Toward an intelligent approach for H 2 S content and vapor pressure of sour condensate of south pars natural gas processing plant

Kazerooni, Nooshin Moradi; Adib, Hooman; Sabet, Askar; Adhami, Mohammad Amin; Adib, Marjan

doi:10.1016/j.jngse.2015.12.006

Cited by 11 publications

(11 citation statements)

References 14 publications

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“…The number of vessels required for the stabilization of oil depends upon the gas-to-oil ratio as well as the wellhead pressure [11]. The flash vaporization method is cheaper than the fractionation method; however, it cannot meet the RVP requirements due to the large loss of lighter hydrocarbons, therefore, a flash separator alone cannot be used for stabilization [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Generally, the crude oil stabilization process consists of heaters, separators, coolers, and stabilizer columns [6]. As a result, stabilized NGL is sent to a storage tank for transfer to the refinery, while lighter hydrocarbon gases, along with gases impurities, are sent to one of the following sources; (i) the fuel gas is diluted by injecting corrosive gas into the fuel gas header if the downstream piping is designed based on corrosive material; (ii) re-compress the gas to inject it into a high-pressure gas handling system; (iii) recover the fuel gas by removing acidic gases through sweetening, such as using the amine system; (iv) gases can be recycled into a reservoir for enhanced oil recovery purposes; or (v) gases are used to flare as a continuous source of light-up gas with the redundant supply of fuel gas from other sources [7]. Stabilized NGL is then sent to the refinery for mixing with heavy crude oil [8], to help in the pumping of heavy crude oil by reducing viscosity from the storage tanks to the preheat exchanger trains and to increase the production of motor gasoline [9].…”

Section: Introductionmentioning

confidence: 99%

“…Gases from the top of the fractionation column in the stabilization process are recovered through liquefaction, LPG production, and the compression unit, for which the 219% highest rate of return (ROR) was found for the liquefaction process based on economic analysis [5]. Development of stabilizer column models through the artificial neural networks (ANN) method have shown an average absolute deviation percent (ADD%) of 1.6 for RVP and 3.8 for H 2 S concentration [6], in comparison to the support vector machine (SVM) method with a squared correlation coefficient of 0.97 for H 2 S concentration and 0.94 for Reid vapor pressure (RVP) [7]. The pressure effect through model development on the stabilization column with a mean absolute error of 2.69% for process variables has been studied in the literature [10].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic and Steady-State Simulation Study for the Stabilization of Natural Gas Condensate and CO2 Removal through Heating and Pressure Reduction

Ehsan,

Ali,

Sher

et al. 2023

ChemEngineering

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Stabilization of condensate is a highly energy-consuming process compared to other oil and gas processes. There is a need to reduce this energy consumption. Therefore, the present work aims to simulate the stabilization unit in terms of available energy and on-spec stabilized condensate products. Natural gas condensate liquids (NGL) need to be stabilized by eliminating lighter hydrocarbon gases and acid gases before being sent to the refinery. Stabilized NGL has the vapor pressure determined as a Reid vapor pressure of 7 psia, showing that light components did not evolve as a separate gas phase. Stabilization and CO2 removal was performed through the distillation method by heating and pressure reduction using steady state and dynamic simulation through Aspen HYSYS. Different process alterations around the exchanger and column have been studied based on the utilities available for the stabilization and CO2 removal process. Sensitivity studies, including the impact of CO2 concentration, the temperature at the inlet of the stabilizer flash separator, and the dynamic simulation for the PID controller, have been performed to analyze the impact on the process parameters, such as Reid vapor pressure (RVP) and CO2 of the rundown air cooler and heat duties of the exchangers. Actual plant data have been used for the validation of process simulation values for the accuracy of the condensate stabilization unit model. Based on the scenarios analyzed, it can be concluded that the nitrogen stripping method achieved 7 ppmv CO2 and 7 psia RVP in the condensate from the cooler outlet, while a variation of 29 bpd was observed for the stabilized condensate flowrate throughout all scenarios with data validation showing 0.24% discrepancy between Aspen Hysys data and actual plant data.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic and Steady-State Simulation Study for the Stabilization of Natural Gas Condensate and CO2 Removal through Heating and Pressure Reduction

Ehsan,

Ali,

Sher

et al. 2023

ChemEngineering

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“…Support Vector Machine training procedure converges to optimum output results faster and it is not need to control model parameters (Cortes and Vapnik, 1995;Pelckmans et al, 2002;Suykens et al, 2002;Curilem et al, 2011). For detailed information about the SVM refer to our Adib et al, 2013Adib et al, , 2015Moradi et al, 2016). Pattern recognition or classification can be performed by SVM in a data set consisting of N data point x k ; y k f gk ¼ 1; 2; .…”

Section: Support Vector Machinementioning

confidence: 99%

“…The main advantage of such models over existing approaches is the capability of learning and generalizing data, fault tolerance and inherent contextual information processing in addition to fast computation potential (Raynal et al, 2016). Such characteristics make them perfect candidates for applications where the complexity of the data or task demands high computational costs (Haghbakhsh et al, 2012;Adib et al, 2013Adib et al, , 2015Moradi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

Adib

Kazerooni

Falsafi

et al. 2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The acidic compounds such as Mercaptans, H2S and COS are commonly present in the liquid LPG streams in the south Pars gas processing plant. Sulfur contaminants not only lead to odor problems but can form objectionable oxides on combustion and cause environmental pollution. In present study, Support Vector Machine (SVM) is employed to develop an intelligent model to predict the sulfur content of propane and butane products of Liquefied Petroleum Gas (LPG) treatment unit of south Pars gas processing plant of Assaluyeh/Iran. A set of seven input/output plant data each consisting of 365 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 70%, 15%, and 15% of available data respectively. Test results from the SVM developed model showed good compliance with operating plant data. Squared correlation coefficients for developed models are 0.97 and 0.99 for propane and butane sulfur content, respectively. According to the results of the present case study, SVM could be regarded as a reliable accurate approach for modeling the sulfur content of LPG treatment unit of a natural gas processing plant.

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A machine learning approach for modelling and optimization of complex systems: Application to condensate stabilizer plants

Alkatheri,

Hourfar,

Khoshnevisan

et al. 2024

Can J Chem Eng

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Recent advancements in supervised machine learning tools have demonstrated their ability to achieve accurate and efficient prediction results. In this paper, we leverage these tools as alternative approaches to model a specific application in the gas industry, based on operating data. The chosen application is a natural gas condensate stabilization process, in which light end components are removed to reduce condensate vapour pressure, meeting storage and transportation specifications. Here, we develop and evaluate various supervised machine learning models to predict the performance of two industrial condensate stabilizer units. By utilizing large datasets from these units and encompassing comprehensive operating data of input–output variables, we not only demonstrate the capability of these techniques to offer reliable and accurate predictions but also shed light on their potential impacts and implementations. The impacts of applying selected AI and machine learning algorithms are two‐fold. First, our research presents an innovative approach to process modelling and optimization in the gas industry, showing the potential for enhanced operational efficiency, profitability, and safety. Second, we propose a data‐driven surrogate‐based optimization framework, where the generated machine learning models can replace detailed first‐principle models, offering a streamlined method to find optimal values for variables to reduce operational energy consumption. Furthermore, we address the validation requirements of our machine learning models, ensuring their robustness and reliability in real‐world applications. By incorporating rigorous validation procedures, we guarantee the quality of our predictions and support their practical implementation. In conclusion, our research not only highlights the capabilities of machine learning in gas industry applications but also emphasizes their potential impacts and contributions to operational excellence. So, the presented approach can pave the way for improved performance, efficiency, and profitability in the gas industry.

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Toward an intelligent approach for H 2 S content and vapor pressure of sour condensate of south pars natural gas processing plant

Cited by 11 publications

References 14 publications

Dynamic and Steady-State Simulation Study for the Stabilization of Natural Gas Condensate and CO2 Removal through Heating and Pressure Reduction

Dynamic and Steady-State Simulation Study for the Stabilization of Natural Gas Condensate and CO2 Removal through Heating and Pressure Reduction

Prediction of sulfur content in propane and butane after gas purification on a treatment unit

A machine learning approach for modelling and optimization of complex systems: Application to condensate stabilizer plants

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