Seismic prediction of fluid and lithofacies distribution is of great interest to reservoir characterization, geologic model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an extreme gradient boosting-based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and $V_{\mathrm{P}}/V_{\mathrm{S}}$ ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all of the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of our workflow.

中文翻译：

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基于测井数据和地震反演的流体和岩相预测：一种机器学习方法

流体和岩相分布的地震预测对储层表征、地质模型构建和流动单元划分具有重要意义。在机器学习框架下从地震数据推断流体和岩相通常会受到特征有限、数据集不平衡和空间约束的问题。因此，提出了一种基于极端梯度提升的工作流程，将特征工程、数据平衡和空间约束考虑在内，通过整合测井和地震数据来预测流体和岩相分布。基于简单数学运算和领域知识构建的特征集优于由 P 阻抗和${伏}_{\mathrm{磷}}/{伏}_{\mathrm{秒}}$比率。开发了根据可用井到目标区域的距离表征训练样本权重的径向基函数，对模型训练过程施加空间约束，显着提高了含气砂岩的预测精度和可靠性。综合少数过采样技术和空间约束相结合的策略进一步提高了含气砂岩的 F1 分数，也有利于所有相的整体预测性能。组合策略在叠前地震反演结果中的应用产生了更地质合理的流体空间分布，从而验证了我们工作流程的稳健性和有效性。