Characteristics of Gas Condensate

Fasesan, S. O.; Olukini, O. O.; Adewumi, O. O.

doi:10.1081/lft-120016924

Cited by 13 publications

(9 citation statements)

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“…This isothermal condensation due to the reduction of pressure below the dewpoint pressure of the original hydrocarbon mixture is called retrograde condensation. [1][2][3][4]11 Gas condensate reservoirs generally produce in the range of 30−300 STB/MMSCF (standard barrels of liquid per million standard cubic feet of gas). 5,12,15,23,33−38,41−47 The depth of majority of the reported retrograde gas condensate fields ranges from 4000−10,000 ft (∼1200−3000 m).…”

Section: Retrograde Gas Condensate Reservoirsmentioning

confidence: 99%

“…Gas condensates are highly valued hydrocarbon resources. Gas condensate reservoirs are characterized by their complicated flow regimes and intricate thermodynamic behaviors. − Multi-phase flow of both gas and liquid condensates is expected during the production of a gas condensate reservoir with dynamic phase behavior from the porous rock to the surface facilities. The Arun field in Indonesia, South Pars field in Iran, and Greentown field in the USA with condensate-to-gas ratio (CGR) ranging from 10 to 300 STB/MMSCF are very large gas condensate reservoirs in the world. − Condensate production due to pressure drop in the reservoir improves project economics through condensate sales as produced liquid condensate is the only potential source of revenue in the severe case of a poor gas market. − …”

Section: Introductionmentioning

confidence: 99%

“…Gas condensate reservoirs differ from conventional gas reservoirs in their thermodynamic and flow behaviors. Prediction and optimization of hydrocarbon recovery in this type of reservoirs require careful reservoir analysis, development plan, and project management. − Isothermal condensation of liquids lowers the gas deliverability in the reservoir due to condensate blockage. ,, Low gas relative permeability in the gas–liquid region near the production well is the main reason behind reduction of gas flow rate. ,− Proper production strategy for gas condensate reservoirs can minimize or even avoid condensate blockage, however the prediction of key parameters like CGR is necessary. High accuracy in prediction methods is needed in order to provide an overall acceptable estimate of the performance of gas condensate reserves before the implementation of a proper gas recovery plan.…”

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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Section: Retrograde Gas Condensate Reservoirsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Gas condensate reservoirs are significantly different from conventional reservoirs in terms of thermodynamic properties and flow characteristics. Reservoir performance prediction and optimization require careful analysis and project management (Fasesan et al 2003;Bozorgzadeh et al 2006). During production process, reservoir pressure falls below dew point and a liquid phase rich in heavy ends drops out of solution in near wellbore zone.…”

Section: Introductionmentioning

confidence: 99%

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

et al. 2018

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Hydrocarbon production from a gas condensate reservoir has a major concern to the petroleum industry around the world. Due to pressure decline in a reservoir, liquid drops out of solution and cause a significant reduction of well productivity. As a result, huge amount of valuable oil stays underground. The proper production management for a gas condensate reservoir minimizes or eliminates the blocking problems. Optimization of condensate recovery can be achieved through appropriate selection of production and injection rates. However, determination of the optimal rates is a complex problem, involving many factors including geological uncertainty, rock petrophysical characteristics, fluid properties, economic costs, and technical ability. In this research, nature-inspired optimization algorithms are employed to evaluate the potential of production boost. Numerical testing and comparative study revealed that investigated natural-inspired algorithms outperform widely reported in the literature optimization methods and provide a higher quality solutions. The primary concern of this research is to develop an optimal gas condensate production strategy in terms of economic efficiency by utilization of nature-inspired optimization algorithm and providing their ability to generate better quality solution than engineering approach as well as gradient optimization.

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“…However, in fixed chokes, the size of the opening is fixed, and it is not possible to change the fluid flow rate and wellhead pressure. Once a well is designed to produce for a long production period at a constant production rate, it is recommended to use the fixed choke due to such a choke's resistance to erosion [2,[5][6][7].…”

mentioning

confidence: 99%

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

et al. 2019

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There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a small number of fields. Therefore, these correlations are valid within the parameter variation ranges of those fields. If such correlations are used elsewhere for the prediction of the subcritical choke flow performance of the other fields, significant errors will occur. Additionally, there are only a few empirical correlations for sub-critical choke flow performance in high rate gas condensate wells. These led the authors to develop a new empirical correlation based on a wider production data set from different gas condensate fields in the world; 234 production data points were collected from a large number of production wells in twenty different gas condensate fields with diverse reservoir conditions and different production histories. A non-linear regression analysis method was applied to their production. The new correlation was validated with a new set of data points from some other production wells to confirm the accuracy of the established correlation. The results show that the new correlation had minimal errors and predicted the gas flow rate more accurately than the other three existing models over a wider range of parameter variation ranges.

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Characteristics of Gas Condensate

Cited by 13 publications

References 1 publication

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

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

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells

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