Modeling natural fracture networks using improved geostatistical inferences

Akshat Chandna, Sanjay Srinivasan

Research output: Contribution to journalConference article

Abstract

Estimation of a reservoir's production potential depends on accurate physical and mathematical modeling of natural fracture networks. However, prediction of spatial locations and connectivity of these fracture networks is uncertain due to lack of sufficient data to model them. A stochastic simulation technique has been developed based on Multiple Point Statistics (MPS) using non-gridded training images and flexible spatial templates. The local angle of propagation of fractures are simulated based on the pattern of fractures in the vicinity of the simulation mode. The fracture is propagated in the simulated direction and a new set of simulation nodes are generated. The process is continued until the target fracture lengths are attained. A synthetic case was successfully used for demonstrating the working of the algorithm.

Original languageEnglish (US)
Pages (from-to)6073-6078
Number of pages6
JournalEnergy Procedia
Volume158
DOIs
StatePublished - Jan 1 2019
Event10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, China
Duration: Aug 22 2018Aug 25 2018

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All Science Journal Classification (ASJC) codes

  • Energy(all)

Cite this

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title = "Modeling natural fracture networks using improved geostatistical inferences",
abstract = "Estimation of a reservoir's production potential depends on accurate physical and mathematical modeling of natural fracture networks. However, prediction of spatial locations and connectivity of these fracture networks is uncertain due to lack of sufficient data to model them. A stochastic simulation technique has been developed based on Multiple Point Statistics (MPS) using non-gridded training images and flexible spatial templates. The local angle of propagation of fractures are simulated based on the pattern of fractures in the vicinity of the simulation mode. The fracture is propagated in the simulated direction and a new set of simulation nodes are generated. The process is continued until the target fracture lengths are attained. A synthetic case was successfully used for demonstrating the working of the algorithm.",
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Modeling natural fracture networks using improved geostatistical inferences. / Chandna, Akshat; Srinivasan, Sanjay.

In: Energy Procedia, Vol. 158, 01.01.2019, p. 6073-6078.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Modeling natural fracture networks using improved geostatistical inferences

AU - Chandna, Akshat

AU - Srinivasan, Sanjay

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N2 - Estimation of a reservoir's production potential depends on accurate physical and mathematical modeling of natural fracture networks. However, prediction of spatial locations and connectivity of these fracture networks is uncertain due to lack of sufficient data to model them. A stochastic simulation technique has been developed based on Multiple Point Statistics (MPS) using non-gridded training images and flexible spatial templates. The local angle of propagation of fractures are simulated based on the pattern of fractures in the vicinity of the simulation mode. The fracture is propagated in the simulated direction and a new set of simulation nodes are generated. The process is continued until the target fracture lengths are attained. A synthetic case was successfully used for demonstrating the working of the algorithm.

AB - Estimation of a reservoir's production potential depends on accurate physical and mathematical modeling of natural fracture networks. However, prediction of spatial locations and connectivity of these fracture networks is uncertain due to lack of sufficient data to model them. A stochastic simulation technique has been developed based on Multiple Point Statistics (MPS) using non-gridded training images and flexible spatial templates. The local angle of propagation of fractures are simulated based on the pattern of fractures in the vicinity of the simulation mode. The fracture is propagated in the simulated direction and a new set of simulation nodes are generated. The process is continued until the target fracture lengths are attained. A synthetic case was successfully used for demonstrating the working of the algorithm.

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