Experiments and Modeling of Volumetric Properties and Phase Behavior for Condensate Gas under Ultra-High-Pressure Conditions

Sun, Chang‐Yu; Liu, Huang; Yan, Ke‐Le; Ma, Qing-Lan; Liu, Bei; Chen, Guangjin; Xiao, Xiangjiao; Wang, Haiying; Zheng, Xu-Chang; Shi, Li

doi:10.1021/ie2025757

Cited by 34 publications

(7 citation statements)

References 51 publications

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“…7−11 The severity of condensate damage depends on several factors, mainly the reservoir formation and the natural gas composition. 4,12−15…”

Section: Introductionmentioning

confidence: 99%

“…In gas condensate reservoirs, condensate banking is one of the most critical problems that occur due to the reservoir depletion and reduce the gas recovery. − During gas production, the reservoir pressure may decrease below the dew point pressure, and then a significant amount of the condensate liquid will develop and accumulate around the production well . The accumulated liquid will impede the flow of natural gas from the reservoir into the borehole, leading to a significant decrease in the well productivity. − The severity of condensate damage depends on several factors, mainly the reservoir formation and the natural gas composition. ,− …”

Section: Introductionmentioning

confidence: 99%

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New Chemical Treatment for Permanent Removal of Condensate Banking from Different Gas Reservoirs

et al. 2019

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Condensate banking represents a challenging problem in producing the hydrocarbon from tight gas reservoirs. The accumulation of liquid condensates around the production well can significantly impair the gas flow rate. Gas injection and hydraulic fracturing are the common techniques used to avoid the condensate development by maintaining the reservoir pressure above the dew point curve. However, these treatments are associated with high operational costs and large initial investment. This study presents a new chemical treatment for removing the condensate banking using thermochemical solutions. The presented treatment can cause a permanent impact on the treated formations. Chemicals are injected to react downhole and generate in situ pressure and heat in certain conditions. The generated pressure can raise the gas pressure above the dew point, and the generated heat can change the phase of the liquid condensate to gas. Kinetic analysis indicates that thermochemical fluids can increase the temperature and heat by 85 °C and 369 kJ/mol, respectively. In addition, the impact of clay content on the efficiency of thermochemical treatment was studied using coreflooding experiments. A condensate removal of more than 60% was achieved using the huff and puff injection mode. A good correlation between the rock permeability and the condensate removal efficiency was observed. Higher condensate removal was obtained for the rock samples with high permeability values. Moreover, the presence of clay minerals in the treated rock showed a minor impact on the condensate removal, indicating that the injected chemicals are able to stabilize the clay minerals and avoid clay damage. This research shows that thermochemical treatment can remove more than 60% of the condensate damage for different types of tight sandstones. Huff and puff treatment was found to be a very practical approach to diminish the condensate banking from different sandstone rocks. Also, this work confirms that thermochemical treatment can be applied in the clayey formation for removing the condensate blockage without affecting the clay stability or inducing clay damage. Ultimately, this study introduces a new chemical treatment in the gas industry, and the used chemicals are effective, environmentally friendly, and not expensive.

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“…7−11 The severity of condensate damage depends on several factors, mainly the reservoir formation and the natural gas composition. 4,12−15…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Chemical Treatment for Permanent Removal of Condensate Banking from Different Gas Reservoirs

et al. 2019

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“…Petersen and Stenby 15 studied the prediction of thermodynamic properties of oil and CG mixtures using ALS EoS. Sun et al 17 and Shariati et al 18 used a synthetic visual method to determine PVT data for CG at high pressures. It is noteworthy that Shariati et al 18 also used a synthetic mixture of hydrocarbons to represent the CG sample.…”

Section: Introductionmentioning

confidence: 99%

Characterization, Pressure–Volume–Temperature Properties, and Phase Behavior of a Condensate Gas and Crude Oil

et al. 2018

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The oil reservoirs are underground and have the oil and gas contained in the porous rock at high temperatures and pressures. Only 5−20% of the oil is withdrawn in primary production. Further recovery can be achieved by injecting carbon dioxide (CO 2 ) that displaces and dissolves part of the remaining oil; this process is called enhanced oil recovery. Although the characterization and fractionation of petroleum are well-known and studied, each oil sample represents a unique multicomponent system; therefore, an individual study of the sample is required. Real samples of condensate gas (CG) and light crude oil (LCO) were collected and analyzed for density, viscosity, atmospheric distillation and fractionation, and aiming characterization. Synthetic visual and non-visual methods for high pressure were successfully applied for bubble point measurements of the systems composed of supercritical CO 2 and CG or LCO. Phase envelope calculations were developed on the basis of pseudocomponents obtained by atmospheric distillation and density values using the Adachi−Lu−Sugie equation of state with van der Waals mixing rule with one interaction parameter (k ij ) from the literature. Pressure−volume−temperature (PVT) measurements are reported for the systems CG + CO 2 and LCO + CO 2 as a function of the temperature and pressure and in a wide range of CO 2 composition. Crude oil characterization using atmospheric distillation and density measurements was demonstrated to be feasible with accuracy, because the boiling points and specific mass obtained allowed us to propose a series of pseudocomponents to represent the sample phase behavior studied experimentally. Thus, results suggest that the characterization and fractioning of the samples were effective. Thermodynamic modeling and experimental data presented an average deviation of 3.1% to CG + CO 2 and 2.5% to LCO + CO 2 systems, indicating reasonable accuracy for petroleum samples.

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“…The measured compressibility factor, compositional analysis, and molecular weight, as well as pressures and temperatures of a wide range of natural lean, rich, sweet, and sour gases, were gathered from the literature in order to predict the gas compressibility factor by using statistical modeling and MLFN [29][30][31][32][33][34]. The pseudo-reduced pressure (P pr ) and temperature (T pr ) of each sample were calculated according to Kay's rule, using the following equations:…”

Section: Data Gatheringmentioning

confidence: 99%

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Ghanem

Gouda²,

Alharthy

et al. 2022

Energies

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Simulating the phase behavior of a reservoir fluid requires the determination of many parameters, such as gas–oil ratio and formation volume factor. The determination of such parameters requires knowledge of the critical properties and compressibility factor (Z factor). There are many techniques to determine the compressibility factor, such as experimental pressure, volume, and temperature (PVT) tests, empirical correlations, and artificial intelligence approaches. In this work, two different models based on statistical regression and multi-layer-feedforward neural network (MLFN) were developed to predict the Z factor of natural gas by utilizing the experimental data of 1079 samples with a wide range of pseudo-reduced pressure (0.12–25.8) and pseudo reduced temperature (1.3–2.4). The statistical regression model was proposed and trained in R using the “rjags” package and Markov chain Monte Carlo simulation, while the multi-layer-feedforward neural network model was postulated and trained using the “neural net” package. The neural network consists of one input layer with two anodes, three hidden layers, and one output layer. The input parameters are the ratio of pseudo-reduced pressure and the pseudo-reduced temperature of the natural hydrocarbon gas, while the output is the Z factor. The proposed statistical and MLFN models showed a positive correlation between the actual and predicted values of the Z factor, with a correlation coefficient of 0.967 and 0.979, respectively. The results from the present study show that the MLFN can lead to accurate and reliable prediction of the natural gas compressibility factor.

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Experiments and Modeling of Volumetric Properties and Phase Behavior for Condensate Gas under Ultra-High-Pressure Conditions

Cited by 34 publications

References 51 publications

New Chemical Treatment for Permanent Removal of Condensate Banking from Different Gas Reservoirs

New Chemical Treatment for Permanent Removal of Condensate Banking from Different Gas Reservoirs

Characterization, Pressure–Volume–Temperature Properties, and Phase Behavior of a Condensate Gas and Crude Oil

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

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