Geophysics and Geophysical Exploration ISSN:1229-1064(Print) 2384-051X(Online) 지구물리와 물리탐사

Alzate, J. H., and Devegowda, D., 2013, Integration of surface seismic, microseismic, and production logs for shale gas characterization: methodology and field application, Interpretation, 1(2), SB37-SB49.

Goodway, B., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters: "λρ," "μρ," and "λ/μ fluid stack" from P and S inversions, SEG Technical Program Expanded Abstracts 1997, 183-186.

Grieser, B., and Bray, J., 2007, Identification of production potential in unconventional reservoirs, SPE Oklahoma City Oil and Gas Symposium/ Production and Operations Symposium, SPE-106623.

Hallam, A., Mukherjee, D., and Chassagne, R., 2022, Multivariate imputation via chained equations for elastic well log imputation and prediction, Applied Computing and Geosciences, 14, 100083.

Hermana, M., Ghosh, D. P., and Sum, C. W., 2019, Elastic-based brittleness estimation from seismic inversion, Exploitation of Unconventional Oil and Gas Resources-Hydraulic Fracturing and Other Recovery and Assessment Techniques, IntechOpen.

Hochreiter, S. and Schmidhuber, J., 1997, Long short-term memory, Neural Computation, MIT Press.

Jarvie, D. M., Hill, R. J., Ruble, T. E., and Pollastro, R. M., 2007, Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment, AAPG Bulletin, 91(4), 475-499.

Jin, X., Shah, S., Truax, J., and Roegiers, J. C., 2014, A practical petrophysical approach for brittleness prediction from porosity and sonic logging in shale reservoirs, SPE Annual Technical Conference and Exhibition, SPE-170972.

Karstens, J., Ahmed, W., Berndt, C., and Class, H., 2017, Focused fluid flow and the sub-seabed storage of CO₂: Evaluating the leakage potential of seismic chimney structures for the Sleipner CO₂ storage operation, Marine and Petroleum Geology, 88, 81-93.

Kim, S. J. and Jun, H. G., 2024, Comparison of machine learning models for efficient elastic property estimation, Kseg Fall Meeting Abstracts 2024, 38-38.

Kwon, S., Ji, M., Park, G., Min, B., and Jeong, H., 2021, Analysis of data disclosure and reservoir model of the Volve Oilfield in the North Sea, Journal of the Korean Society of Mineral and Energy Resources Engineers, 58(4), 353-363 (in Korean with English abstract).

Lai, J., Wang, G., Huang, L., Li, W., Ran, Y., Wang, D., Zhou, Z., and Chen, J., 2015, Brittleness index estimation in a tight shaly sandstone reservoir using well logs, Journal of Natural Gas Science and Engineering, 27, 1536-1545.

Lee, J. and Lumley, D. E., 2023, Predicting shale mineralogical brittleness index from seismic and elastic property logs using interpretable deep learning, Journal of Petroleum Science and Engineering, 220, 111231.

Machek, M., Kalvoda, L., Hladil, J., Roxerová, Z., Vratislav, S., Drahokoupil, J., and Ryukhtin, V., 2018, Petrophysical record of evolution of weakly deformed low-porosity limestone revealed by small-angle neutron scattering, neutron diffraction, and AMS study, Geophysical Journal International, 215(2), 895-908.

Mathia, E., Ratcliffe, K., and Wright, M., 2016, Brittleness index-a parameter to embrace or avoid?, SPE/AAPG/SEG Unconventional Resources Technology Conference, 1156-1165.

Medina, L. A., Lozano, H. A., Mantilla, H. D., and Espinosa Mora, C. A., 2017, Geomechanical characterization for an unconventional shale reservoir with the goal of improving drilling efficiency and sweet spots selection in a field in Colombia, ISRM International Symposium Geomechanics, ISRM-ISG-2017-006.

Mlella, M., Ma, M., Zhang, R., and Mokhtari, M., 2020, Machine learning for geophysical characterization of brittleness: Tuscaloosa Marine Shale case study, Interpretation, 8(3), T589-T597.

Mustafa, A., Tariq, Z., Abdulraheem, A., Mahmoud, M., Kalam, S., and Khan, R. A., 2022, Shale brittleness prediction using machine learning-a Middle East basin case study, AAPG Bulletin, 106(11), 2275-2296.

Ore, T. and Gao, D., 2023, Prediction of reservoir brittleness from geophysical logs using machine learning algorithms, Computers & Geosciences, 171, 105266.

Osorio, J. G. and Muzzio, M. E., 2013, Correlation between microseismicity and geomechanics factors affecting the hydraulic fracturing performance in unconventional reservoirs-a field case in Neuquén, Argentina, ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2013.

Pan, X. P., Zhang, G. Z., and Chen, J. J., 2020, The construction of shale rock physics model and brittleness prediction for high-porosity shale gas-bearing reservoir, Petroleum Science, 17, 658-670.

Perez, R. and Marfurt, K., 2013, Brittleness estimation from seismic measurements in unconventional reservoirs: application to the Barnett Shale, SEG International Exposition and Annual Meeting, 2258-2263.

Rahman, M. J., Fawad, M., and Mondol, N. H., 2020, Organic-rich shale caprock properties of potential CO₂ storage sites in the northern North Sea, offshore Norway, Marine and Petroleum Geology, 122, 104665.

Rickman, R., Mullen, M., Petre, E., Grieser, B., and Kundert, D., 2008, A practical use of shale petrophysics for stimulation design optimization: all shale plays are not clones of the Barnett Shale, SPE Annual Technical Conference and Exhibition, SPE-115258.

Rojas, L. F., Peña, Y. Q., and Carrillo, Z. H., 2016, Brittleness analysis: a methodology to identify sweet spots in shale gas reservoirs, SPE Argentina Exploration and Production of Unconventional Resources Symposium, SPE-180955-MS.

Sanei, M., Ramezanzadeh, A., and Asgari, A., 2023, Building 1D and 3D static reservoir geomechanical properties models in the oil field, Journal of Petroleum Exploration and Production Technology, 13(1), 329-351.

Sari, M., Sarout, J., Poulet, T., Dautriat, J., and Veveakis, M., 2022, The brittle-ductile transition and the formation of compaction bands in the Savonnieres Limestone: impact of the stress and pore fluid, Rock Mechanics and Rock Engineering, 55(11), 6541-6553.

Sen, S., and Ganguli, S. S., 2019, Estimation of pore pressure and fracture gradient in Volve field, Norwegian North Sea, SPE Oil and Gas India Conference and Exhibition, SPE-194578-MS.

Sharma, R. K., and Chopra, S., 2012, New attribute for determination of lithology and brittleness, SEG International Exposition and Annual Meeting, 1-5.

Subiatmono, P., Buntoro, A., Lukmana, A. H., David, M., and Kristanto, D., 2022, Brittleness prediction using sonic and density logs to determine sweet spot of brown shale reservoir, Journal of Multidisciplinary Engineering Science and Technology (JMEST), 9(2), 15078-15084.

Sun, S. Z., Wang, K. N., Yang, P., Li, X. G., Sun, J. X., Liu, B. H., and Jin, K., 2013, Integrated prediction of shale oil reservoir using pre-stack algorithms for brittleness and fracture detection, International Petroleum Technology Conference, IPTC-17048.

Varga, R., Holden, T., and Pendrel, J., 2013, How to maximize recoverable reserves in an unconventional reservoir using reservoir characterization from 3D seismic: a Barnett Shale case study, CSEG Recorder, 38(4), 64-70.

Wang, F. P. and Gale, J. F., 2009, Screening criteria for shale-gas systems, Gulf Coast Association of Geological Societies Transactions, 59, 779-793.

Wang, X. and Tsvankin, I., 2013, Multiparameter TTI tomography of P-wave reflection and VSP data, Geophysics, 78(5), WC51-WC63.

Wen, X., Zhao, Y., Xie, C., and Li, C., 2024, Direct seismic inversion of a novel brittleness index based on petrophysical modeling in shale reservoirs, IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15.

Wood, D. A., 2021, Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities, Geoscience Frontiers, 12(6), 101087.

Yang, S. Q., Jing, H. W., and Xu, T., 2014, Mechanical behavior and failure analysis of brittle sandstone specimens containing combined flaws under uniaxial compression, Journal of Central South University, 21(5), 2059-2073.

Yang, Y., Sone, H., Hows, A., and Zoback, M. D., 2013, Comparison of brittleness indices in organic-rich shale formations, ARMA US Rock Mechanics/Geomechanics Symposium, 7.

Zamanzadeh Talkhouncheh, M., Davoodi, S., Larki, B., Mehrad, M., Rashidi, S., and Vasfi, M., 2023, A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms, Earth Science Informatics, 16(4), 3387-3416.

Zhang, C., Dong, D., Wang, Y., and Guan, Q., 2017, Brittleness evaluation of the upper Ordovician Wufeng-Lower Silurian Longmaxi shale in Southern Sichuan Basin, China, Energy Exploration and Exploitation, 35(4), 430-443.

Zheng, Y., Zhang, L., Wu, P., Guo, X., Li, M., and Zhu, F., 2024, Physical and mechanical properties and damage mechanism of sandstone at high temperatures, Applied Sciences, 14(1), 444.