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AuthorAlipour, M.
AuthorEsatyana, E.
AuthorSakhaee-Pour, A.
AuthorSadooni, F. N.
AuthorAl-Kuwari, H. A.
Available date2023-09-10T05:49:32Z
Publication Date2021-05-01
Publication NameJournal of Petroleum Science and Engineering
Identifierhttp://dx.doi.org/10.1016/j.petrol.2020.108202
CitationAlipour, M., Esatyana, E., Sakhaee-Pour, A., Sadooni, F. N., & Al-Kuwari, H. A. (2021). Characterizing fracture toughness using machine learning. Journal of Petroleum Science and Engineering, 200, 108202.‏
ISSN09204105
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099147982&origin=inward
URIhttp://hdl.handle.net/10576/47337
AbstractThe existing models for fracture toughness characterization based on nanoindentations that account for the fracture length are limited to simple (ideal) geometries that are absent in shales. The present study proposes two conceptual models to estimate the fracture length created by nanoindentations in shales. It also presents a workflow to apply the conceptual models and uses machine learning, enabling a systematic and automated analysis. The conceptual models assume that the induced fracture is in the first mode to determine the fracture toughness. In this study, fracture toughness is also determined by the energy method that relates the load-displacement hysteresis to the fracture toughness without restricting the fracture mode. The present study sheds light on the complexities of characterizing fracture toughness using nanoindentations and has applications in the petroleum industry. The conceptual models are appealing for formation characterization using small pieces, such as drill cuttings, when large samples (~2.5 cm) required for conventional tests are unavailable. The conceptual models have applications in estimating fracture toughness when the induced fracture patterns become more complex.
SponsorThe authors would like to acknowledge the support of the Qatar National Research Fund (a member of the Qatar Foundation ) through Grant # NPRP11S-0109–180241 . The findings achieved herein are solely the responsibility of the authors. The authors are also very grateful for the constructive comments of the reviewers.
Languageen
PublisherElsevier B.V.
SubjectFracture toughness
Image processing
Machine learning
Nanoindentation
TitleCharacterizing fracture toughness using machine learning
TypeArticle
Volume Number200
dc.accessType Abstract Only


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