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Measurement of Gas Condensate, Near-Critical and Volatile Oil Densities and Viscosities at Reservoir Conditions
... In fact, inaccurate estimation of condensate liquid viscosity below the dew point has a detrimental effect on cumulative production and can lead to large errors in reservoir performance. Previous studies show 1% error in reservoir fluid viscosity resulted in 1% error in cumulative production [3][4][5] . ...
... All of these models, which estimate the viscosity in the liquid phase, are used for oil and are a function of the viscosity of the crude oil, which is very different from the liquid of the condensate reservoirs and is not suitable for predicting the viscosity of condensate 16 . Also, due to the variability of viscosity in condensate reservoirs due to pressure changes, the empirical relationships provided to estimate the viscosity of gas mixture cannot well describe the behavior of condensate 4,17,18 . ...
... In this study, a comprehensive set of data was collected to predict the viscosity of gas condensate 4,[27][28][29][30][31][32][33][34][35][36] . The data set includes 1370 laboratory data points comprising of temperature and pressure of gas reservoirs and components of condensate mixtures (from C 1 to C 11 and the molecular weight of C 12+ along with N 2 and CO 2 ), which are the inputs of the models. ...
In gas-condensate reservoirs, liquid dropout occurs by reducing the pressure below the dew point pressure in the area near the wellbore. Estimation of production rate in these reservoirs is important. This goal is possible if the amount of viscosity of the liquids released below the dew point is available. In this study, the most comprehensive database related to the viscosity of gas condensate, including 1370 laboratory data was used. Several intelligent techniques, including Ensemble methods, support vector regression (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) optimized by Bayesian Regularization and Levenberg–Marquardt were applied for modeling. In models presented in the literature, one of the input parameters for the development of the models is solution gas oil ratio (Rs). Measuring Rs in wellhead requires special equipment and is somewhat difficult. Also, measuring this parameter in the laboratory requires spending time and money. According to the mentioned cases, in this research, unlike the research done in the literature, Rs parameter was not used to develop the models. The input parameters for the development of the models presented in this research were temperature, pressure and condensate composition. The data used includes a wide range of temperature and pressure, and the models presented in this research are the most accurate models to date for predicting the condensate viscosity. Using the mentioned intelligent approaches, precise compositional models were presented to predict the viscosity of gas/condensate at different temperatures and pressures for different gas components. Ensemble method with an average absolute percent relative error (AAPRE) of 4.83% was obtained as the most accurate model. Moreover, the AAPRE values for SVR, KNN, MLP-BR, MLP-LM, and RBF models developed in this study are 4.95%, 5.45%, 6.56%, 7.89%, and 10.9%, respectively. Then, the effect of input parameters on the viscosity of the condensate was determined by the relevancy factor using the results of the Ensemble methods. The most negative and positive effects of parameters on the gas condensate viscosity were related to the reservoir temperature and the mole fraction of C 11 , respectively. Finally, suspicious laboratory data were determined and reported using the leverage technique.
... It can be said that determination with the error of this parameter leads to misdiagnosis in estimating the behavior of these reservoirs. It was shown that a low percentage of error in determining liquid viscosity leads to the same amount of error in calculating reservoir accumulative production, which is a significant amount (Al-Meshari et al., 2007;Whitson et al., 1999;Yang et al., 2007). ...
... Experimental measurement of liquid viscosity in these reservoirs is a difficult process because of the inaccessibility of the samples, the limitation in establishing HPHT condition at laboratory equipment, the small volume of measuring cell, and the time-consuming and expensive nature of laboratory tests. This can lead to a tendency toward using theory-based correlations (Al-Meshari et al., 2007;Whitson et al., 1999;Hemmati-Sarapardeh et al., 2014). Based on the input parameters, these correlations are categorized into two groups; the first one contains semi-experimental models that use some of the reservoir fluid properties, such as acentric factor, critical temperature, pour point temperature, fluid composition, boiling point, and molecular weight. ...
... They are a straight function of the crude oil viscosity, and as a result of the huge effect of crude oil components on its viscosity, the predicted value of this parameter by the correlations is very unreliable (Whitson and Brulé, 2000;El Aily et al., 2019). Due to significant changes in the viscosity of condensate, empirical and semi-empirical correlations cannot quite represent changes in viscosity with pressure in the GC reservoirs (Al-Meshari et al., 2007;Whitson and Brulé, 2000;Fevang and Whitson, 1996). ...
By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software.
... In fact, inaccurate estimation of condensate liquid viscosity below the dew point has detrimental effect on cumulative production and can lead to large errors in reservoir performance. Previous studies show 1% error in reservoir fluid viscosity resulted in a 1% error in cumulative production (Al-Meshari et al., 2007;Whitson et al., 1999;Yang et al., 2007). ...
... Measurement of condensate viscosity in gas condensate reservoirs is not made in a routine laboratory test and it may be very difficult to obtain due to unavailability of the samples, lack of high pressure high temperature (HPHT) facilities, small volume cell viscometers and time and cost required for the measurements. Consequently this makes use of theoretical correlation more attractive (Al-Meshari et al., 2007;Hemmati-Sarapardeh et al., 2014;Whitson et al., 1999). ...
... Moreover, they are direct function of dead oil viscosity, which is one of the most unreliable properties to be predicted by correlations due to the large effect that oil type (paraffinicity, aromaticity and asphaltene content) has on viscosity (Aily et al., 2019; Whitson et al., 2000). Condensate liquid viscosity in near wellbore region can change significantly during depletion in gas condensate reservoirs (Al-Meshari et al., 2007;Fevang, 1995;Whitson et al., 2000). Consequently, empirical and semi-empirical correlations do not fully reflect the viscosity changes with pressure in gas condensate reservoirs near wellbore region. ...
... Therefore, for better reflection of aforementioned changes below the dew point a new viscosity correlation developed, using nonlinear regression analysis and optimization techniques. Two sets of experimental data of Yang et al. (2007) and Al-Meshari et al. (2007) was used for developing new viscosity correlation. New correlation was incorporated with two-phase compressibility factor of Rayes et al. (1992) in generating PVT tables and determining pseudopressure integral. ...
... The knowledge of PVT data such as formation volume factor, viscosity, compressibility factor and solution gas to oil ratio is essential to form pseudopressure integral and construct inflow performance relationship (IPR). Viscosity and compressibility factor are governing parameters to model gas condensate pseudopressure integral and determine the performance ( Two-phase important of viscosity the research shows 1% error in calculating reservoir fluid viscosity resulted in 1% error in cumulative production (Al-Meshari et al., 2007;Fevang and Whitson, 1996;Hernandez et al., 2002;Sutton, 2005;Whitson et al., 1999). Behmanesh et al. (2017) found that using single dry gas viscosity and compressibility factor effect the performance prediction of gas condensate reservoirs. ...
... Hence in this study an attempt was made to optimize the existing well known viscosity correlations, for better modelling of gas condensate reservoirs through establishing new Inflow Performance Relationship (IPR). For this purpose two sets of experimental data by Al-Meshari et al. (2007) and Yang et al. (2007) selected. These studies carried out in elevated pressure and temperature in laboratory condition similar to the reservoir temperature and pressure condition. ...
Inflow Performance Relationships (IPRs) are important element for reservoir engineers in the design of new wells and also for monitoring and optimizing existing wells. IPRs are used to determine optimum production of gas rate and condensate rate in a well for any specified value of average reservoir pressure and predict the performance.
Jokhio and Tiab proposed a simple method of establishing IPR for gas condensate wells. The method uses transient pressure test data to estimate effective permeability as a function of pressure. Effective permeability data used to convert production bottomhole flow pressure into pseudopressure to establish well performance. Despite the effectiveness of the method, single phase correlations were used in PVT calculations of each phase, which over simplified the fluid flow in gas condensate wells. Single phase dry gas equations do not reflect the multiphase flow behaviour of gas condensate wells below the dew point. Due to this limitation Jokhio and Tiab method modified by this study and new analytical IPRs for gas condensate well proposed.
The major improvement of the above method is incorporating new viscosity correlation developed by this study and using two-phase compressibility factor as key parameters for predicting gas condensate inflow performance. Therefore, the main contribution of this study is development of viscosity correlation which is a critical issue in predicting gas condensate inflow performance both above and below the dew point. Optimization techniques and nonlinear regression used to develop a new viscosity correlation for high temperature heavy gas condensate reservoirs under depletion.
The application of the new model is illustrated with field example for current IPR curves. Compositional simulation study of the well performed in PIPSIM simulator. The proposal approach provides reasonable estimates of simulator input reservoir properties (e.g. IPRs). Accuracy of the new method compared with compositional simulation study. The proposed method presents average absolute relative deviation (AARD) of 5.8% for gas IPR and 7.5% for condensate IPR compare to compositional simulation results. New method provides a tool for quick estimation of gas condensate wells without need of relative permeability curves and expensive and time consuming compositional simulation.
... To assess the accuracy of the studied literature viscosity models and also develop a novel condensate viscosity correlation a data bank is gathered from experimental studies in the open literature. The sources of the data bank are (Al-Meshari et al., 2007;Audonnet and Pádua, 2004;Fevang, 1995;Gozalpour et al., 2005;Guo et al., 1997;Kariznovi et al., 2012;Kashefi et al., 2013;Khorami et al., 2017;Strand and Bjørkvik, 2019;Thomas et al., 2009;Yang et al., 2007). In the aforementioned studies, various methods include rolling ball viscometer, electromagnetic pulse technology viscometer and capillary viscometer and vibrating-wire sensor have been used for measurement of condensate phase viscosity. ...
Accurate estimation of gas condensate fluid properties is a challenging task due to evolving condensate liquid from the gas phase below the saturation pressure. Among the fluid properties viscosity of condensate liquid has the largest prediction uncertainty. The existing literature methods cannot cope with non-linearity and physics of gas condensate mixture (transition from single phase to two-phase) below the saturation pressure. Hence, in this study based on the experimental condensate viscosity data a simple linear equation as a function of pressure, temperature and solution gas to oil ratio was developed. For this purpose, comprehensive data source of 1368 experimental data points acquired from open literature has been used. For developing new condensate viscosity correlation an Artificial Intelligence method known as Takagi – Sugeno – Kang fuzzy algorithm was utilized. The accuracy of the developed correlation was compared with five previously published literature models. The superiority of new correlation over existing literature models is confirmed by statistical parameters of least root mean square error of 0.0194, mean average error of 0.0163 and average absolute relative deviation percentage of 7.123. The proposed condensate viscosity correlation is valid in a pressure rang of 0.25 – 75.84 MPa), temperature range of 303 – 443.15°K and Rs of 41.96 – 13496 scf/STB.
The proposed correlation can be used as an alternative approach to existing models for accurate estimation of gas condensate viscosity and produce reliable reservoir simulation studies.
... Gravity can affect the near critical condensate very strongly and wettability plays a great role during phase separation(Williams and Dawe 1989). Proper characterization of higher molecular fractions (C7 through C20+) of near critical reservoir fluids were necessary(Rosales, Ashford et al. 1992) to match the field GOR, saturation pressure etc. Importance of proper calculations method of Interfacial Tension (IFT) on the gravity drainage contribution on recovery of near critical fluid like condensate was demonstrated(Ceragioli and Masserano 1998).Viscosity and density of near critical fluids were measured (Al-Meshari,Kokal et al. 2007) by designing PVT apparatus suitable for elevated pressure and temperature and these data were used to evaluate the correlations for viscosity used in commercial software. Laboratory experiments proved that an increase in temperature improved the final oil recovery after injecting surfactant and water(Najurieta, Galacho et al. 2001).Fang et al. (1998) discussed the importance of correct fluid characterizations. ...
The growth of production from liquid shale plays has been phenomenal. However, the
recoveries are low of the order of 10% and more efficient methods of producing liquids
are necessary. This research is aimed at understanding production performances
involving complex interaction between phase behavior and flow in unconventional
reservoirs like shales. A new rapid semianalytical forecast tool for transient state linear
flow in ultralow permeability (100 nD to 5000 nD) fractured reservoir was developed.
The tool is useful for well inflow performance, condensate drop out and material balance
calculations of condensate production in unconventional reservoirs.
Effects of individual parameters such as reservoir properties (matrix permeability,
heterogeneity, rock compressibility and reservoir pressure) on production oil were
studied using reservoir simulations with an appropriate number of grid blocks. The
matrix permeability, initial reservoir pressure, fracture spacing were the most influencing
factors in recoveries from gas-condensate as well as from oil reservoirs. Operating the
well at higher flowing bottom hole pressure (FBHP) is preferable for low permeability
(100 nD) reservoir and low FBHP for higher permeability (1000 nD) reservoir to recover
more liquid. Production data, including Gas Oil Ratios (GOR) are valuable in assessing
reservoir performance. A single characteristic factor affecting the produced gas oil ratio
was found to be (1– Rsw/Rsb) (1–Pwf/Pb) /(1–Pwf/Pi) that predicts deviation of gas oil ratio from its initial value.
Oil recovery simulation sensitivity increases with heavier oils for the existing viscosity models, driving into higher levels of difficulty when fitting viscosity data for rising oil heaviness, particularly below the saturation pressure. Keeping in view the similarity of trends of viscosity and density with isothermal pressures for reservoir oils, the P-μ-T cubic viscosity model, which was developed for pure hydrocarbon components, was extended to reservoir oils. Two parameters in the P-μ-T cubic viscosity model for mixtures with pure and pseudocomponents are identified for adjusting the viscosity data fitting: Kc for controlling the viscosity gradient with pressure and the “ε” shifting trends for increasing viscosity direction. These two parameters are treated as the adjustable parameters required for fitting the viscosity data.
A total of 129 reservoir oils from different sources are used to validate the reliability of the P-μ-T viscosity model. The default model (where ε and Kc are 1 and 45, respectively), extended to 71 light oils, resulted in 31% of average absolute relative deviation (AARD) in viscosity prediction. However, separate adjusted parameters are obtained per oil for more accurate viscosity data fitting. Application of the model in this work results in (post-fitting) AARD% of 2.86% average for 36 low-viscosity oil data, 5.68% for 9 high-viscosity oil data, 9% for five oil blends, and 4.11% for bitumen blends. The model gives an AARD of 3.06% in the undersaturated region and 3.79% in the saturated region for the oil considered. The model predicts better the viscosity above saturation pressure for low-viscosity oils and below saturation pressure for high-viscosity oils. A comparative analysis of the P-μ-T cubic viscosity model vs. other models demonstrates its ability to successfully capture viscosity trends of oil and blends in all viscosity ranges. For simplicity, the P-μ-T cubic viscosity model is proposed with only two optimizing parameters even though using a third parameter improves the matching in heavy oils.
Gas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel machine learning approach is developed to predict condensate viscosity in the near wellbore regions (μc) from five input variables: pressure (P), temperature (T), initial gas to condensate ratio (RS), gas specific gravity (γg), and condensate gravity (API). Due to the absence of accurate recombination methods for determining μc machine learning methods offer a useful alternative approach. Nine machine learning and hybrid machine learning algorithms are evaluated including novel multiple extreme learning machine (MELM), least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) and each hybridized with a particle swarm optimizer (PSO) and genetic algorithm (GA). The new MELM algorithm has some advantages including 1) rapid execution, 2) high accuracy, 3) simple training, 4) avoidance of overfitting, 5) non-linear conversion during training, 6) no trapping at local optima, 6) fewer optimization steps than SVM and LSSVM. Combining MELM with PSO, to find the best control parameters, further improves its performance. Analysis indicates that the MELM-PSO model provides the highest μc prediction accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R²) of 0.9931 for a dataset of 2269 data records compiled from gas-condensate fields around the world. The MELM-PSO algorithm generates no outlying data predictions. Spearman correlation coefficient analysis identifies that P, γg and Rs are the most influential variables in terms of condensate viscosity based on the large dataset studied.
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