Production performance analysis of Sarkhoon gas condensate reservoir

Alavi, Farzad; Mowla, Dariush; Esmaeilzadeh, Feridun

doi:10.1016/j.petrol.2010.10.002

Cited by 13 publications

(10 citation statements)

References 6 publications

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“…Therefore, the phase behavior analysis method of condensate gas based on the equilibrium theory cannot simulate the real nonequilibrium process of oil and gas system near the wellbore zone [12][13][14][15][16]. Moreover, a conventional phase behavior experiment based on the equilibrium theory needs to consume large amounts of time and the experimental analysis process is not consistent with the actual gas field exploitation [17][18][19]. Currently, there are few relevant experimental reports on the phase behavior of gas condensate with gaseous water in the process of nonequilibrium pressure drop.…”

Section: Introductionmentioning

confidence: 99%

Study on Nonequilibrium Effect of Condensate Gas Reservoir with Gaseous Water under HT and HP Condition

Hou

Luo

Sun

et al. 2014

Journal of Chemistry

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When a condensate gas reservoir with gaseous water under high temperature and high pressure condition is producing, the gaseous water and nonequilibrium effect will have great influences on the phase behavior of condensate oil and gas system and the accumulation of condensate liquid near the wellbore area. Therefore, a series of experiments were performed to investigate phase behavior of the condensate gas reservoirs with gaseous water using a PVT cell, in which the constant volume depletion process of nonequilibrium pressure drop and equilibrium pressure drop within near wellbore zone was simulated. And using the modified PR EOS, PR EOS, and nonequilibrium effect theory, the authors calculated the content of condensate oil and condensate liquid of the nonequilibrium pressure drop and equilibrium pressure drop and compared the calculated results with the experimental data. The results show that the modified PR EOS combined with nonequilibrium effect theory is more suitable for representing phase behavior characteristics of the development process of condensate gas reservoir containing gaseous water, with the average relative error of 4.49%. Furthermore, choosing the appropriate exploiting opportunity and properly increasing the nonequilibrium effect are helpful to increase condensate oil and water recovery.

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Section: Introductionmentioning

confidence: 99%

Study on Nonequilibrium Effect of Condensate Gas Reservoir with Gaseous Water under HT and HP Condition

Hou

Luo

Sun

et al. 2014

Journal of Chemistry

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“…Proper thermodynamic knowledge of phase behavior in this type of reservoirs is essential in order to predict the recovery factor and CGR. Several approaches are available in the literature for performance analysis of retrograde gas condensate reservoirs. − Thomas et al (2009) proposed a systematic strategy for performance prediction of gas condensate reservoirs based on relative permeability and two-phase dynamic measurements …”

Section: Introductionmentioning

confidence: 99%

“…Although measuring nonlinear parameters such as CGR and dewpoint pressure is viable through some tests (e.g., Constant Composition Expansion (CCE) and Constant Volume Depletion (CVD)), however, the laboratory and field tests are quite costly and people and time intensive. , As quick, inexpensive and efficient alternatives, other techniques that include theoretical or/and laboratory tests combined with artificial intelligent systems were suggested by a number of researchers. − Several models have been reported in the literature to predict flow and thermodynamic behaviors of gas condensate reservoirs. ,,,− Clearly, prediction of production rate is central in design of wellhead equipment and surface facilities. Alavi et al (2010) used a modified Peng-Robison equation of state in their model combined with some experiments to determine physical properties of gas condensate mixtures.…”

Section: Introductionmentioning

confidence: 99%

“…Alavi et al (2010) used a modified Peng-Robison equation of state in their model combined with some experiments to determine physical properties of gas condensate mixtures. Their main goal was developing an appropriate design to optimize the amount of the produced condensate in a gas reservoir when pressure decreases below the dewpoint . Jokhio et al (2002) employed the Whitson and Fevang’s model to estimate the extent of produced liquid condensate. , Al-Farhan et al (2006) examined the changes in CGR at various process conditions with considering a three-stage separation process optimized using a four-layer back propagation (BP) artificial neural network (ANN) .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

et al. 2012

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Condensate-to-gas ratio (CGR) plays an important role in sales potential assessment of both gas and liquid, design of required surface processing facilities, reservoir characterization, and modeling of gas condensate reservoirs. Field work and laboratory determination of CGR is both time consuming and resource intensive. Developing a rapid and inexpensive technique to accurately estimate CGR is of great interest. An intelligent model is proposed in this paper based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) technique. The PSO-ANN model was evaluated using experimental data and some PVT data available in the literature. The model predictions were compared with field data, experimental data, and the CGR obtained from an empirical correlation. A good agreement was observed between the predicted CGR values and the experimental and field data. Results of this study indicate that mixture molecular weight among input parameters selected for PSO-ANN has the greatest impact on CGR value, and the PSO-ANN is superior over conventional neural networks and empirical correlations. The developed model has the ability to predict the CGR with high precision in a wide range of thermodynamic conditions. The proposed model can serve as a reliable tool for quick and inexpensive but effective assessment of CGR in the absence of adequate experimental or field data.

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“…This problem called condensate blockage [3] reduces permeability in the place that condensate is formed [6,8,10] because liquid has lower mobility than the gas and high liquid-to-gas ratio [7,11]. Due to these specifications some of the liquid in the reservoir is lost [11].…”

Section: Introductionmentioning

confidence: 99%

Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study

Ebadi¹,

Ahmadi²,

Gerami³

et al. 2012

IJCA

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Condensate-to-gas ratio (CGR) plays a significant role in sales potential assessment of both gas and liquid, design of the required surface processing facilities, and reservoir characterization and modeling in gas-condensate reservoirs. Precise field and laboratory determination of the CGR is time and people intensive. Developing a rapid and inexpensive technique for accurate estimation of the CGR is inevitable. To tackle this issue, an intelligent model was proposed and the purpose of this paper is to illustrate how Fuzzy Decision Tree (FDT), which is an automatic method of generating fuzzy rules, can predict the condensate gas ratio, as a vital parameter in order to design the necessary wellhead production facilities in retrograde gas reservoir. As the FDT method is strongly based on applying widely and effectively the concept of ambiguity and furthermore, to do this project more accurately and less dependent on experts' knowledge, it was decided to gain from piecewise linear membership functions (MFs) whose parameters have automatically been dedicated through calculating a very special method of possibility density function (pdf). In conclusion, according to the calculated result, it is possible to exploit this method for CGR prediction field wide. The Fuzzy model was evaluated using the experimental data combined with some PVT data from the open literature. The model predictions were compared with the data from the actual field data and experimental runs. Results from this study indicate that the developed model can predict the CGR with good precision. The proposed model can serve as a reliable tool for quick and cheap but effective assessment of the CGR in absence of any adequate laboratory or field data.

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Production performance analysis of Sarkhoon gas condensate reservoir

Cited by 13 publications

References 6 publications

Study on Nonequilibrium Effect of Condensate Gas Reservoir with Gaseous Water under HT and HP Condition

Study on Nonequilibrium Effect of Condensate Gas Reservoir with Gaseous Water under HT and HP Condition

Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study

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