Wax formation assessment of condensate in South Pars gas processing plant sea pipeline (a case study)

Rahimpour, Mohammad Reza; Davoudi, Mehdi; Jokar, Seyyed Mohammad; Khoramdel, Iraj; Shariati, Alireza; Dehnavi, M.R.

doi:10.1016/j.jngse.2012.10.003

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…Because of negative impacts of wax on petroleum production, over the past decades numerous efforts have been devoted to this subject and various models have emerged for the prediction of WAT and consequent wax deposits . The developed models are classified into two general categories: solid solution models, and multi‐solid phase models . In former models, the precipitated wax is assumed to be a single solid solution in which all the components are miscible at all proportions.…”

Section: Introductionmentioning

confidence: 99%

“…In former models, the precipitated wax is assumed to be a single solid solution in which all the components are miscible at all proportions. In this category of models, the activity model and the equation of state (EOS) are employed for the solid–liquid and vapor–liquid equilibrium, respectively . In recent models, each precipitated component is deemed as a separate solid phase that is unable to blend with other solid phases.…”

Section: Introductionmentioning

confidence: 99%

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Combining of intelligent models through committee machine for estimation of wax deposition

Gholami

Ansari²,

Ahmadi

2018

J Chinese Chemical Soc

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Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining of intelligent models through committee machine for estimation of wax deposition

Gholami

Ansari²,

Ahmadi

2018

J Chinese Chemical Soc

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“…Petroleum fluids produced from off‐shore reservoirs are often at high pressures. Along the pipeline, petroleum fluids are often exposed to low temperatures . In these conditions, multiphase flow is quite common and a flow assurance engineer is liable for the safe deliverance of the fluids.…”

Section: Introductionmentioning

confidence: 99%

Phase boundaries for synthetic light oils and gas condensate mixtures using predictive equations of state with a solid solution model

Nasrifar

Javanmardi

2018

Can J Chem Eng

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Phase boundaries for five synthetic paraffinic mixtures were predicted using a previously developed Gibbs energy minimization algorithm. In this method, the vapour and liquid phases were described by cubic equations of state (EoS). These EoS employed a predictive temperature-dependent group-contribution binary interaction parameter model. The solid phase was described using a universal quasi-chemical activity coefficient (UNIQUAC) solid solution model. Using this algorithm, the phase boundaries were calculated from a temperature-search strategy. The predicted phase boundaries were found to be in good agreement with experimental phase boundaries for vapour-liquid (VL), solid-liquid (SL), solid-vapour (SV), and solid-vapour-liquid (SVL) regions. The model was also used to predict the phase boundaries at low temperatures where no experimental data were available.

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“…In the SS approach, it is assumed that all components contribute in wax formation, and over-estimate the amount of the precipitated wax (Vafaie-Sefti et al 2000). SS models use two different types of equation of state (EOS) and activity coefficients simultaneously for non-ideality description of the liquid state which makes these models, thermodynamically inconsistent (Rahimpour et al 2013). However, MS model uses only EOS for non-ideality of phases in equilibrium.…”

Section: Introductionmentioning

confidence: 99%

“…However, MS model uses only EOS for non-ideality of phases in equilibrium. The MS model does not require any adjustable mixture parameters which makes it very simple and accurate in practice (Rahimpour et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Study of wax disappearance temperature using multi-solid thermodynamic model

Mansourpoor

Azin

Osfouri

et al. 2018

J Petrol Explor Prod Technol

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Wax deposition inside pipeline and process equipment is a major problem in oil industry. In this study, a multi-solid thermodynamic model was developed to predict wax disappearance temperature (WDT). Paraffinic-naphthenic-aromatic (PNA) analysis was performed and two correlations were introduced for fusion properties of these species. In addition, WDT of 12 Iranian oil and condensate samples were measured using viscometry and differential scanning calorimetry (DSC) techniques. Experimental data of multi-component and ternary systems were utilized for validation of the model. It was observed that measured WDT by viscometry method is higher compared to DSC. Statistics analysis shows that DSC technique has lower average absolute relative error (AARE) and standard uncertainty compared to viscometry. Results show that the AARE of the model for ternary systems is 0.52% which is much lower among the previous developed thermodynamic models. In addition, AARE of the new model for 68 data was calculated about 0.23%, and R-square of model prediction was calculated about 0.97. The cumulative distribution function also indicates that P50 values are almost the same for model and experimental data. These results show that the model has a good accuracy. In addition, the accuracy of model increases as the average carbon number of oil mixtures increases. Finally, it was found that PNA analysis and distribution of each component in its sub-fractions have a considerable effect on the model accuracy.

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Wax formation assessment of condensate in South Pars gas processing plant sea pipeline (a case study)

Cited by 17 publications

References 29 publications

Combining of intelligent models through committee machine for estimation of wax deposition

Combining of intelligent models through committee machine for estimation of wax deposition

Phase boundaries for synthetic light oils and gas condensate mixtures using predictive equations of state with a solid solution model

Study of wax disappearance temperature using multi-solid thermodynamic model

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