Using an artificial neural network to predict carbon ...

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networks is presented to predict CO2 compressibility factor. ... Keywords: Carbon Dioxide, Compressibility Factor, Artificial Neural Network, Equations of State, ...

Korean J. Chem. Eng., 32(4), 1-10 (2015) DOI: 10.1007/s11814-015-0025-y

pISSN: 0256-1115 eISSN: 1975-7220



Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature Erfan Mohagheghian*, Habiballah Zafarian-Rigaki**, Yaser Motamedi-Ghahfarrokhi**, and Abdolhossein Hemmati-Sarapardeh**,† *Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada **Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran (Received 18 October 2014 • accepted 27 January 2015) Abstract−Carbon dioxide injection, which is widely used as an enhanced oil recovery (EOR) method, has the potential of being coupled with CO2 sequestration and reducing the emission of greenhouse gas. Hence, knowing the compressibility factor of carbon dioxide is of a vital significance. Compressibility factor (Z-factor) is traditionally measured through time consuming, expensive and cumbersome experiments. Hence, developing a fast, robust and accurate model for its estimation is necessary. In this study, a new reliable model on the basis of feed forward artificial neural networks is presented to predict CO2 compressibility factor. Reduced temperature and pressure were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with pre-existing models, both statistical and graphical error analyses were employed. The results indicated that the proposed model is more reliable and accurate compared to pre-existing models in a wide range of temperature (up to 1,273.15 K) and pressure (up to 140 MPa). Furthermore, by employing the relevancy factor, the effect of pressure and temprature on the Z-factor of CO2 was compared for below and above the critical pressure of CO2, and the physcially expected trends were observed. Finally, to identify the probable outliers and applicability domain of the proposed ANN model, both numerical and graphical techniques based on Leverage approach were performed. The results illustrated that only 1.75% of the experimental data points were located out of the applicability domain of the proposed model. As a result, the developed model is reliable for the prediction of CO2 compressibility factor. Keywords: Carbon Dioxide, Compressibility Factor, Artificial Neural Network, Equations of State, Leverage Approach

provide the solution in a shorter time [4]; however, they sometimes impose additional complications on the problem, lead to errors and their accuracy, especially close to and beyond the experimental data used for the curve fitting is under question [7]. Compressibility factor should be determined with the highest possible accuracy to avoid the error propagation in the prediction of other properties such as gas density, viscosity, isothermal compressibility, thermal conductivity and gas formation volume factor for carbon storage and injection rate calculations to enhance oil recovery [8]. The exact equations or correlations for the calculation of the mentioned properties require direct knowledge of the compressibility factor. Hence, a robust fast method is required to predict the compressibility factor of carbon dioxide quickly and precisely. Artificial neural networks (ANN’s) have attracted much attention in petroleum industry for solving nonlinear complex problems and handling complicated systems since they are capable of allowing large number of weights and biases making them flexible for fitting the parameters. We developed a simple and robust predictive model based upon artificial neural networks to estimate the compressibility factor of carbon dioxide in a wide range of temperature and pressure. To the best of authors’ knowledge, an ANN model for the prediction of compressibility factor of carbon dioxide, especially on a large data bank covering wide ranges of temperature and pressure, has not been proposed before. The objectives of this study are as follows: 1. Collecting a large data bank from literature sources which covers compressibility factor of carbon dioxide in a wide range of pres-

INTRODUCTION After primary and secondary oil recovery, usually more than half the original oil in place (OOIP) remains in light and medium oil reservoirs. Among enhanced oil recovery (EOR) methods, CO2 injection has gained much popularity due to its considerable effect on oil recovery enhancement and reducing the amount of greenhouse gas emissions by its sequestration in depleted oil reservoirs [1,2]. Hence, the compressibility factor of carbon dioxide is of a high importance in the oil and gas industry for CO2 compression, pipeline design, material balance calculations and surface facilities design for EOR and sequestration purposes [3]. Compressibility factor is normally determined through experimental procedures in PVT laboratories. This process is costly, demanding and lengthy [4], so alternatives such as equations of state (EOS) or empirical correlations are preferred for estimation of compressibility factor [5]. Equations of state usually result in a cubic equation for compressibility factor, and the root which minimizes the free Gibbs energy has to be selected as the solution. This mathematical procedure adds to the complexities involved in the engineering calculations [6]. Correlations, on the other hand, are much easier to use and † To whom correspondence should be addressed. E-mail: [email protected], [email protected] Copyright by The Korean Institute of Chemical Engineers.



E. Mohagheghian et al.

sure and temperature 2. Developing a reliable, fast and accurate model based on artificial neural networks predictive modeling to estimate compressibility factor of CO2 3. Comparing the results of the model with those of equations of state and correlations via statistical and graphical error analyses to evaluate the accuracy and reliability of the model 4. Identifying the outliers and the suspected data points by means of leverage approach 5. Investigating the effect of each individual input parameter on the Z-factor of CO2 by means of relevancy factor COMPRESSIBILITY FACTOR Gas deviation or compressibility factor (Z-factor) is defined as the ratio of the actual volume of a gas at a given pressure and temperature to the ideal volume and is a measure of how far the gas deviates from ideal behavior [9]. Some thermodynamic properties such as density, isothermal compressibility and viscosity can be calculated by means of compressibility factor. According to the law of corresponding states proposed initially by van der Waals, all gases deviate from ideality to the same extent at the same reduced pressure Pr and reduced temperature Tr [4]. This can be represented in the following mathematical form: Z=f(Tr, Pr)


where T Tr = ----Tc


P Pr = ----Pc


where Tc and Pc are the critical temperature and pressure, respectively. At and beyond the critical point, the liquid and gaseous phase of a pure substance like carbon dioxide cannot be distinguished. Equations of state and empirical correlations are two common methods of compressibility factor estimation. 1. Equations of State Since 1873, many equations of state have been developed and modified for estimation of PVT properties of gas and crude oil systems. The most well-known type of EOS widely being used in petroleum industry is the van der Waals type, which is of the following general format: RT a P = ---------- − -------------------------v − b v2 + uv − w2


In a two-parameter EOS, u and w are correlated with b, while in a three or four-parameter EOS, u and w are related to b and a third or fourth parameter. A two-parameter EOS is usually enough for the prediction of vapor pressure; however, including a third parameter in an EOS, which is mostly derived by matching the experimental data of saturated liquid density, increases the EOS flexibility and its capability for prediction of liquid and vapor density as well as vapor pressure [10]. To calculate the PVT properties, it is preferred to rewrite the above June, 2015

equation in terms of compressibility factor as follows: Z3−(1+B−U)Z2+(A−BU−U−W2)Z−(AB−BW2−W2)=0


where dimensionless parameters A, B, U and W are described as functions of only pressure and temperature for a pure component as below: aP A = -------------2 (RT)


bP B = ------RT


uP U = ------RT


wP W = ------RT


where the parameters a and b are represented by: 2 2

RT a = Ωa -----------c α(Tr, ω ) Pc


RT b = Ωb ---------c Pc


where α is the temperature dependency term and ω is the acentric factor [10]. In the following, six of the most well-known equations of states will be reviewed. 1-1. Redlich-Kwong (RK) EOS [11] u=b, w=0


Ωa=0.42747, Ω b=0.08664


1 α = --------Tr


1-2. Soave-Redlich-Kwong (SRK) EOS [12] Soave [12] replaced the term 1/ Tr in RK EOS by a more general term as follows: α = [1+ m(1− Tr)]



where, m=(0.48+1.574ω −0.176ω 2)/1.18


1-3. Peng-Robinson (PR) EOS [13] u=2b, w=b


Ωa=0.457235, Ω b=0.077796


The temperature dependency function is similar to Eq. (15). The authors correlated m as: m=0.3796+1.485ω −0.1644ω 2+0.1667ω 3


1-4. Schmidt-Wenzel (SW) EOS [14] u=(1+3ω)b, w2=3ωb2




Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature

where q is the smallest positive root of the following equation: (6ω +1)q3+3q2+3q−1=0

related with acentric factor as follows: (22)


0.361 Ωw = --------------------------1+ 0.0274ω 2

1 η = --------------------3( 1+ qω )


and Ω b=ηq


The temperature dependency function is similar to Eq. (15). The authors correlated m as: m≡m1=m0+0.01429(5Tr−3m0−1)2

for ω ≤0.4



for ω ≥0.55


m0=0.465+1.347ω −0.528ω2

for ω ≤0.3671



for ω >0.3671



and for intermediate values of 0.4

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