ArticleScientific reports2025
Well log data generation and imputation using sequence based generative adversarial networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Personalized talent cultivation and academic prediction framework for higher education based on the HA-GNN-LSTM architecture.Scientific reports · 2026Article
- Predicting drilling fluid lost circulation volume and radius using petrophysical well logs and machine learning approaches.Scientific reports · 2026Article
- Integrated CNN-LSTM-XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends.Scientific reports · 2026Article
- Cross-well machine learning prediction of sonic logs in Newfoundland and Labrador.Scientific reports · 2026Article
- XGBoost regression for robust acoustic impedance prediction in the absence of density and sonic logs.Scientific reports · 2025Article
- Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China.Scientific reports · 2025Article
- Ensemble learning for prediction of inorganic scale formation: A case study in Oman.Scientific reports · 2025Article
- Estimated ultimate recovery prediction of shale gas wells based on stacked integrated learning algorithm.Scientific reports · 2025Article
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Authors and funding
5 authors.
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Abstract
Well log analysis is significant for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir evaluation. Addressing these challenges requires effective methods for both synthetic data generation and precise imputation of missing data, ensuring data completeness and reliability. This study introduces a novel framework utilizing sequence-based generative adversarial networks (GANs) specifically designed for well log data generation and imputation. The framework integrates two distinct sequence-based GAN models: time series GAN (TSGAN) for generating synthetic well log data and sequence GAN (SeqGAN) for imputing missing data. Both models were tested on a dataset from the North Sea, Netherlands region. For the imputation task, the input comprises logs with missing values and the output is the corresponding imputed logs; for the synthetic data generation task, the input is complete real logs and the output is synthetic logs that mimic the statistical properties of the original data. All log measurements are normalized to a 0-1 range using min-max scaling, and error metrics are reported in these normalized units. Different sections of 5, 10, and 50 data points were used. Experimental results demonstrate that this approach achieves superior accuracy in filling data gaps compared to other deep learning models for spatial series analysis. The imputation method yielded [Formula: see text] values of 0.92, 0.86, and 0.57, with corresponding mean absolute percentage error (MAPE) values of 8.320, 0.005, and 166.6, and mean absolute error (MAE) values of 0.012, 0.002, and 0.03, respectively. The synthetic generation yielded [Formula: see text] of 0.92, MAE, of 0.35, and MRLE of 0.01. These results set a new benchmark for data integrity and utility in geosciences, particularly in well log data analysis.
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