Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning
This work focussed on developing approximate methods for rapidly estimating gas field production performance. Proper orthogonal decomposition (POD) - Radial basis function (RBF) and POD-Autoencoder (AE) Non Intrusive Reduced Order Models (NIROMs) were considered. The accuracy and speed of both NIROM...
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Earth Science & Engineering, Imperial College London
2022
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ftimperialcol:oai:spiral.imperial.ac.uk:10044/1/101797 2023-05-15T17:25:06+02:00 Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning Samuel, Jemimah-Sandra Adebisi Muggeridge, Ann Woodside Petroleum 2022-02 http://hdl.handle.net/10044/1/101797 https://doi.org/10.25560/101797 unknown Earth Science & Engineering, Imperial College London alma http://hdl.handle.net/10044/1/101797 https://doi.org/10.25560/101797 Creative Commons Attribution NonCommercial Licence http://creativecommons.org/licenses/by-nc/4.0/ CC-BY-NC Thesis or dissertation Doctoral Doctor of Philosophy (PhD) 2022 ftimperialcol https://doi.org/10.25560/101797 2023-02-16T23:42:45Z This work focussed on developing approximate methods for rapidly estimating gas field production performance. Proper orthogonal decomposition (POD) - Radial basis function (RBF) and POD-Autoencoder (AE) Non Intrusive Reduced Order Models (NIROMs) were considered. The accuracy and speed of both NIROMs were evaluated for modelling different aspects of gas field modelling including reservoirs with time-varying and mixed production controls, reservoirs with and without aquifer pressure support, and for wells that were (or not ) shut-in during production lifecycle. These NIROMs were applied to predicting the performance of four gas reservoir models: a homogeneous synthetic model; a heterogeneous gas field with 3 wells and structures similar to the Norne Field; a water coning model in radian grid; and a sector model of a real gas field provided by Woodside Petroleum. The POD-RBF and POD-AE NIROMs were trained using the simulation solutions from a commercial reservoir simulator (ECLIPSE): grid distributions of pressure and saturations as well as time series production data such as production rates, cumulative productions and pressures. Different cases were run based on typical input parameters usually used in field performance studies. The simulation solutions were then standardised to zero mean and reduced into hyperspace using POD. In most cases, the optimum number of POD basis functions (99.9% energy criterion) of the solutions (training data) were used to reduce the training data into a lower-dimensional hyperspace space. The reduced training data and their corresponding parameter values were combined to form sample and response arrays based on a cause and effect pattern. RBF or AE was then used to interpolate the weighting coefficients that represented the dynamics of the gas reservoir as captured within the reduced training data. These weighting coefficients were used to propagate the prediction of new unseen simulation cases for the duration of predictions. The simulation results from either or both NIROMs was ... Doctoral or Postdoctoral Thesis Norne field Imperial College London: Spiral |
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Open Polar |
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Imperial College London: Spiral |
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ftimperialcol |
language |
unknown |
description |
This work focussed on developing approximate methods for rapidly estimating gas field production performance. Proper orthogonal decomposition (POD) - Radial basis function (RBF) and POD-Autoencoder (AE) Non Intrusive Reduced Order Models (NIROMs) were considered. The accuracy and speed of both NIROMs were evaluated for modelling different aspects of gas field modelling including reservoirs with time-varying and mixed production controls, reservoirs with and without aquifer pressure support, and for wells that were (or not ) shut-in during production lifecycle. These NIROMs were applied to predicting the performance of four gas reservoir models: a homogeneous synthetic model; a heterogeneous gas field with 3 wells and structures similar to the Norne Field; a water coning model in radian grid; and a sector model of a real gas field provided by Woodside Petroleum. The POD-RBF and POD-AE NIROMs were trained using the simulation solutions from a commercial reservoir simulator (ECLIPSE): grid distributions of pressure and saturations as well as time series production data such as production rates, cumulative productions and pressures. Different cases were run based on typical input parameters usually used in field performance studies. The simulation solutions were then standardised to zero mean and reduced into hyperspace using POD. In most cases, the optimum number of POD basis functions (99.9% energy criterion) of the solutions (training data) were used to reduce the training data into a lower-dimensional hyperspace space. The reduced training data and their corresponding parameter values were combined to form sample and response arrays based on a cause and effect pattern. RBF or AE was then used to interpolate the weighting coefficients that represented the dynamics of the gas reservoir as captured within the reduced training data. These weighting coefficients were used to propagate the prediction of new unseen simulation cases for the duration of predictions. The simulation results from either or both NIROMs was ... |
author2 |
Muggeridge, Ann Woodside Petroleum |
format |
Doctoral or Postdoctoral Thesis |
author |
Samuel, Jemimah-Sandra Adebisi |
spellingShingle |
Samuel, Jemimah-Sandra Adebisi Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
author_facet |
Samuel, Jemimah-Sandra Adebisi |
author_sort |
Samuel, Jemimah-Sandra Adebisi |
title |
Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
title_short |
Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
title_full |
Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
title_fullStr |
Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
title_full_unstemmed |
Fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
title_sort |
fast modelling of gas reservoirs using non-intrusive reduced order modelling and machine learning |
publisher |
Earth Science & Engineering, Imperial College London |
publishDate |
2022 |
url |
http://hdl.handle.net/10044/1/101797 https://doi.org/10.25560/101797 |
genre |
Norne field |
genre_facet |
Norne field |
op_relation |
alma http://hdl.handle.net/10044/1/101797 https://doi.org/10.25560/101797 |
op_rights |
Creative Commons Attribution NonCommercial Licence http://creativecommons.org/licenses/by-nc/4.0/ |
op_rightsnorm |
CC-BY-NC |
op_doi |
https://doi.org/10.25560/101797 |
_version_ |
1766116397668106240 |