluid properties is necessary in order to have an internally consistent set of PVT properties, which is essential, especially, when it is desired to use compositional simulators to model two-phase reservoirs. In this article, the 3-parameter Peng-Robinson equation of state along with single carbon number (SCN) splitting of the C₇₊ fraction are used to model a major onshore reservoir in Abu Dhabi that has horizontal and vertical fluid properties variations. Extensive screening and checking of PVT data of the field was necessary to develop this model. Also, extensive verification of the developed model was accomplished by comparing its results to data external to the model. Results of this article indicate the capability of using multiple well PVT analysis within the three-parameter Peng-Robinson EOS to model complex two-phase reservoirs such as this one. We describe the process of building up the model and the challenges involved in performing this task, which include proper selection of representative experimental data to build the model, along with extensive screening and data quality these data, and the model verification so that we have the confidence that one EOS model that can predict the reservoir fluid PVT properties.     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.     

GENERALIZED KINEMATIC VISCOSITY-TEMPERATURECORRELATION FOR UNDEFINED PETROLEUM FRACTIONS OF IBP – 95°C TO 455°C+ BOILING RANGES

ABSTRACT

A generalized kinematic viscosity-temperature correlation for undefined petroleum fractions of all boiling ranges including 455^+°C fractions have been developed to represent the data for a wide range of temperature from 30 to 200°C. The correlation is based on experimental kinematic viscosity data for twenty TBP fractions of Arab heavy, Arab medium, Arab light and Arab extra-light crude oils. The proposed correlation has been found to fit all the eperimental data consisting of 248 measurements of the kinematic viscosity with an overall average absolute deviation of 9.07% compared to 15.47% given by ASTM method.     

ANFIS-GA modeling of dynamic viscosity of N-Alkane in different operational conditions

Viscosity is known as one of major properties of fluids which have straight effects on different parts of chemical and petroleum industries. Due to this importance, in the present work, adaptive neuro-fuzzy interference system (ANFIS) was coupled with genetic algorithm (GA) to predict dynamic viscosity of normal alkane in terms of molecular weight of n-alkane, temperature and pressure. In order to prepare and validate the predicting model 228 experimental data points were extracted from the literature. The outputs of this predictive tool were compared with the experimental data and comparisons showed that predicted dynamic viscosities have good agreement with experimental data. According to the statistical and graphical analyses this simple tool can be used as a rigorous and accurate method for prediction of dynamic viscosity of n-alkane, especially at reservoir conditions.     

Dynamic Interfacial Tension Between Gudao Heavy Oil and Petroleum Sulfonate/HPAM Complex Systems

Abstract

The dynamic interfacial tension between Gudao heavy oil and petroleum sulfonate/hydrolyzed polyacrylamide complex system was studied. It is shown that with the addition of hydrolyzed polyacrylamide into the solution of petroleum sulfonate, not only is the viscosity of the complex system increased, but also the dynamic interfacial tension is further lowered. Thus a complex system with high viscosity and low interfacial tension can be constituted by 0.3 wt% petroleum sulfonate and 0.18 wt% hydrolyzed polyacrylamide with a viscosity-averaged molecular weight of 1 × 10⁷. In addition, the dynamic interfacial tension between complex system and crude oil can also be lowered by increasing the salt content in solution.     

Evaluation of empirically derived PVT properties for Pakistani crude oils

This study evaluates the most frequently used pressure-volume-temperature (PVT) empirical correlations for Pakistani crude oil samples. The evaluation is performed by using an unpublished data set of 22 bottomhole fluid samples collected from different locations in Pakistan. Based on statistical error analysis, suitable correlations for field applications are recommended for estimating bubblepoint pressure, oil formation volume factor (FVF), oil compressibility and oil viscosity.