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dc.contributor.authorZhu, Weiwei
dc.contributor.authorHe, Xupeng
dc.contributor.authorSantoso‬, ‪Ryan Kurniawan
dc.contributor.authorLei, Gang
dc.contributor.authorPatzek, Tadeusz
dc.contributor.authorWang, Moran
dc.date.accessioned2021-10-13T07:28:57Z
dc.date.available2021-10-13T07:28:57Z
dc.date.issued2021-10-11
dc.identifier.citationZhu, W., He, X., Santoso‬, ‪Ryan K., Lei, G., Patzek, T., & Wang, M. (2021). Enhancing Fracture Network Characterization: A Data-Driven, Outcrop-Based Analysis. doi:10.1002/essoar.10508232.1
dc.identifier.doi10.1002/essoar.10508232.1
dc.identifier.urihttp://hdl.handle.net/10754/672820
dc.description.abstractThe stochastic discrete fracture network (SDFN) model is a practical approach to model complex fracture systems in the subsurface. However, it is impossible to validate the correctness and quality of an SDFN model because the comprehensive subsurface structure is never known. We utilize a pixel-based fracture detection algorithm to digitize 80 published outcrop maps of different scales at different locations. The key fracture properties, including fracture lengths, orientations, intensities, topological structures, clusters and flow are then analyzed. Our findings provide significant justifications for statistical distributions used in SDFN modellings. In addition, the shortcomings of current SDFN models are discussed. We find that fracture lengths follow multiple (instead of single) power-law distributions with varying exponents. Large fractures tend to have large exponents, possibly because of a small coalescence probability. Most small-scale natural fracture networks have scattered orientations, corresponding to a small κ value (κ<3) in a von Mises--Fisher distribution. Large fracture systems collected in this research usually have more concentrated orientations with large κ values. Fracture intensities are spatially clustered at all scales. A fractal spatial density distribution, which introduces clustered fracture positions, can better capture the spatial clustering than a uniform distribution. Natural fracture networks usually have a significant proportion of T-type nodes, which is unavailable in conventional SDFN models. Thus a rule-based algorithm to mimic the fracture growth and form T-type nodes is necessary. Most outcrop maps show good topological connectivity. However, sealing patterns and stress impact must be considered to evaluate the hydraulic connectivity of fracture networks.
dc.description.sponsorshipThis project was supported by the National Key Research and Development Program of China (No. 2019YFA0708704). The authors would like to thank all editors and anonymous reviewers for their comments and suggestions. All the synthetically generated data and digitized outcrop maps are available online (https://doi.org/10.4121/14865096.v2)
dc.publisherWiley
dc.relation.urlhttp://www.essoar.org/doi/10.1002/essoar.10508232.1
dc.rightsYou are free to: Share — copy and redistribute the material in any medium or format The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
dc.titleEnhancing Fracture Network Characterization: A Data-Driven, Outcrop-Based Analysis
dc.typePreprint
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEnergy Resources & Petroleum Engineering
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentEnergy Resources and Petroleum Engineering Program
dc.contributor.departmentAli I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
dc.eprint.versionPre-print
dc.contributor.institutionTsinghua University
dc.contributor.institutionRWTH Aachen University
dc.contributor.institutionKing Fahd University of Petroleum and Minerals
kaust.personHe, Xupeng
kaust.personPatzek, Tadeusz
refterms.dateFOA2021-10-13T07:30:30Z


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You are free to:
Share — copy and redistribute the material in any medium or format
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Except where otherwise noted, this item's license is described as You are free to: Share — copy and redistribute the material in any medium or format The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.