mlsanityData to 14 Oct 2026

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

arXiv 2212.00796 ↗First posted 15 Oct 2022eess.IV cs.LGcited by 0

Not on the map. Why?

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.

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

8 of 48 references.

References · 8

  1. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  2. 2018PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learningcited by 122
  3. 2015Spatio-temporal video autoencoder with differentiable memorycited by 32
  4. 2021Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regressioncited by 15
  5. 2019Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classificationcited by 8
  6. 2019Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural networkcited by 4
  7. 2019FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognitioncited by 3
  8. 2020Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulationcited by 1

Built on by

Not cited yet.