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Title: Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks

Abstract

In this work, we compare hydrocarbon production curves obtained from a graph-based reduced-order model with the high-fidelity Discrete Fracture Network (DFN) predictions for a fracture network created using data from a real shale site. We observe that the bounds for the high fidelity DFN model lie within the bounds for the reduced order model, implying that the reduced-order model provides a conservative estimate. Moreover, we found that except for first-passage times and late arriving mass, the production curves from the reduced-order model predict transport accurately. However, it is to be noted that the results are inspite of trading a three-dimensional geometry for a reduced system in the form of a graph, one that is 500–1000 times faster in terms of computational efficiency (for this particular application). In addition, we also compare the production curves for large drawdown and small drawdown using our graph approach. The reduced-order model is successful in showing that the long term productivity is higher in case of small drawdown although the initial productivity is higher for large drawdown. Thus, this reduced-order model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make real-time decisions for optimal production.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1659197
Alternate Identifier(s):
OSTI ID: 1809634
Report Number(s):
LA-UR-19-28682
Journal ID: ISSN 0920-4105
Grant/Contract Number:  
89233218CNA000001; 20170103DR; 20170508DR
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Petroleum Science and Engineering
Additional Journal Information:
Journal Volume: 195; Journal ID: ISSN 0920-4105
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
03 NATURAL GAS; Computer Science; Earth Sciences; Mathematics

Citation Formats

Dana, Saumik Prasanta Kumar, Srinivasan, Shriram, Karra, Satish, Makedonska, Nataliia, Hyman, Jeffrey De'Haven, O'Malley, Daniel, Viswanathan, Hari S., and Srinivasan, Gowri. Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks. United States: N. p., 2020. Web. https://doi.org/10.1016/j.petrol.2020.107791.
Dana, Saumik Prasanta Kumar, Srinivasan, Shriram, Karra, Satish, Makedonska, Nataliia, Hyman, Jeffrey De'Haven, O'Malley, Daniel, Viswanathan, Hari S., & Srinivasan, Gowri. Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks. United States. https://doi.org/10.1016/j.petrol.2020.107791
Dana, Saumik Prasanta Kumar, Srinivasan, Shriram, Karra, Satish, Makedonska, Nataliia, Hyman, Jeffrey De'Haven, O'Malley, Daniel, Viswanathan, Hari S., and Srinivasan, Gowri. Sun . "Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks". United States. https://doi.org/10.1016/j.petrol.2020.107791.
@article{osti_1659197,
title = {Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks},
author = {Dana, Saumik Prasanta Kumar and Srinivasan, Shriram and Karra, Satish and Makedonska, Nataliia and Hyman, Jeffrey De'Haven and O'Malley, Daniel and Viswanathan, Hari S. and Srinivasan, Gowri},
abstractNote = {In this work, we compare hydrocarbon production curves obtained from a graph-based reduced-order model with the high-fidelity Discrete Fracture Network (DFN) predictions for a fracture network created using data from a real shale site. We observe that the bounds for the high fidelity DFN model lie within the bounds for the reduced order model, implying that the reduced-order model provides a conservative estimate. Moreover, we found that except for first-passage times and late arriving mass, the production curves from the reduced-order model predict transport accurately. However, it is to be noted that the results are inspite of trading a three-dimensional geometry for a reduced system in the form of a graph, one that is 500–1000 times faster in terms of computational efficiency (for this particular application). In addition, we also compare the production curves for large drawdown and small drawdown using our graph approach. The reduced-order model is successful in showing that the long term productivity is higher in case of small drawdown although the initial productivity is higher for large drawdown. Thus, this reduced-order model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make real-time decisions for optimal production.},
doi = {10.1016/j.petrol.2020.107791},
journal = {Journal of Petroleum Science and Engineering},
number = ,
volume = 195,
place = {United States},
year = {2020},
month = {8}
}

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This content will become publicly available on August 30, 2021
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