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Towards realtime 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 graphbased reducedorder model with the highfidelity 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 reducedorder model provides a conservative estimate. Moreover, we found that except for firstpassage times and late arriving mass, the production curves from the reducedorder model predict transport accurately. However, it is to be noted that the results are inspite of trading a threedimensional 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 reducedorder 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 reducedorder model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make realtime decisions for optimal production.
 Authors:

 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):
 LAUR1928682
Journal ID: ISSN 09204105
 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 09204105
 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 realtime 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 realtime 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 realtime 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 realtime 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 graphbased reducedorder model with the highfidelity 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 reducedorder model provides a conservative estimate. Moreover, we found that except for firstpassage times and late arriving mass, the production curves from the reducedorder model predict transport accurately. However, it is to be noted that the results are inspite of trading a threedimensional 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 reducedorder 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 reducedorder model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make realtime 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}
}