Forthcoming and Online First Articles

International Journal of Petroleum Engineering

International Journal of Petroleum Engineering (IJPE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Petroleum Engineering (3 papers in press)

Regular Issues

  • An Evaluation of Thermodynamic Models for Temperature Rise Across a Multiphase Booster   Order a copy of this article
    by Carl Martin Carstensen, Kyrre Grønstøl 
    Abstract: Multiphase boosting technology significantly enhances oil and gas field production by reducing backpressure on the wells. Various designs, including helico-axial pumps, twin-screw pumps, and contra-rotating axial compressors, are employed to efficiently handle multiphase streams of gas, oil, and water. This in-depth study investigates the thermodynamics governing temperature rise across boosters, a critical factor impacting mechanical integrity, flow assurance, and safety. Several models have been studied to calculate temperature rise. Among them, the cubic equation of state has emerged as the most accurate and robust. It determines discharge temperature based on enthalpy change across the booster and has been validated against datasets involving air/nitrogen and freshwater, and a hydrocarbon process at high gas fractions. For simpler mixtures like air and water, an ideal gas and incompressible liquid approach proves comparable to the generic model, but for complex hydrocarbon mixtures, accounting for non-ideality and phase transfer is essential to ensure model reliability.
    Keywords: boosting; thermodynamic equilibrium; Joule-Thomson compression; latent heat.
    DOI: 10.1504/IJPE.2024.10064786
     
  • Study on the Development of Effective Drilling Fluid Based on Corn Starch   Order a copy of this article
    by Darshil Patel, Hit Patel, Achinta Bera 
    Abstract: The effectiveness of drilling fluid can be depicted based on its rheology and filtration loss properties which play a pivotal role in drilling operation. To improve these properties advanced and exciting research works are required to provide the solutions for drilling challenges of hydrocarbon industry. The required properties of drilling muds can be improved using potential additives and biomaterials which are preferred to meet the criteria of eco-friendly drilling operations. However, corn starch is one of the environment-friendly viscosity and fluid loss control modifying agent which can help the drilling fluid to work at high temperature to mitigate the drilling problems. This study aims to investigate the potential of utilizing natural polymer like corn starch acting as fluid loss control agents in water-based drilling mud. The filtration and rheological properties of the water-based mud were analysed at 30 to 80
    Keywords: Corn starch; Water-based drilling fluid; Filtration loss; Rheological property; Mud cake; Plastic viscosity.
    DOI: 10.1504/IJPE.2024.10065943
     
  • Prediction of Reservoir Gas-Oil Ratio based on PSO-ELM Algorithm   Order a copy of this article
    by Haibo Liang, Xia Zhang, Yi Yang, Jialing Zou 
    Abstract: Fluid identification is the key to reservoirs' characterisation in oil exploration, and the gas-oil ratio (GOR) is an important parameter for fluid's property. Traditional methods are mainly based on the artificial division, which can only be used for simple case. For this issue, this study proposes a predictive model of extreme learning machine (ELM) optimised by particle swarm optimisation (PSO). Firstly, the characteristic parameters are got by fluids logging and analysis in real-time databased on improved methods, and the characteristic vectors are obtained by Pearson correlation and random forest. Then, the prediction model of extreme learning machine was established, and two decisive parameters were optimised by particle swarm optimisation. Finally, the GOR of the oil layer and condensate gas layer were predicted, and a comparison of PSO-ELM and some other algorithms was performed. The result shows, PSO-ELM model can predict the GOR with high precision, providing theoretical support for complex fluid identification.
    Keywords: gas logging; random forest; RF; extreme learning machine; ELM; particle swarm optimisation; PSO; gas-oil ratio; GOR.
    DOI: 10.1504/IJPE.2024.10066250
     

 

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