标题： 基于序列生成对抗网络的测井数据生成与填补 显示英文标题

标题： Well log data generation and imputation using sequence-based generative adversarial networks

摘要： 测井分析对于油气勘探至关重要，能够提供地下地质构造的详细信息。 然而，由于设备限制、操作挑战以及恶劣的地下条件，测井数据中的空白和不准确之处常常会引入储层评估中的重大不确定性。 解决这些挑战需要有效的合成数据生成方法和缺失数据精确填补的方法，以确保数据的完整性和可靠性。 本研究介绍了一种新颖的框架，利用基于序列的生成对抗网络（GAN）专门设计用于测井数据的生成和填补。 该框架集成了两种不同的基于序列的GAN模型：用于生成合成测井数据的时间序列GAN（TSGAN）和用于填补缺失数据的序列GAN（SeqGAN）。 这两种模型都在来自荷兰北海地区的数据集上进行了测试，重点关注长度为5、10和50个数据点的不同部分。 实验结果表明，与用于空间序列分析的其他深度学习模型相比，该方法在填补数据空白方面具有更高的准确性。 该方法分别获得了R²值为0.921、0.899和0.594，相应的平均绝对百分比误差（MAPE）值为8.320、0.005和151.154，平均绝对误差（MAE）值为0.012、0.005和0.032。 这些结果为地球科学领域特别是测井数据分析中的数据完整性和实用性设定了新的基准。 显示英文摘要

摘要： 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.