Effect of Crude Oil Properties on the Transport Profile inside Pipeline using Computational Fluid Dynamics Simulation.

Transcription

1 The 4 th Asian Conference on Innovative energy & Environmental Chemical Engineering The Ocean Resort, Yeosu, Korea, November 9-12, 2014 FLD-6 Effect of Crude Oil Properties on the Transport Profile inside Pipeline using Computational Fluid Dynamics Simulation. Wanwisa Rukthong 1, Pornpote Piumsomboon 1,2, Wichapun Weerapakkaroon 3, Benjapon Chalermsinsuwan 1,2,* 1 Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Patumwan, Bangkok 10330, Thailand 2 Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University, 254, Phayathai Road, Patumwan, Bangkok 10330, Thailand 3 PTT Research & Technology Institute, PTT Public Company Limited, 555 Vibhavadi Rangsit Road, Chatuchak, Bangkok 10900, Thailand * benjapon.c@chula.ac.th, tel : Abstract Transporting crude oil inside pipeline is the common process in petroleum industry. Crude oil from difference sources has difference properties due to terrains and climates which cause the transport profile to change during the operation. In this study, computational fluid dynamics model was developed. The governing equations were employed to study the effect of crude oil properties on the transport profile such as crude oil density and viscosity. A good agreement between numerical model and commercial software suggests that the proposed numerical scheme is suitable for simulating the transport profile in pipeline and predicting the phenomena for any other conditions 1. Introduction Pipelines are generally used process equipment for transport crude oil from reservoir to station. They run throughout the world because there are a number of crude oil sources. Crude and refinery oil pipelines in Canada have a length of 23,564 km and petroleum product pipelines in United states have 244,620 km long. Crude oil from different sources has different physical and chemical properties due to a variety of geological environments 1, terrains and climates, which cause the transport profile to change during the operation. In the petroleum industry, classi cation of crude oil can be feasibly done based on API gravity 2. Canadian basins has the density of the oils ranging from 17.5 to 54.0 API 3 while Alif Field, Marib-Shabowah Basin has a variety of API gravity values in the range of 15.0 to 58.7 API 4. Table 1 summarizes the changing of API gravity operating on different crude oil sources. From the table, API gravity has an effect on other crude oil properties which are crude oil density and viscosity 5. These parameters are important governing parameters for predicting the transport phenomena which mainly control the flow of crude oil. However, crude oil properties do not only depend on API gravity but also reservoir temperature. Heat capacity and thermal conductivity are parameters mainly control the heat transfer through pipelines. All the parameters then are used in designing of the process or production facilities 6. Type of Malaysia crude AG1 BK2 DG3 PN4 TP5 Table 1. Physical properties of some Malaysian oilfields 5. API gravity Density Viscosity (g/cm o C) o C) Wax content (wt%)

2 Heat and mass transfer of crude oil through pipelines is a very complex process because there are many flow parameters that affect transport profile. In order to better understand behavior of crude oil flow in pipeline, the knowledge in computational fluid dynamics (CFD) is required for predicting the phenomena. It is a branch of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems that involve fluid flows. Yu et al. 1 created a physical model to simulate the heat transfer and oil flow of a buried hot oil pipeline under normal operation. Huang et al. 7 used numerical methods to study wax deposition in oil/water stratified flow through a channel. However, there still was no research study about the effect of crude oil properties on transport profile using numerical model. In this study, the aim is to develop in-house computational fluid dynamics model to study the effect of crude oil properties, crude oil density, viscosity, heat capacity and thermal conductivity on the transport profile. This information will be useful for protecting and solving problems which may occur during transport process such as, appearance of wax. 2. Methodology 2.1 Computational fluid dynamics model Computational fluid dynamics (CFD) has an important role for fluid mechanic. It is very useful tool to solve the fluid flow problems predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by solving the mathematical equations which govern these processes using a numerical technique. To develop CFD model, governing equations which are in partial differential form are converted into the algebra form with finite volume numerical solution techniques. The conservative form of all flow equation for twodimensional system can usefully be written in the following form 8 : u v S (1) t x y x x x y Where the transient term and the convective term in x and y directions are on the left hand side and the diffusive term in x and y directions and the source term are on the right hand side ( is density, u is x-velocity, v is y-velocity, is diffusion coefficient and S is source term). When equals to 1, equation (1) will be the continuity equation. In addition, when replaced by u, v and T, equation (1) will become the conservation of momentum in x and y directions and the conservation of energy respectively. 2.2 Numerical simulation In this study, SIMPLE algorithm (Semi Implicit Method Pressure-Linked Equation) which is an iterative solution strategy for the calculation of pressure on the staggered grid arrangement was employed. First order upwind differencing scheme and Tri-Diagonal Matrix Algorithm (TDMA) were used to solve the cell face problem and to calculate the results of linear algebraic equations, respectively. After developed all above equations and CFD procedure, they were written as standalone computer program code. The parameters of CFD simulation test are listed in Table 2 except the physical property parameters, crude oil density, viscosity, heat capacity and thermal conductivity. Fig. 1 shows the diagram of straight pipe model. Table 2. The parameters for CFD simulation test. Description Value Unit Pipe diameter 0.15 m Pipe length 1500 m Flow rate 133 barrel/day Surrounding temperature 35 o C Operating time 3000 second

3 To study the effect of crude oil properties on the transport profile inside pipeline, numerical simulation was tested under laminar unsteady state flow conditions. Single phase liquid flow was simulated with the 30,000 cell straight pipe Fig. 1. The schematic diagram of straight pipe model. model after the grid independency testing. The simulation test operated until reaching the steady state. The results from developed CFD simulation program will be verified with the commercial program ANSYS FLUENT. 2.3 Design of experiments The first important step in design of experiment is the selection of suitable factors and their levels. In this study, four physical properties factors Table 3. The physical properties factors and their levels. Factor Symbol Levels 1 2 Dynamic viscosity (kg/ms) A Density (kg/m 3 ) B Heat capacity (kj/kg o C) C Thermal conductivity (kj/m o C) D (dynamic viscosity, density, heat capacity and thermal conductivity) are considered in two levels as shown in Table 3. With the 2 4 design, the low and high levels of factors are denoted by the code -1, 1 respectively. The parameters and their levels are selected based on the available literature and some experiments of QH crude oil 9. For the 2 4 factorial design, orthogonal array is designed and the response data is then obtained from a single replicate. In this study, the location of wax appearance represented by pipe distance is used as the response, based on the wax appearance temperature (47 o C) for QH crude oil. The 16 runs are made in random order to avoid the systematic bias. The 2 4 factorial design is shown in Table 4. Table 4. The 2 4 factorial experimental design based on the codes levels and their responses. Run A B C D Distance

4 3. Results and discussion 3.1 Numerical result - Transport profile in crude oil pipeline. According to the 2 4 factorial experimental design, 16 runs of simulation were carried out in order to obtain 16 flow patterns represented by distribution profiles of crude oil flow in pipeline. The results of simulation showed that those all 16 profiles had quite similar patterns. One example of temperature and velocity profile are shown in Figs. 2 and 3, respectively, consisting of line graph and contour plot. The temperature of the oil in the pipe center had maximum value and decreased along the radial direction of the pipe until reaching the pipe wall (the temperature of the pipe wall was the same as surrounding temperature). The velocity or flow rate of crude oil in pipeline was quite constant along the pipeline because of the constant cross-section area of pipeline. Furthermore, decreasing of crude oil temperature near the pipe wall lead to a rapid increase of the oil viscosity near the wall, which made the crude oil velocity to drop along the radial of pipe as can be seen in velocity contour plot in Fig. 3. These results were in agreement with previously reported trends 8. The difference between each profile was the decreasing rate of crude oil temperature. Comparison between temperature gradient profiles in crude oil pipeline obtained from developed CFD simulation program and commercial program ANSYS FLUENT after the system reached the steady state found that the obtained temperature gradient profiles from both programs are quite similar. The comparison results were previously presented in technical report 10. Therefore, the model accuracy was proved and confirmed Fig. 2. Temperature gradient of crude oil in pipeline from developed CFD simulation program (Each curve represents the various radial of pipe) Fig. 3. U-velocity gradient of crude oil in pipeline from developed CFD simulation program (Each curve represents the various radial of pipe)

5 3.2 The 2 4 factorial experimental design analysis. All 16 responses result with a single replicate are shown in Table 4. ANOVA is used to analyze the result of the simulation. The statistical analysis of the results obtaining with a confidence level of 95% or p-value equals to 0.05 is shown in Table 5. Consider the p-value, B and C factors had statistically significant effect at 95% confidence level. It is found that the heat capacity had more significant effect than the density of crude oil. The residual error term combines the effect of uncontrollable factors excluded from the simulation results 11. The obtained mathematic model is: * B * C (2) y Where y is the location of wax appearance represented by pipe distance, B and C are the actual factors of crude density and heat capacity, respectively. Table 5. Analysis of Variance (ANOVA). Degree of Source Mean Sum of Squares freedom Square (DF) F-value P-value Model E < B E E < C E < Residual E E-09 Total The effect of density (B) and heat capacity (C) on pipe distance. As mention above, heat capacity was more significant factor than density which consistently can be observed in Fig. 4. Both the factors had negative effect on pipe distance. The pipe distance decreased dramatically as the heat capacity increased whereas the pipe distance slightly decreased as the density increased. The heat capacity of crude oil is the quantity of heat or heat rate required to change the temperature of a kilomole of crude oil by 1 degree Celsius 12. Heat capacity increases the amount of energy required to decreases crude oil temperature along the pipeline. Thus, the temperature Fig. 4. Main factors effects: density (B) and heat capacity (C). decline rate of crude oil decrease as heat capacity increased 8 and resulting in slower waxing time. Density is defined as mass divided by volume which is a specific property of matter and is also depended on temperature of matter. Consider the heat equation: Q mc T (3) Where m is mass of crude oil, c is heat capacity and T is the change in temperature. When m is replaced by density and volume, as a result, the quantity of heat or heat rate required to

6 change the temperature increases with an increasing in density and causing the slower of waxing time. Conversely, when density of oil increases, it causes the decreasing in oil velocity because it needs more driving force to transfer bulk of crude oil along the pipeline. 4. Conclusions In this study, the aim is to investigate the effect of crude oil properties (dynamic viscosity, density, heat capacity and thermal conductivity) on the transport profile by developing computational fluid dynamics model. Single phase liquid flow was simulated in straight pipe model. 16 runs of simulation were carried out based on the 2 4 factorial design. It is found that all of 16 flow distribution trends obtained from the developed CFD simulation program are quite similar. The influences of various physical properties on transport profile were analyzed using the 2 4 factorial design. The result showed that the crude oil heat capacity and density had statistical significance at 95% confidence level. The heat capacity had the most significantly effect on wax appearance. The obtained mathematical equations presented the reliable result with high R-squared value. To apply this knowledge in real situation, the physical properties of crude oil should be adjusted by mixing with some lighter/heavier crude oil or other chemical compositions/solvents 13 to prevent the occurrence of wax inside the pipeline. Acknowledgements The Scholarship from the Graduate School, Chulalongkorn University to commemorate the 72nd anniversary of his Majesty King Bhumibala Aduladeja for financial support of this study, the Grant from PETROMAT and PTT Public Company Limited are gratefully acknowledged. References 1. Yu et al. Numerical simulation of a buried hot crude oil pipeline under normal operation. Applied Thermal Engineering, 30:17-18 (2010). 2. Kelesoglu et al. Flow properties of water-in-north Sea heavy crude oil emulsions. Journal of Petroleum Science and Engineering, 100:14-23 (2012). 3. Stasiuk et al. Fluorescence micro-spectrometry of synthetic and natural hydrocarbon fluid inclusions: crude oil chemistry, density and application to petroleum migration. Applied Geochemistry, 12: (1997). 4. Hakimi et al. Geochemical characteristics of some crude oils from Alif Field in the Marib- Shabowah Basin, and source-related types. Marine and Petroleum Geology, 45: (2013). 5. Kelechukwua et al. Prediction of wax deposition problems of hydrocarbon production system. Journal of Petroleum Science and Engineering, 108: (2013). 6. El-hoshoudy et al. New correlations for prediction of viscosity and density of Egyptian oil. Fuel, 112: (2013). 7. Huang et al. Wax Deposition Modeling of Oil/Water Stratified Channel Flow. American Institute of Chemical Engineers AIChE, 57: , (2011). 8. Versteeg et al. An Introduction to Computational Fluid Dynamics: The Finite Volume Method. 2 nd ed. Harlow: Pearson Education, Guozhong et al. Study on the wax deposition of waxy crude in pipelines and its application. Journal of Petroleum Science and Engineering, 70:1-9 (2010). 10. Rukthong et al. Development of CFD Simulator for Multi-phase Flow System. 1/2014 Chemical Technology Seminar. Bangkok, Thailand, Kareem et al. Isobaric speci c heat capacity of natural gas as a function of speci c gravity, pressure and temperature. Journal of Natural Gas Science and Engineering, 19:74-83 (2014). 12. Bai et al. A Novel Scheduling Strategy for Crude Oil Blending. Chinese Journal of Chemical Engineering, 18: (2010). 13. Valinejad et al. An experimental design approach for investigating the effects of operating factors on the wax deposition in pipelines. Fuel 106: (2013)