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Keywords: young
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Proceedings Papers
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4027422-MS
... calculation application journal bounded scenario shear module mudstone equation fracture anisotropy young solution URTeC: 4027422 A Semi-analytical Poroelastic Solution of Induced Pressure-Stress Changes and Its Applications to Depletion Delineation of Transversely Isotropic Shale Reservoirs...
Proceedings Papers
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4038060-MS
... stress on the dynamic Young s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure...
Proceedings Papers
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3861336-MS
... reservoir geomechanics geochemical characterization formation toc clastic rock modulus saturation evaluation optimize stimulation design oil shale shale gas log analysis geological subdiscipline young rabe gamma ray brittleness utilizing geochemical petrophysical information...
Proceedings Papers
Roberto Suarez-Rivera, Evan Kias, John Degenhardt, Angelica Rios Alvarez, Rabah Mesdour, Shannon Eichmann, Anuj Gupta
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864914-MS
... Abstract The mechanical properties of unconventional reservoir rocks typically follow linear and elastic trends where the Young’s modulus, Poisson’s ratio, elastic anisotropy, and compressive strength are constant when subjected to varying confining stress. The measured properties...
Proceedings Papers
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3866084-MS
... upstream oil & gas geologist deep learning information geological subdiscipline neural network reservoir geomechanics young united states government artificial intelligence machine learning geology textural intensity 0 machine learning facilitate prediction grayscale intensity...