用生成对抗网络填补钻井数据空缺，提升油气勘探准确性
Well log data generation and imputation using sequence-based generative adversarial networks
- 设计双模型框架：时序GAN生成数据，序列GAN补全缺失值
- 在北海数据上实现最高0.921的R²和低于8.3%的平均误差
- 适合地质勘探、油藏评价等需要高可靠数据的场景
钻井测井分析对油气勘探至关重要，可提供地下地质构造的详细信息。然而，由于设备限制、操作困难及恶劣地下条件，测井数据常存在缺失与不准确，导致储层评价不确定性增加。解决这一问题需高效的数据生成与缺失值填补方法，以确保数据完整性和可靠性。本文提出一种基于序列生成对抗网络（GAN）的新框架，专门用于测井数据生成与填补。该框架集成两种序列型GAN模型：时序生成对抗网络（TSGAN）用于生成合成数据，序列生成对抗网络（SeqGAN）用于填补缺失数据。在荷兰北海地区某数据集上进行测试，分别针对5、10、50个数据点的片段评估。实验结果表明，该方法在空间序列分析中优于其他深度学习模型，对应R²分别为0.921、0.899、0.594，平均绝对百分比误差（MAPE）为8.320、0.005、151.154，平均绝对误差（MAE）为0.012、0.005、0.032。该方法为地球科学领域，尤其是测井数据分析中的数据完整性与可用性树立了新基准。
原文摘要 · Abstract (English)
Well log analysis is crucial 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, focusing on different sections of 5, 10, and 50 data points. Experimental results demonstrate that this approach achieves superior accuracy in filling data gaps compared to other deep learning models for spatial series analysis. The method yielded R^2 values of 0.921, 0.899, and 0.594, with corresponding mean absolute percentage error (MAPE) values of 8.320, 0.005, and 151.154, and mean absolute error (MAE) values of 0.012, 0.005, and 0.032, respectively. These results set a new benchmark for data integrity and utility in geosciences, particularly in well log data analysis.
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