The aim of this work is to create and apply a methodological approach for predicting gas traps from 3D seismic data and gas well testing. The paper formalizes the approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within the seismic wavefield. The training dataset thus created is used in a process stack of sequential application of data processing methods and ensemble machine learning algorithms. As a result, a cube of calibrated probabilities of belonging of the study space to gas reservoirs was obtained. The high efficiency of this approach is shown on a delayed test sample of three wells (blind wells). The final value of the gas reservoir prediction quality metric f1 score was 0.893846.

翻译：本研究旨在构建并应用一种基于3D地震数据及气井测试的气藏预测方法。论文通过在地震波场中选取具有已知含气饱和度与渗流特性的数据体，系统规范了训练数据集的构建流程。所构建的训练集经数据处理方法与集成机器学习算法的顺序处理栈，最终生成研究空间含气储层归属概率的校准数据体。基于三口盲井的延迟测试样本验证表明，该方法具有显著有效性——气藏预测质量评价指标f1分数的最终值为0.893846。