Neural Network Prediction of Inflow Performance Relationship of Producing Wells- Yemeni Oil Field

The isomerization unit improves the research octane number of light naphtha with better environmental impacts.Recent developments in increasing the octane number in the isomerization unit lead to an increase in the demand for hydrogen in the refining.Therefore, the disconnection between the hydrogen network and increasing the octane number in the isomerization unit will lead to a shortage of hydrogen and unmet demand for hydrogen.The present work is an integrated approach to the isomerization process in refineries and hydrogen network design.Modeling and optimization of the overall hydrogen network objective to minimize the total consumption of fresh hydrogen and maximize the isomerization octane number using a goal programming multi-objective optimization approach is presented.The model is introduced for two cases, reuse/recycling and hydrogen regeneration networks.The global network model is a multi-objective nonlinear programming model, and is optimized using Lingo optimization software version 14.The results showed that by integrating the isomerization model with the hydrogen reuse/recycling network, the fresh hydrogen saving was 20.871 kmol/h compared to 7.772 kmol/h in the case of neglecting the isomerization model.On the other hand, a significant reduction of 447.898 kmol/h in fresh hydrogen was achieved in the hydrogen regeneration network when including the isomerization model compared to that achieved when neglecting the isomerization model (343.261kmol/h).In addition, the optimum operating conditions, feed specifications and hydrogen consumption of the isomerization unit that gives the maximum research octane number and the minimum fresh hydrogen were determined for both the reuse/recycling and hydrogen regeneration network cases.

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Publication Details

Journal
Journal of Petroleum and Mining Engineering
Published
2025-06-02
DOI
https://doi.org/10.21608/jpme.2025.339180.1218
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
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article

Neural Network Prediction of Inflow Performance Relationship of Producing Wells- Yemeni Oil Field

Ghareb Hamada
Journal of Petroleum and Mining Engineering
Reservoir Engineering and Simulation Methods
article

Neural Network Prediction of Inflow Performance Relationship of Producing Wells- Yemeni Oil Field

Ghareb Hamada
article en

Abstract

The isomerization unit improves the research octane number of light naphtha with better environmental impacts.Recent developments in increasing the octane number in the isomerization unit lead to an increase in the demand for hydrogen in the refining.Therefore, the disconnection between the hydrogen network and increasing the octane number in the isomerization unit will lead to a shortage of hydrogen and unmet demand for hydrogen.The present work is an integrated approach to the isomerization process in refineries and hydrogen network design.Modeling and optimization of the overall hydrogen network objective to minimize the total consumption of fresh hydrogen and maximize the isomerization octane number using a goal programming multi-objective optimization approach is presented.The model is introduced for two cases, reuse/recycling and hydrogen regeneration networks.The global network model is a multi-objective nonlinear programming model, and is optimized using Lingo optimization software version 14.The results showed that by integrating the isomerization model with the hydrogen reuse/recycling network, the fresh hydrogen saving was 20.871 kmol/h compared to 7.772 kmol/h in the case of neglecting the isomerization model.On the other hand, a significant reduction of 447.898 kmol/h in fresh hydrogen was achieved in the hydrogen regeneration network when including the isomerization model compared to that achieved when neglecting the isomerization model (343.261kmol/h).In addition, the optimum operating conditions, feed specifications and hydrogen consumption of the isomerization unit that gives the maximum research octane number and the minimum fresh hydrogen were determined for both the reuse/recycling and hydrogen regeneration network cases.

Journal of Petroleum and Mining EngineeringVol. 0(0)
King Faisal University (SA)
Openalex Percentile: Top 83%
Reservoir Engineering and Simulation Methods
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Neural Network Prediction of Inflow Performance Relationship of Producing Wells- Yemeni Oil Field — Ghareb Hamada · Journal of Petroleum and Mining Engineering (2025) | TGRS Research Map | TGRS