A correlation approach for prediction of crude oil viscosities

The role of reservoir fluid viscosity for reservoir evaluation in performance calculations, planning thermal methods of enhanced oil recovery, evaluation of hydrocarbon reserves and designing production equipment and pipelines makes its accurate determination necessary. Reservoir oil viscosity is usually measured isothermally at reservoir temperature. However, at temperature other than reservoir temperature these data are estimated by empirical correlations. High dependency of oil viscosity on fluid nature and fluid source causes the unique application of these correlations to special cases from which they have been derived.Here, based on Iranian oil reservoirs data; new correlations have been developed for prediction of dead, saturated and under-saturated oil viscosities. These correlations have been derived using so many oil viscosity data. Validity and accuracy of these correlations have been confirmed by comparing the obtained results of these correlations and other ones with experimental data for so many Iranian oil samples. In contrast to other correlations which need so many specific parameters for oil viscosity prediction, this type of correlations need only some field data which always are available. Checking the results of these correlations shows that the obtained results of Iranian oil viscosities in this work are in agreement with experimental data compared with other correlations.     

PVT-generated Correlations of Heavy Oil Properties

Pressure-volume-temperature (PVT) properties are necessary to reservoir engineering calculations in porous media and it is important for calculations in pipeline as well. This work presents a new set of correlation for estimating Iranian Crude oils properties based on some experimental data. Whenever there is no representative experimental PVT data, these correlations can be used for oils of API ranging between 15 and 30. New correlations was developed to calculate oil formation volume factor (Bo), bubble point pressure (Pb), and solution gas oil ratio (Rs). Finally, a comparison is made between these correlations and other published correlations such as Standing, Vazquez and Beggs, Glaso, Farshad et al., Al Marhoun, Petrosky and Farshad, and Hanafi et al. and it is found out that the new correlations are more accurate than the other ones.     

New Empirical Correlations for Predicting Crude Oil Properties

Abstract

Pressure–volume–temperature (PVT) properties of crude oil are essential parameters used for prediction of fluid flow both in porous media and through transmission pipelines. Whenever laboratory works are absent, the engineer should use regionally developed correlations. A large portion of all crude oil resources is located in the Persian Gulf countries, and they have more or less similar API ranges and acidities, so that any empirical PVT correlation based on data from one region can adequately predict the behavior of others in this large geological basin. In this study, a new set of black oil–type correlations for bubble point pressure (P_b), solution gas–oil ratio (GOR; R_s), and formation volume factor (Bo) is proposed based on more than 400 Iranian crude oil PVT lab data. Moreover, previous works were reviewed, most of which were not suitable to model Iranian crude PVT behavior. Although the new correlations are developed over Iranian crudes, they can be used for prediction over any crude oil with similar compositional properties (API and acidity). Then the accuracy of these correlations is compared with the newly presented set and the superiority of this work for predicting those parameters is demonstrated.     

Introducing a New Method for Predicting PVT Properties of Iranian Crude Oils by Applying Artificial Neural Networks

Abstract

In this study artificial neural networks (ANNs) have been applied for the prediction of main pressure, volume, and temperature (PVT) properties, bubble point pressure (P_b), and bubble point oil formation volume factor (B_ob) of crude oil samples from different wells of Iranian oil reservoirs. Via a detailed comparison, the great power of ANNs with respect to traditional methods of predicting PVT properties, like Standing, Vasquez and Beggs, and Al-Marhoun, with higher prediction precision up to R² = 0.990 has been illustrated and the obtained parameters of ANNs for the application of prediction of other crude oil samples has been presented. The applied PVT data set in this study consists of 218 crude oil samples from Iranian reservoirs and for assurance of the applicability of the ANN model the PVT data set has been divided into 2 training (190 samples) and cross validation (28 samples) data sets and obtained ANNs from applying the training data set has been tested on the cross validation data set which has not been seen by the network during the training process. The obtained results for both training and cross validation data sets confirm the great prediction power of ANNs, for both data sets with respect to traditional PVT correlations.     

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.     

New Correlation for Calculation of Hydrocarbon Gas Minimum Miscibility Pressure (MMP) Using Wide Experimental Data

The authors present a new empirically derived correlation for estimating the minimum miscibility pressure (MMP) required for multicontact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding. Only few empirical correlations exist for determining the MMP. These correlations are often used to estimate the MMP without considering the composition of the injected gas. On the other hand these correlations are based on a limited set of experimental data, which are not quite applicable. In addition, in such correlations the complex condensing/vaporizing displacement process is not regarded. In this study, however, the derived correlation investigates the influence of the vaporizing/condensing drive mechanism and oil and gas composition on gas miscibility pressure. MMP has been correlated with temperature, oil composition, and injection gas composition. Their effect on hydrocarbon gas MMP has been documented by using sensitivity analysis by slim tube experimental data. The new correlation is based on regression of widely experimentally measured MMP data in literature and data derived from slim tube experiment in this study. By comparing the calculated MMPs from the improved correlation data with currently used correlations and experimentally measured data, it was found that the new correlation is significantly more accurate than other correlations.     

Study on the characteristics of palm oil–biodiesel–diesel fuel blend

Palm oil/palm oil methyl esters are blends with diesel fuel, the blends were characterized as an alternative fuels for diesel engines. Density, kinematic viscosity, and flash point were estimated according to ASTM as key fuel properties. Palm oil and palm oil biodiesel were blended with diesel. The properties of both blends were estimated. The results showed that the fuel properties of the blends were very close to that of diesel till 30% unless other characteristics are within the limits. The experimental data were correlated as a function of the volume fraction of oil/biodiesel in the blend. Different correlations were developed to predict the properties of the oil/bio-oil-diesel blends based on our experimental results. The developed correlations were validated by comparing the correlation prediction with experimental data in literature. A good agreement was found between modeled equations prediction and experimental data in literature. The developed equations can be used as a guide for determining the best blending mixture to be used for diesel engines.     

The Thermodynamic Modeling of Asphaltene Precipitation Equilibrium during the Natural Production of Crude Oil: The Role of Pressure and Temperature

Asphaltene precipitation is a sophisticated issue in the upstream oil industry, worldwide, and has detrimental effect on a verity of production processes; it damages the properties of the reservoir and causes an unfavorable and significant decrease in oil production. In spite of numerous studies to predict asphaltene behavior, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. In this study the PVT data as well as experimental data of asphaltene precipitation at reservoir conditions of an Iranian light oil samples is used, and the asphaltene precipitation and deposition envelops (APE and ADE) of the oil are developed using solid thermodynamic modeling.     

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.     

An Experimental Investigation of Asphaltene Precipitation During Natural Production of Heavy and Light Oil Reservoirs: The Role of Pressure and Temperature

Abstract

Many oil reservoirs encounter asphaltene precipitation as a major problem during natural production. In spite of numerous experimental studies, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. To study their asphaltene precipitation behavior at different temperatures, two Iranian light and heavy live oil samples were selected. First, different screening criteria were applied to evaluate asphaltene instability of the selected reservoirs using pressure, volume, and temperature data. Then, a high pressure, high temperature filtration (HPHT) setup was designed to investigate the asphaltene precipitation behavior of the crude samples throughout the pressure depletion process. The performed HPHT tests at different temperature levels provided valuable data and illuminated the role of temperature on precipitation. In the final stage, the obtained data were fed into a commercial simulator for modeling and predicting purposes of asphaltene precipitation at different conditions. The results of the instability analysis illustrated precipitation possibilities for both reservoirs which are in agreement with the oil field observations. It is observed from experimental results that by increasing the temperature, the amount of precipitated asphaltene in light oil will increase, although it decreases precipitation for the heavy crude. The role of temperature is shown to be more significant for the light crude and more illuminated at lower pressures for both crude oils. The results of thermodynamic modeling proved reliable applicability of the software for predicting asphaltene precipitation under pressure depletion conditions. This study attempts to reveal the complicated role of temperature changes on asphaltene precipitation behavior for different reservoir crudes during natural production.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

黑油模型在凝析油气多相集输管流工艺计算中的应用

采用黑油模型计算凝析油气的热物性参数,并采用经验或半经验关系式计算集输管路的压力、温度,计算精度能够满足要求。在输送压力高于凝析油气露点压力的情况下,首先应考虑采用黑油模型进行工艺计算。     

Prediction of asphaltene precipitation during gas injection

Maintaining the flow of multiphase fluid from the reservoir to the surface has been an important issue with wide economic importance for the petroleum industry. Asphaltene precipitation due to change in temperature, pressure, and composition of oil can adversely affect the oil flow to the surface by reducing the available diameter of the tubing. In this study, the precipitation of asphaltene from an Iranian crude oil was investigated. To do our study, through information about asphaltene instability in the live oil during both natural depletion and gas injection conditions about oil sample from Iranian oil field was gathered. Then, the solid model and scaling model were utilized to predict the weight percent of precipitated asphaltene at a wide range of the pressure and temperature. Results of the work revealed that both models predict the increase in weight percent of precipitated asphaltene when lean gas injected to the live oil at the maximum point of asphaltene instability. In addition, the study showed that both models are capable of predicting the experimental data of asphaltene precipitation; while scaling modeling is more reliable when the gas is injected to the oil.     

The Effect of Temperature and Pressure on the Reversibility of Asphaltene Precipitation

A lot of hindrances are seen in petroleum operation, production, and transportation as a results of factors that related to asphaltene precipitation. It has great importance to investigate the reversibility of asphaltene precipitation under changes of effective factors on thermodynamic conditions such as pressure, temperature, and composition. In the present work the reversibility of asphaltene precipitation under changes of pressure and temperature was investigated for two kind of Iranian heavy oil. The stability test shows these samples are located at unstable region in aspect of asphaltene precipitation. The experimental procedure includes two parts, (a) decreasing pressure from initial reservoir pressure to near saturation pressure and surveying asphaltene content hysteresis with redissolution process at reservoir temperature, and (b) investigation of precipitated asphaltene in both precipitation and redissolution processes at different temperature and reservoir pressure. At each step IP143 standard test was used to measure precipitated asphaltene. It was concluded that above bubble point pressure, asphaltene precipitation is nearly reversible with respect to pressure for both samples and it was partially reversible with respect to the temperature for sample A, and accordingly pressurizing is acceptable method for solving the problem in both heavy asphaltenic crude oil samples and increasing temperature is acceptable method for solving asphaltene problem in crude oil sample A. Also density measurement of flashed oil confirmed that there is a little hysteresis in asphaltene content during redissolution and precipitation processes.     

Experimental Comparison of Nitrogen and Carbon Dioxide Oil Displacement in Carbonate Cores

Abstract

The feasibility of nitrogen and carbon dioxide flooding is being investigated experimentally as possible enhanced oil recovery processes in Iranian carbonate oil fields. Laboratory tests were conducted on a tight permeability sample of an Iranian oil field. Three flooding tests were conducted at back pressures of 1,000, 2,000, and 2,500 psi for both nitrogen and carbon dioxide separately. All tests were conducted at constant temperature of 28°C. Experimental results indicate that immiscible carbon dioxide can mobilize more oil than immiscible nitrogen due to the ability of carbon dioxide to dissolve in oil. The key factor in higher recoveries of carbon dioxide injection compared to nitrogen is the ability of carbon dioxide to extract oil components. Extraction dominates after carbon dioxide breakthrough. Although the only mechanism of oil displacement in nitrogen flooding tests was the displacement energy applied by injection pressure, the oil recoveries in nitrogen flooding are considerable, especially at higher pressures.     

An Experimental Investigation of Permeability Impairment Under Dynamic Flow Conditions Due to Natural Depletion in an Iranian Oilfield

Asphaltene deposition is an issue that has received much attention since it has been shown to be the cause of major production problems. It leads to permeability reduction under the processes of natural depletion as well as hydrocarbon gas/CO₂ injection. Though a great deal of researches have focused on studying permeability impairment in reservoir rocks, little is known about the asphaltene deposition mechanisms that control the permeability reduction for Iranian reservoirs. In this work, an experimental effort is made to investigate the permeability impairment of core samples of Iranian oil reservoirs. The experiments are performed on both sandstone and carbonate rock types at reservoir temperature and pressure. The mass balance was used for evaluating of porosity reduction during the experiments. The results indicate that the dominant deposition mechanism changes as production proceeds. In addition, it has been found that the primary mechanism in permeability impairment is surface deposition. On the other hand, entrainment of asphaltene particles is manifested when outlet pressure drops from 4,200 to 3,800 Psig for both sandstone and carbonate samples. It can be drawn that asphaltene entrainment dependence to pressure is much more than that to the injected pore volume. This research illuminates the deposition mechanisms and determines dynamic parameters of asphaltene deposition, which are necessary to devise reliable prevention strategies.     

Visual Experimental Study of the Factors Affecting the Stability of Foamy Oil Flow

Abstract

Some of the heavy oil reservoirs in Canada, Venezuela, and China under solution gas drive illustrate important features: low gas–oil ratio, high production rates, and very high ultimate oil recovery. It is generally believed that the foamy oil flow is one of the important mechanisms contributing to these unusual performances. A series of two-dimensional etched glass micromodel experiments was carried out to gain an insight into the processes involved in foamy oil flow. In these experiments, the bubble nucleation, migration, coalescence, breakup, and ultimate generation of a continuous gas phase can be observed continuously. On the basis of the experimental analysis, there are two bubble-point pressures in the foamy oil: One is the true bubble-point pressure, and the other is the pseudo-bubble-point pressure. The greater the difference between these two bubble-point pressures, the more stable the foamy oil flow is and the greater the contribution to oil recovery from the foamy oil drive mechanism is. The visualization experimental results show that there are many factors affecting the stability of the foamy oil, including pressure depletion rate, crude oil viscosity, temperature, dissolved gas–oil ratio, etc.     

A condensation model for calculating pressure gradients in condensate wells

A method is presented to calculate pressure traverses in a condensate well utilizing existing techniques for two-phase flow of fluids in pipes. Existing correlations have been developed for black oil flow where gas evolves from the oil as pressure and temperature are reduced. In the case of condensate flow, a liquid evolves as pressure and temperature are reduced, thereby making the existing correlations inapplicable. The Peng-Robinson equation of state is used to model the gas/condensate fluid and predict the gas/liquid ratio at any pressure and temperature as a traverse is calculated for the depth of the flowing well. The existing two-phase flow correlations for black oil flow are used in this work to predict gas/liquid properties such as liquid holdup, friction factors, and flow regimes. This model is then applied to several wells for which extensive data was taken, including PVT properties and pressure measurements.     

Calculation of heavy oil viscosity based on an Arrhenius model

The commonly used heavy oil viscosity models are considered as the function of temperature, API, and colloid asphalt. Some of the published correlations are just for one oil field or a region, but they have some limitations when the viscosity of other oil samples is calculated. Even these multi-parameter models cannot be used in the absence of API or colloid asphalt content data. This paper presents a new and simple method to predict the viscosity of heavy oil based on the Arrhenius model, which only needs to measure the viscosity of heavy oil under the temperature condition of 50°C. This method can predict the viscosity in the absence of API or colloid asphalt content data. A total of 31 heavy oil samples were collected from published literature works to test the accuracy and reliability of the new method and other eight classical models. The new method can predict the viscosity with an average relative error of 2.37% and an average absolute error of 8.43%. Compared with the commonly used model, the new method shows consistently accurate results. It also reduces the experimental workload and calculation process, without measuring the API and asphalt content data.     

The Determination of Effective Diffusivity Coefficients in a Solvent Gas Heavy Oil System for Methane

Abstract

In this investigation, an accurate high pressure and temperature diffusion setup was applied to measure the diffusion coefficients of methane in Iranian heavy oils in presence and absence of porous media by using the pressure-decay method. The solvent diffusivity in heavy oil was determined by both graphical and numerical methods. In addition, the effects of the porous medium and the temperature on the molecular diffusion coefficient of the solvent gas in the liquid phase were discussed and finally, using experimental data, a functionality dependence of molecular diffusivity on temperature and porous medium characteristics was proposed.