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Crude Distillation Process Simulation Using Unisim Design Simulator

The paper deals with the simulation of the crude distillation process using the Unisim Design simulator. The necessity of simulating this process is argued both by considerations related to the design of the crude distillation column, but also by considerations related to the design of advanced control systems. In order to use the Unisim Design simulator to simulate the crude distillation process, the identification of the simulators used in Romania and an analysis of the PRO/II, HYSYS, and Aspen HYSYS simulators were carried out. Analysis of the simulators for the crude distillation process has allowed the authors to elaborate the conclusions of the success of the crude modelling. A first aspect developed by the authors is the implementation of specific problems of petroleum liquid-vapors equilibrium using Unisim Design simulator. The second major element of the article is the development of the methodology and the elaboration of the simulation program for the crude distillation pr...

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Crude distillation unit (CDU) is a complex process in the field of separation which produces wide range of products at different stages under different conditions.The products of the process were heavy naphtha,kerosene,diesel,atmospheric gas oil and reduced crude.However,the dynamic and multivariable nature with strict quality was makes it difficult to operate the process units steadily.More,the dynamics of CDU are complex due to its complex vapor-liquid equilibrium relationships.It is necessary to predict the new steady-state values for any changes in the operating conditions.This research aims to develop a steady-state model for CDU based on the fundamental modeling approach. The simulation was carried out in Aspen Plus.The effect of feed flow rate,feed composition and steam flow rate on product compositions and tray temperatures were studied.The results were compared with the data available in the literature and the accuracy of the model has been proved.

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Steady-state and dynamic simulation play important roles in investigation of refinery units. Therefore, simulation can help this investigation and behavior assessment. In this paper, simulation was done by commercial software. In fact, because of solving many state equations simultaneously and using control theory, dynamic simulation has more significant impact than steady-state simulation. Flow, pressure, temperature and level (FPTL) were controlled by Proportional-Integral-Derivative (PID) controllers in the unit. The case study is Kermanshah Refinery. The behavior of the FPTL controllers in dynamic regime were observed after the changing of the crude oil feed flow rate by 3% for 5 h. ASTM D86 boiling points (compositions) of two simulations were compared with experimental data. Finally, system sensitivity to inputs variables was investigated in the MATLAB®/SimulinkTM by transferring the dynamic results. Transient responses to changes such as feed temperature, feed flow rates, st...

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Steady state models for a multi-component crude distillation column have been developed from the MESH equations. The equations developed are capable of predicting the concentrations (mole fractions) and temperature of any component/fraction of interest from the mixture on the trays of the column. The accuracy of the models was ascertained with plant data of the crude distillation unit of the Port-Harcourt Refinery. The models applied to this distillation column produced a set of forty eight coupled algebraic equations for any component/fraction of interest. These equations were transformed into a matrix and solve by matrix inversion using the Mathlab solver. The model results of the concentrations and temperatures for five components compared favorably with output values from the distillation unit with maximum deviations of 8.33% and 6.62% respectively. The developed model also accurately predicted the tray of withdrawal of the five components/fractions in the distillation column. The models were therefore used to simulate the effects of feed flow rate and feed tray position on the efficiency of the distillation column.

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Numerical tuning in reservoir simulation: it is worth the effort in practical petroleum applications

• Technical Paper
• Published: 04 January 2019
• Volume 41 , article number  59 , ( 2019 )

Cite this article

• G. Avansi 1 ,
• V. Rios 1 &
• D. Schiozer 1  

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Giant carbonate oil fields in Brazilian pre-salt area present a high level of heterogeneity. Despite the fact that geoscientists aim at modelling these complex models in a high-block cell resolution, it is important to reduce the computational effort and speed up some processes such as forecasting the production during a risk analysis in a probabilistic approach. This practice must be evaluated to keep numerical and geological consistency of reservoir models. As a result, creating a procedure to assist petroleum scientists and engineers so as to reduce computational time, without losing accuracy, is the main goal of this proposed work. A methodology based on numerical parameter evaluation, tuning technique, diagnosis and application is defined to provide a robust and effective starting point of the numerical control settings during reservoir simulation model runs. All of the analyses are based on a good understanding of the reservoir heterogeneities (geologists) and characteristics (reservoir engineering) that may result in reduced central processing unit time, number of time-step cuts, solver failures and material balance compared to the default approach considered for these problems. In addition, to check the consistency of the proposed procedure, one building risk curve application for two case studies was selected, assuming that we are executing the tuning and the application in sequence of a common reservoir application. The results of the two case applications under our proposed assumptions showed that it is possible to (1) speed the simulation runs up to three times in comparison with the base case which uses the default numerical parameters of a commercial simulator, (2) reduce the number of cuts and failures and (3) control the material balance equation error within a previously defined tolerance. Despite decreasing the simulation run time in practical reservoir study applications, we are also avoiding bad combination of numerical inputs which can result in unfeasible reservoir numerical models. To conclude, depending on the amount of applications, number of runs and complexity of reservoir numerical model, the results showed that we can save time in the reservoir engineer’s routine activities while maintaining the consistency of reservoir models by dedicating an initial time to better understand and optimise the numerical parameters. Even though the vast majority of reservoir numerical models are created in high-resolution grid cells to maintain the level of heterogeneity and, consequently, increase the computational effort, more suitable numerical model quantifications should be conducted in order to reduce the running time. Nonetheless, a consistency in the reservoir model must be maintained during the entire procedure. Thus, the most important step before starting any reservoir simulation workflow is to set the numerical parameters to avoid convergence problems, to keep the consistency of the numerical simulation results (approaching the solution of nonlinear equations to the true solution) and to assist geoengineering in starting the reservoir numerical parameters consciously. In addition, we can make sure that appropriate controls of the numerical model are then being used efficiently as a practice in reservoir simulation studies, mainly before starting to run a large amount of simulations.

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Reservoir Optimization and Simulation Modeling: A Case Study

Geological uncertainty and effects of depositional sequence on improved oil recovery processes.

Ngoc Nguyen, Cuong T. Q. Dang, … Long X. Nghiem

Geostatistical facies simulation with geometric patterns of a petroleum reservoir

Paulo Roberto Moura de Carvalho, João Felipe Coimbra Leite da Costa, … Luiz Eduardo Seabra Varella

Abbreviations

Root-mean-square

m th grid cell

Time increment

Production rate from the grid cell (m 3 /day)

Formation volume factor

Volume of the grid cell (m 3 )

Porosity of the grid cell

Bubble point pressure (kPa)

Time-step cuts

Solver convergence failures

Cumulative oil production (m 3 )

Cumulative water production (m 3 )

Cumulative gas production (m 3 )

Material balance equation

Solver iterations

Newtonian cycles

Number of time-step cuts

Central processing unit (s)

• Numerical tuning

Standard deviation

n th time-step

Fluid phase (oil, water or gas)

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Acknowledgements

This work was conducted with the support of Energi Simulation and in association with the ongoing project registered as “BG-32—Análise de Risco para o Desenvolvimento e Gerenciamento de Campos de Petróleo e Potencial uso de Emuladores” (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil, under the ANP R&D levy as “Compromisso de Investimentos com Pesquisa e Desenvolvimento”. The authors also thank UNISIM, DE-FEM-UNICAMP, CEPETRO and PETROBRAS for supporting this work and CMG, Emerson and Schlumberger for software licenses. We would like to thank Samuel Ferreira de Mello (researcher at UNISIM’s Group with expertise in PVT modelling process) for modelling the fluid.

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Department of Energy, School of Mechanical Engineering, University of Campinas, Campinas, São Paulo, Brazil

G. Avansi, V. Rios & D. Schiozer

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Avansi, G., Rios, V. & Schiozer, D. Numerical tuning in reservoir simulation: it is worth the effort in practical petroleum applications. J Braz. Soc. Mech. Sci. Eng. 41 , 59 (2019). https://doi.org/10.1007/s40430-018-1559-9

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Received : 05 April 2018

Accepted : 25 December 2018

Published : 04 January 2019

DOI : https://doi.org/10.1007/s40430-018-1559-9

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UNISIM-I-D: Case Study for Selection of Production Strategy

The case study was organized by Gaspar, A.T., Maschio, C., Santos, A.A., Avansi, G.D., Filho, J.H. and Schiozer, D.J. (2013) . The comparative study should consider deterministic and probabilistic approaches.

Gaspar, A.T.F.S., Santos, A.A.S., Avansi, G.D., Hohendorff Filho, J. C.V., Schiozer, D.J., “UNISIM-I-D: Benchmark Studies of Oil Field Development Strategy Selection”. International Journal of Modeling and Simulation for the Petroleum Industry, v.9, pp. 47-55, April 2015, Brazil.

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