Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning and Data Assimilation for Real-Time Production Prediction
  in Natural Gas Wells   The prediction of the gas production from mature gas wells, due to their
complex end-of-life behavior, is challenging and crucial for operational
decision making. In this paper, we apply a modified deep LSTM model for
prediction of the gas flow rates in mature gas wells, including the
uncertainties in input parameters. Additionally, due to changes in the system
in time and in order to increase the accuracy and robustness of the prediction,
the Ensemble Kalman Filter (EnKF) is used to update the flow rate predictions
based on new observations. The developed approach was tested on the data from
two mature gas production wells in which their production is highly dynamic and
suffering from salt deposition. The results show that the flow predictions
using the EnKF updated model leads to better Jeffreys' J-divergences than the
predictions without the EnKF model updating scheme.

28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ