| 631 - 638 |
Applying deep learning for identifying bioturbation from core photographs
Timmer E, Knudson C, Gingras M |
| 639 - 645 |
Performance tracking: A historical background to promote learning
Citron GP |
| 647 - 668 |
Source rocks in foreland basins: A preferential context for the development of natural hydraulic fractures
Zanella A, Cobbold PR, Rodrigues N, Loseth H, Jolivet M, Gouttefangeas F, Chew D |
| 669 - 694 |
Skempton's poroelastic relaxation: The mechanism that accounts for the distribution of pore pressure and exhumation-related fractures in black shale of the Appalachian Basine
Engelder T, Behr RA |
| 695 - 720 |
Consideration of the limitations of thermal maturity with respect to vitrinite reflectance, T-max, and other proxies
Katz BJ, Lin F |
| 721 - 748 |
Evaluation of Paleozoic source rocks in Kuwait
Abdullah F, Shaaban F, Al-Khamiss A, Khalaf F, Bahman F, Akbar B |
| 749 - 783 |
Evidence for deeply buried, oil-prone source rocks in the Baiyun depression, Pearl River Mouth Basin, northern South China Sea
Ping HW, Chen HH, Zhai PQ, Zhu JZ, Xiong WL, Kong LT, Gong S, Vergara TJ, George SC |
| 785 - 807 |
Application of principal component analysis on chemical data for reservoir correlation: A case study from Cretaceous carbonate sedimentary rocks, Saudi Arabia
Michael NA, Craigie NW |
| 809 - 843 |
Depositional environment and source rock quality of the Woodbine and Eagle Ford Groups, southern East Texas (Brazos) Basin: An integrated geochemical, sequence stratigraphic, and petrographic approach
Meyer MJ, Donovan AD, Pope MC |
