A machine learning architecture composed of convolutional long short-term memory (convLSTM) is developed to predict spatiotemporal parameters, that is, saturation and pressure in an oil & gas field namely, the SACROC field in Texas, USA. The spatial parameters are recorded at the end of each month for 360 months, approximately 83% of which is used for training and the rest 17% 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. A workflow is provided describing the entire process of data extraction, preprocessing, sample preparation, training, testing of machine learning models, and error analysis. The structural similarity index measure (SSIM) and normalized root mean squared error (NRMSE) are calculated during training and testing for quantitative error analysis. During the training phase, the predicted frames demonstrate a strong resemblance to the ground truth frames. Moreover, it exhibits superior performance during the initial months of testing. As testing progresses into the later months, deviations increase as errors accumulate over time for relying on past predictions. In summary, convLSTM for spatio-temporal prediction offers significant potential for forecasting and optimizing hydrocarbon recovery from porous media.