# Convolutional Long Short-Term Memory (convLSTM) for Spatio-Temporal Forecastings of Saturations and Pressure in the SACROC Field

arXiv: https://arxiv.org/abs/2212.00796 · HTML: https://mlsanity.com/paper/2212.00796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning](https://mlsanity.com/paper/1804.06300/) (2018)
- [Spatio-temporal video autoencoder with differentiable memory](https://mlsanity.com/paper/1511.06309/) (2015)
- [Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression](https://mlsanity.com/paper/2109.08248/) (2021)
- [Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.03577/) (2019)
- [Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network](https://mlsanity.com/paper/1902.06927/) (2019)
- [FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition](https://mlsanity.com/paper/1904.09405/) (2019)
- [Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation](https://mlsanity.com/paper/2010.07747/) (2020)

## Abstract (arXiv)

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.