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

• Published in: Journal of petroleum science & engineering Vol. 195; p. 107791
• Main Authors: Dana, Saumik, Srinivasan, Shriram, Karra, Satish, Makedonska, Nataliia, Hyman, Jeffrey D., O'Malley, Daniel, Viswanathan, Hari, Srinivasan, Gowri
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.12.2020; Elsevier
• Subjects: 03 NATURAL GAS; Computer Science; Discrete fracture networks; Earth Sciences; Graphs; Hydrocarbon production; Mathematics; Real-time forecasting; Reduced-order model; Uncertainty quantification; Uncertainty quantification; Graphs; Discrete fracture networks; Reduced-order model; Hydrocarbon production; Real-time forecasting
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2020.107791

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. •Flow and transport simulation on DFN mesh is replaced with flow and transport simulation on a graph representation of the DFN.•Graph theory provides a much faster and elegant way for model order reduction of DFNs.•Production curves for the reduced-order model match well with the high fidelity DFN model except early times and late times.