论文标题
卷积长的短期记忆（Convlstm），用于饱和的时空预测和压力的压力
Convolutional Long Short-Term Memory (convLSTM) for Spatio-Temporal Forecastings of Saturations and Pressure in the SACROC Field
论文作者
论文摘要
开发了由卷积长的短期记忆（ConvlstM）组成的机器学习体系结构，以预测美国德克萨斯州Sacroc油田中的时空参数。空间参数记录在每个月末30年（360个月），其中约83％（300个月）用于训练，其余17％（60个月）保留进行测试。通过选择连续十帧作为输入和十个连续的框架以输出为输出，将准备十个连续的帧作为输入来制备。对单个模型进行了用于油，天然气和水饱和的训练，并使用Nesterov加速适应力矩估计（NADAM）优化算法的压力。提供了一个工作流程，以理解数据提取，预处理，样本准备，培训，机器学习模型的测试和错误分析的整个过程。总体而言，时空预测的探测显示在预测多孔介质中时空参数方面有令人鼓舞的结果。
A machine learning architecture composed of convolutional long short-term memory (convLSTM) is developed to predict spatio-temporal parameters in the SACROC oil field, Texas, USA. The spatial parameters are recorded at the end of each month for 30 years (360 months), approximately 83% (300 months) of which is used for training and the rest 17% (60 months) is kept for testing. The samples for the convLSTM models are prepared by choosing ten consecutive frames as input and ten consecutive frames shifted forward by one frame as output. Individual models are trained for oil, gas, and water saturations, and pressure using the Nesterov accelerated adaptive moment estimation (Nadam) optimization algorithm. A workflow is provided to comprehend the entire process of data extraction, preprocessing, sample preparation, training, testing of machine learning models, and error analysis. Overall, the convLSTM for spatio-temporal prediction shows promising results in predicting spatio-temporal parameters in porous media.
