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An Accurate Reservoir's Bubble Point Pressure Correlation.

Fahd Saeed Alakbari1, Mysara Eissa Mohyaldinn1, Mohammed Abdalla Ayoub1

  • 1Petroleum Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak 32610, Malaysia.

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This study introduces a new Group Method of Data Handling (GMDH) correlation for accurately predicting bubble point pressure (Pb) in petroleum reservoirs. The GMDH model offers superior accuracy and robustness compared to existing methods, improving reservoir characterization.

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Area of Science:

  • Petroleum Engineering
  • Reservoir Characterization
  • Computational Geoscience

Background:

  • Bubble point pressure (Pb) is critical for petroleum production and reservoir simulation.
  • Traditional PVT measurements for Pb are costly and time-consuming.
  • Existing empirical and machine learning models for Pb prediction have limitations in accuracy and applicability.

Purpose of the Study:

  • To develop an accurate and robust correlation for predicting bubble point pressure (Pb).
  • To utilize the Group Method of Data Handling (GMDH) for improved prediction capabilities.
  • To ensure the developed model reflects correct physical behavior based on input parameters.

Main Methods:

  • Application of the Group Method of Data Handling (GMDH), integrating neural networks and statistical methods.
  • Development of the GMDH model using a global dataset of 760 data points.
  • Verification through trend analysis and statistical comparisons with existing models.

Main Results:

  • The GMDH model accurately follows the physical behavior of input parameters: gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature.
  • Achieved the lowest average percent relative error (8.51%), root mean square error (12.70), and standard deviation (0.09).
  • Attained the highest correlation coefficient (0.9883) compared to previously published models.

Conclusions:

  • The GMDH correlation provides a highly accurate and robust method for predicting bubble point pressure (Pb).
  • The model demonstrates superior performance over existing methods in predicting Pb.
  • This advancement aids in more reliable petroleum production, simulation, and reservoir characterization.