Convolutional Long Short-Term Memory (convLSTM) for Spatio-Temporal Forecastings of Saturations and Pressure in the SACROC Field. (arXiv:2212.00796v1 [eess.IV])

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.