On July 15,2011,the editorial office of the Journal of Arid Land（JAL） received an e-mail transmitted through Prof.ZHU Cheng,the consultant of the Chinese Society of University Journals in Natural Sciences and the st...On July 15,2011,the editorial office of the Journal of Arid Land（JAL） received an e-mail transmitted through Prof.ZHU Cheng,the consultant of the Chinese Society of University Journals in Natural Sciences and the standing director of China Editology of Science Periodicals. The e-mail was sent by Mariana Boletta, the Senior Editor of Thomson Reuters, who said that JAL has been selected for coverage in Science Citation Index Expanded （SCIE）and Current Contents/Agriculture, Biology ＆ Environmental Sciences, beginning from the initial issue in 2009.展开更多
In November 2011, Journal of Arid Land （JAL） was accepted for indexing in Cambridge Science Abstracts （CSA） and Centre for Agriculture and Bioscience Abstracts （CAB Abstracts） after strict assessments by these t...In November 2011, Journal of Arid Land （JAL） was accepted for indexing in Cambridge Science Abstracts （CSA） and Centre for Agriculture and Bioscience Abstracts （CAB Abstracts） after strict assessments by these two databases.展开更多
Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predic...Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.展开更多
文摘On July 15,2011,the editorial office of the Journal of Arid Land（JAL） received an e-mail transmitted through Prof.ZHU Cheng,the consultant of the Chinese Society of University Journals in Natural Sciences and the standing director of China Editology of Science Periodicals. The e-mail was sent by Mariana Boletta, the Senior Editor of Thomson Reuters, who said that JAL has been selected for coverage in Science Citation Index Expanded （SCIE）and Current Contents/Agriculture, Biology ＆ Environmental Sciences, beginning from the initial issue in 2009.
文摘In November 2011, Journal of Arid Land （JAL） was accepted for indexing in Cambridge Science Abstracts （CSA） and Centre for Agriculture and Bioscience Abstracts （CAB Abstracts） after strict assessments by these two databases.
文摘Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.
