TY - JOUR
T1 - Machine-Learning Approach for Forecasting Steam-Assisted Gravity-Drainage Performance in the Presence of Noncondensable Gases
AU - Canbolat, Serhat
AU - Artun, Emre
N1 - Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.
PY - 2022/6/21
Y1 - 2022/6/21
N2 - Steam-assisted gravity drainage (SAGD) is an effective enhanced oil recovery method for heavy oil reservoirs. The addition of certain amounts of noncondensable gases (NCG) may reduce the steam consumption, yet this requires new design-related decisions to be made. In this study, we aimed to develop a machine-learning-based forecasting model that can help in the design of SAGD applications with NCG. Experiments with or without carbon dioxide (CO2) or n-butane (n-C4H10) mixed with steam were performed in a scaled physical model to explore SAGD mechanisms. The model was filled with crushed limestone that was premixed with heavy oil of 12.4° API gravity. Throughout the experiments, temperature, pressure, and production were continuously monitored. The experimental results were used to train neural-network models that can predict oil recovery (%) and cumulative steam-oil ratio (CSOR). The input parameters included injected gas composition, prior saturation with CO2or n-C4H10, separation between wells, and pore volume injected. Among different neural-network architectures tested, a 3-hidden-layer structure with 40, 30, and 20 neurons was chosen as the forecasting model. The model was able to predict oil recovery and CSOR with R2values of 0.98 and 0.95, respectively. Variable importance analysis indicated that pore volume injected, distance between wells, and prior CO2saturation are the most critical parameters that would affect the performance, in agreement with the experiments.
AB - Steam-assisted gravity drainage (SAGD) is an effective enhanced oil recovery method for heavy oil reservoirs. The addition of certain amounts of noncondensable gases (NCG) may reduce the steam consumption, yet this requires new design-related decisions to be made. In this study, we aimed to develop a machine-learning-based forecasting model that can help in the design of SAGD applications with NCG. Experiments with or without carbon dioxide (CO2) or n-butane (n-C4H10) mixed with steam were performed in a scaled physical model to explore SAGD mechanisms. The model was filled with crushed limestone that was premixed with heavy oil of 12.4° API gravity. Throughout the experiments, temperature, pressure, and production were continuously monitored. The experimental results were used to train neural-network models that can predict oil recovery (%) and cumulative steam-oil ratio (CSOR). The input parameters included injected gas composition, prior saturation with CO2or n-C4H10, separation between wells, and pore volume injected. Among different neural-network architectures tested, a 3-hidden-layer structure with 40, 30, and 20 neurons was chosen as the forecasting model. The model was able to predict oil recovery and CSOR with R2values of 0.98 and 0.95, respectively. Variable importance analysis indicated that pore volume injected, distance between wells, and prior CO2saturation are the most critical parameters that would affect the performance, in agreement with the experiments.
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U2 - 10.1021/acsomega.2c01939
DO - 10.1021/acsomega.2c01939
M3 - Article
AN - SCOPUS:85133323650
SN - 2470-1343
VL - 7
SP - 21119
EP - 21130
JO - ACS Omega
JF - ACS Omega
IS - 24
ER -