Gas Condensate Reservoir Modeling, Part I: From Deterministics To Stochastics

Babalola, F. U.; Susu, Alfred A.; Olaberinjo, F. A.

doi:10.1080/10916460802455681

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…A PSO-ANN structure with appropriate parameters can exhibit a good performance. 90−93 To design a proper network, six (6) The constant for global best (c 1 ), constant for personal best (c 2 ), time interval, and number of particles have significant effects on the optimal configuration of the PSO-ANN model. 90−93 In addition, other parameters namely, maximum iteration, number of neurons in the hidden layer, and length of input and output data, are examined for calibration and optimization of the model.…”

Section: Experimental Procedures and Data Collectionmentioning

confidence: 99%

“…1−4 Isothermal condensation of liquids lowers the gas deliverability in the reservoir due to condensate blockage. 4,6,7 Low gas relative permeability in the gas−liquid region near the production well is the main reason behind reduction of gas flow rate. 4,6−8 Proper production strategy for gas condensate reservoirs can minimize or even avoid condensate blockage, however the prediction of key parameters like CGR is necessary.…”

Section: Introductionmentioning

confidence: 99%

“…Numerical and analytical modeling, PVT studies, and characterizations of retrograde gas condensate reservoirs remain challenging to the reservoir engineers as a huge amount of time and energy is required to carry out such studies mentioned above, efficiently. [4][5][6]9,10 The phase behavior of a gas condensate reservoir is strongly dependent on the P-T envelope and thermodynamic conditions of the hydrocarbon mixture. Proper thermodynamic knowledge of phase behavior in this type of reservoirs is essential in order to predict the recovery factor and CGR.…”

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: Experimental Procedures and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…It was also observed that the critical condensate saturation was less sensitive to interfacial tension but the end-point saturations are sensitive to interfacial tension. Babalola et al (2009a) stated that original and best modified black oil models are limited and thus, not adequate for modelling gas condensate reservoirs as a result of the deterministic nature of evaluation. A stochastic gas condensate model is required to match the adequacy and serve optimal recovery techniques.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Akpet Gt9 Gas Condensate Reservoir Performance

Sylvester¹,

Ogbarode²

2020

OJES

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To effectively evaluate a gas condensate reservoir performance, the reservoir engineer must have a reasonable amount of knowledge about the reservoir to adequately analyze the reservoir performance and predict future production under various modes of operation. Due to the multiphase flow that exists in the reservoir, characterization of gas condensate reservoirs is often a difficult task with the variation of its overall composition in both space and time during production which complicates well deliverability analysis and the sizing of surface facilities. This study is primarily concern with the evaluation of a gas condensate reservoir performance of Akpet GT 9 Reservoir in the Niger Delta region of Nigeria with material balance analysis tool “MBal” without having to run numerical simulations. The result obtained with MBal on the analysis of Akpet GT 9 reservoir gave 23.934 Bscf of gas initially in place which compares favorably with the volume obtained from volumetric techniques. Results also shows that the most likely aquifer model is the Hurst–Van Everdingen - Dake radial aquifer and the reservoir is supported by a combined drive of water influx and fluid expansion. Okotie, S. | Department of Petroleum Engineering, Federal University of Petroleum Resources (FUPRE), Effurun, Delta State, Nigeria.

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