Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    The artificial neural network's prediction of crude oil viscosity for pipeline safety

    Access Status
    Fulltext not available
    Authors
    Obanijesu, Emmanuel
    Omidiora, E.
    Date
    2009
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Obanijesu, E. and Omidiora, E. 2009. The artificial neural network's prediction of crude oil viscosity for pipeline safety. Petroleum Science and Technology. 27 (4): pp. 412-426.
    Source Title
    Petroleum Science and Technology
    DOI
    10.1080/10916460701853846
    ISSN
    1091-6466
    School
    Department of Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/11969
    Collection
    • Curtin Research Publications
    Abstract

    Predicting crude oil viscosity is a challenge faced by reservoir engineers in production planning. Some early researchers have propounded some theories based on crude oil properties and have encountered various problems leading to errors in forecasted values. This article discusses work carried out with a model using an artificial neural network (ANN) for predicting crude oil viscosity of Nigerian crude oil. The model was started through adoption of a classical regression technique empirical method for dead oil viscosity as a function of American Institute for Petroleum (API) and reduced temperature. The Peng-Robinson equation of state and other thermodynamic properties are introduced, coupled with the Standing model for calculating bubble point pressure (Pb). The developed model was evaluated using existing measured real-life data collected from 10 oil fields within the Niger Delta region of Nigeria. Both the predicted and measured viscosities were plotted against each corresponding reservoir pressure to establish the model's level of reliability. The superimposition of the pressure-viscosity relationship shows that at each point, the viscosity model captures the physical behavior of viscosity variations with pressure. In each case, the ANN does not require a data relationship to predict the crude oil viscosity but rather relies on the field data obtained for training. For this reason, it is recommended that the ANN approach should be applied in oil fields for reduction in error, computational time, and cost of overproduction and underproduction.

    Related items

    Showing items related by title, author, creator and subject.

    • Development of accurate and reliable correlations for various design parameters in oil and gas processing industries
      Bahadori, Alireza (2011)
      The continuing growth in the importance of oil and gas production and processing overall the globe increase the need for accurate prediction of various parameters and their impact on unit operations, process simulation ...
    • Simulation of subsea production pipeline stream to evaluate and address the flow assurance issues of waxy crude oil
      Ahmed, Ashfaq (2009)
      The modern world is heavily dependent on crude oil and its associated products and the petroleum industry has taken responsibility to meet the rising consumer demand. Oil and gas production can be broadly subdivided into ...
    • Artificial neural network's prediction of wax deposition potential of Nigerian crude oil for pipeline safety
      Obanijesu, Emmanuel; Omidiora, E. (2008)
      Paraffin wax deposition from crude oil along pipeline is a global problem, making preventive methods preferred to removal methods. In this work, a neural network model based on mathematical modeling technique using ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.