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Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in
Miao Jin1, Qinzhuo Liao1, Shirish Patil1
1Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia.
ACS Omega
|July 25, 2022
Summary
This study optimizes artificial neural networks (ANNs) for estimating oil production rate (OPR), water-oil ratio (WOR), and gas-oil ratio (GOR) in oil and gas fields. The ANN model demonstrated high accuracy, outperforming traditional methods.
Area of Science:
- Petroleum Engineering
- Artificial Intelligence
- Data Science
Background:
- Accurate well production rate estimation is crucial for oil and gas field development.
- Traditional estimation models (numerical simulations, empirical models) face limitations like high computational cost and oversimplified assumptions.
- Artificial Neural Networks (ANNs) offer a powerful alternative for complex regression problems in reservoir engineering.
Purpose of the Study:
- To apply and optimize an Artificial Neural Network (ANN) model for estimating key well production parameters: oil production rate (OPR), water-oil ratio (WOR), and gas-oil ratio (GOR).
- To identify optimal ANN hyperparameters and transfer function combinations for enhanced prediction accuracy.
- To validate the ANN model's performance against traditional methods and analyze input parameter influence.
Main Methods:
- Data analysis to select relevant well operation parameters for OPR, WOR, and GOR prediction.
- Systematic evaluation of ANN hyperparameters, including network architecture, training functions, and transfer functions.
- Stochastic analysis using relative root mean square error (RMSE) to select optimal transfer functions.
- Monte Carlo simulation for input effect analysis and model comparison.
Main Results:
- The optimized ANN model achieved an average relative RMSE of 6.8% for OPR, 18.0% for WOR, and 1.98% for GOR.
- The study identified optimal ANN configurations and transfer function combinations for accurate production forecasting.
- ANN model performance was validated and compared against an empirical model, highlighting its strengths.
Conclusions:
- The optimized ANN model provides a highly effective and accurate method for estimating well production rates (OPR, WOR, GOR) in oil and gas fields.
- ANNs offer a superior alternative to traditional methods, balancing accuracy with computational efficiency.
- Further analysis using Monte Carlo simulations provides insights into the ANN model's capabilities and limitations for practical application.
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