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Well log data generation and imputation using sequence based generative adversarial networks.
Abdulrahman Al-Fakih1, A Koeshidayatullah2, Tapan Mukerji3
1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum Minerals, 31261, Dhahran, Saudi Arabia. alja2014ser@gmail.com.
This study introduces a novel framework using generative adversarial networks (GANs) for well log data imputation and synthetic data generation. The approach enhances data reliability for hydrocarbon exploration by accurately filling data gaps and creating realistic log data.
Area of Science:
- Geosciences
- Petroleum Engineering
- Data Science
Background:
- Well log data is crucial for hydrocarbon exploration but often contains gaps and inaccuracies.
- These data deficiencies introduce uncertainties in reservoir evaluation.
- Effective methods for synthetic data generation and missing data imputation are essential for reliable analysis.
Purpose of the Study:
- To develop and evaluate a novel framework for well log data generation and imputation.
- To address challenges posed by incomplete and inaccurate well log data.
- To improve the integrity and utility of well log data in geosciences.
Main Methods:
- Utilized sequence-based generative adversarial networks (GANs).
- Integrated Time Series GAN (TSGAN) for synthetic data generation and Sequence GAN (SeqGAN) for data imputation.
- Tested the framework on a North Sea, Netherlands dataset with normalized log measurements.
Main Results:
- The imputation method demonstrated superior accuracy in filling data gaps compared to other deep learning models.
- Achieved high R-squared values (up to 0.92) and low Mean Absolute Error (MAE) for imputation.
- Synthetic data generation also yielded promising results with an R-squared of 0.92.
Conclusions:
- The proposed GAN-based framework effectively generates synthetic well log data and imputes missing values.
- This approach significantly enhances data completeness and reliability for reservoir evaluation.
- The study sets a new benchmark for well log data integrity in geoscientific applications.

