Impacts of fracture properties on the formation and development of stimulated reservoir volume: A global sensitivity analysis

Zhu, Weiwei; He, Xupeng; Li, Yiteng; Lei, Gang; Santoso, Ryan; Wang, Moran

doi:10.1016/j.petrol.2022.110852

Cited by 8 publications

(6 citation statements)

References 64 publications

(58 reference statements)

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“…A water-gas flow simulation in a discrete fracture matrix system is conducted with UNCONG (Li et al, 2015). Detailed settings of the simulation, such as the relative permeability curve in the matrix and fractures, and boundary conditions, are consistent with previous work (Zhu, He, Li, et al, 2022). The fracture permeability is five orders of magnitude larger than that of the matrix to mimic a low-permeable formation.…”

Section: Impact Of Fracture Sealingmentioning

confidence: 86%

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

Zhu,

He,

Patzek

et al. 2024

Geophysical Research Letters

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Complex natural fracture networks typically consist of multiple clusters, whose connectivity is rarely quantified. Therefore, for each identified fracture network, we propose a connectivity metric that accounts for individual fracture clusters and their interactions. This metric evaluates contributions from all fracture clusters, considering their relative sizes and interactions among the isolated clusters, which in turn depend on the hydraulic conductance of the interconnecting rock matrix. Furthermore, we investigate how the system connectivity depends on fracture sealing, alterations of central clusters, and cluster linkage. Fracture sealing strongly impacts overall fracture connectivity, with 5 percent of sealed fractures reducing connectivity by 20 percent. The connectivity reduction is small when transitioning the central cluster from the largest to the smallest one. However, the largest cluster significantly contributes to overall connectivity, while the smallest one contributes minimally. Natural fracture networks increase connectivity by linking more clusters, with heterogeneity and anisotropy playing pivotal roles.

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Section: Impact Of Fracture Sealingmentioning

confidence: 86%

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

Zhu,

He,

Patzek

et al. 2024

Geophysical Research Letters

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“…The new fracture direction generated during the pyrolysis of the oil shale reservoir is mainly along the bedding direction. The discrete fracture network (DFN) model obtained by random simulation has been widely recognized by many scholars. − …”

Section: Random Fracture and Thm Coupling Modelmentioning

confidence: 99%

Analysis of Effective Pyrolysis Zone and Heat Loss in Oil Shale Reservoir with Random Fractures

Yu,

Tang,

Zhang

et al. 2023

ACS Omega

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“…Zhu et al. (2022) proposed a novel method to explicitly represent SRV using discrete fracture networks coupled with a given failure criterion. However, their model greatly simplifies the hydraulic fracturing process, and an in‐depth investigation of the possible influential factors is necessary.…”

Section: Introductionmentioning

confidence: 99%

“…Current available techniques, such as microseismic monitoring, tiltmeter measurement, and electromagnetic imaging, cannot reveal the detailed internal structure of SRV. Zhu et al (2022) proposed a novel method to explicitly represent SRV using discrete fracture networks coupled with a given failure criterion. However, their model greatly simplifies the hydraulic fracturing process, and an in-depth investigation of the possible influential factors is necessary.…”

mentioning

confidence: 99%

Numerical Investigation of Influential Factors in Hydraulic Fracturing Processes Using Coupled Discrete Element‐Lattice Boltzmann Method

Zhu,

Chen,

et al. 2023

JGR Solid Earth

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Hydraulic fracturing is widely used to stimulate unconventional reservoirs, but a systematic and comprehensive investigation into the hydraulic fracturing process is insufficient. In this work, a discrete element‐lattice Boltzmann method is implemented to simulate the hydro‐mechanical behavior in a hydraulic fracturing process. Different influential factors, including treatment parameters (injection rates and fluid viscosity), formation parameters (in situ stress states and natural fractures) and rock properties (heterogeneity of rock strengths and rock permeability), are considered and their impacts on the initiation and propagation of hydraulic fractures are evaluated. A higher injection rate, increased viscosity, and larger in situ stress will lead to an increase in the initiation pressure. Conversely, higher formation permeability and a greater degree of heterogeneity in bond strengths will result in a decrease in the initiation pressure. The complexity of generated fractures is significantly influenced by the injection rate and degree of heterogeneity. However, fluid viscosity, in situ stress states, and formation permeability individually do not affect the geometrical complexity. Shear displacement can occur during a hydraulic fracturing process due to increased pore pressure and variations in in situ stress caused by injected fluid. Low‐viscosity fluid with a high injection rate can have a significant pressure buildup and generate complex fracture networks in low‐permeability heterogeneous formations. Natural fractures can significantly impact the complexity of generated fractures, while more in‐depth research is required regarding complex natural fracture distributions.

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Impacts of fracture properties on the formation and development of stimulated reservoir volume: A global sensitivity analysis

Cited by 8 publications

References 64 publications

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

Analysis of Effective Pyrolysis Zone and Heat Loss in Oil Shale Reservoir with Random Fractures

Numerical Investigation of Influential Factors in Hydraulic Fracturing Processes Using Coupled Discrete Element‐Lattice Boltzmann Method

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