The fluid oil and gas volumes (S1) retained within the shales are one of the most important parameter of producible fluid oil and gas saturations of shales together with total organic carbon content. The S1 volumes can directly be obtained by Rock-Eval pyrolysis analysis. However, it is time consuming and not practical to obtain samples from all intervals of all wells in any shale play. S1 volumes prediction with a deep learning (DL) model have increasingly became important with the booming exploration and development of shale oil and gas resources. S1 volumes of shales are controlled by organic matter richness, type and maturity together with reservoir quality and adsorption capacity which are mainly effected by age, depth, organic content, maturity and mineralogy. A dataset consisting of 331 samples from 19 wells of various locations of the world-class organic-rich shales of the Niobrara, Eagle Ford, Barnett, Haynesville, Woodford, Vaca Muerta and Dadaş has been used to determination of a DL model for S1 volumes prediction using Python 3 programing environment with Tensorflow and Keras open-source libraries. The DL model that contains 5 dense layers and, 1024, 512, 256, 128 and 128 neurons has been predicted S1 volumes of shales as high as R² = 0.97 from the standard petroleum E&P activities. The DL model has also successfully been applied to S1 volumes prediction of the Bakken and Marcellus shales of the North America. The prediction of the S1 volumes show that the shales have lower to higher reservoir quality and, oil and gas production rate that are well-matches with former studies.

页岩中滞留的流体油气体积（S1）是页岩可采流体油气饱和度与总有机碳含量的最重要参数之一。S1 体积可以通过 Rock-Eval 热解分析直接获得。然而，从任何页岩区段的所有井的所有层段获取样本既费时又不切实际。随着页岩油气资源勘探开发的蓬勃发展，利用深度学习 (DL) 模型预测 S1 体积变得越来越重要。页岩的 S1 体积受有机质丰富度、类型和成熟度以及储层质量和吸附能力的控制，而储层质量和吸附能力主要受年龄、深度、有机质含量、成熟度和矿物学的影响。由来自 Niobrara、Eagle Ford、Barnett、Haynesville、Woodford、Vaca Muerta 和 Dadaş 等世界级富有机质页岩不同位置的 19 口井的 331 个样本组成的数据集已用于确定 S1 体积的 DL 模型使用带有 Tensorflow 和 Keras 开源库的 Python 3 编程环境进行预测。包含 5 个致密层和 1024、512、256、128 和 128 个神经元的 DL 模型已预测页岩的 S1 体积高达 R²  = 0.97 来自标准石油勘探与生产活动。DL 模型还成功应用于北美 Bakken 和 Marcellus 页岩的 S1 体积预测。S1 体积的预测表明，页岩具有从低到高的储层质量和油气产量，与以前的研究非常匹配。