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  11. Optimized Artificial Neural Network Application For Estimating Oil Recovery Factor Of Solution Gas Drive Sandstone Reservoirs

Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs

Muhammad Taufiq Fathaddin1, Sonny Irawan2, Rini Setiati1

  • 1Department of Petroleum Engineering, Universitas Trisakti, 11440 Jakarta, Indonesia.

Heliyon
|July 19, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances artificial neural network (ANN) models for predicting oil recovery factor (RF). The optimized ANN model significantly improves prediction accuracy, crucial for field development planning.

Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Reservoir Management

Background:

  • Oil recovery factor (RF) is critical for field development planning but has a complex relationship with reservoir properties.
  • Artificial neural networks (ANNs) can model these complex correlations.

Purpose of the Study:

  • To develop a more accurate ANN model for predicting oil recovery factor (RF).
  • To optimize ANN model parameters for improved correlation accuracy.

Main Methods:

  • Data preprocessing including outlier removal.
  • Systematic selection of input parameters, transfer functions, neuron count, and hidden layers.
  • Development and validation of an ANN model with nine input parameters.

Main Results:

  • An optimized ANN model was developed using nine key input parameters.
Keywords:
Artificial neural networkRecovery factorReservoirSandstone

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  • A tangent sigmoid transfer function, 30 neurons, and two hidden layers were identified as optimal.
  • The proposed ANN model achieved a high correlation coefficient of 0.91657, outperforming previous correlations.

    • Conclusions:

    • The refined ANN approach significantly enhances the accuracy of oil recovery factor predictions.
    • This improved accuracy supports more reliable field development planning.
    Solution gas drive