Open Access
| Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 4 | |
| Section | Multidimensional Research and Practice on Water Resources and Water Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202452001022 | |
| Published online | 03 May 2024 | |
- Herro N. S. L. 1986. Derivation of a Total Organic Carbon Log for Source Rock Evaluation [A]. SPWLA 27th Annual logging Symposium, Paper H H. [Google Scholar]
- Mendelson J. D. and Toksoz M. N. 1985. Source rock characterization using multivariate analysis of log data [A]. SPWLA 26th Annual Logging Symposium, Paper UU. [Google Scholar]
- Mallick R. K. and Raju S. V. 1995. Application of Wireline Logs in Characterization and Evaluation of Generation Potential of Palaeocene Lower Eocene Source Rocks in Parts of Upper Assam Basin, India [J]. The Log Analyst, 36(3): 49–63 [Google Scholar]
- Meyer B. L., Nederlof M. H. 1984. Identification of source rocks on wireline logs by density/resistivity and sonic transit time/resistivity crossplots 1[J]. AAPG Bulletin, 68(2):121–129 [Google Scholar]
- Passey Q. R., Moretti F. J., Kulla J. B., et al. 1990. Practical model for organic richness from porosity and resistivity logs[J]. AAPG Bulletin, 74(12):1777–1794 [Google Scholar]
- Zhu G. Y., Jin Q., Zhang L. Y. 2003. Using log information to analyze the geochemical characteristics of source rocks in Jiyang depression[J]. Well Logging Technology, 2003, 27(2): 104–109 [Google Scholar]
- Guo L., Chen J. F., Miao Z. Y. 2009. Study and application of a new overlay method of the TOC content[J]. Natural Gas Geoscience, 2009, 20(6): 951–956 [Google Scholar]
- Mahmoud A. A. A., Elkatatny S., Mahmoud M., et al. 2017. Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network[J]. International Journal of Coal Geology, 179: 72–80. [CrossRef] [Google Scholar]
- Bolandi V., Kadkhodaie A., Farzi R. 2017. Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: a case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran[J]. Journal of Petroleum Science & Engineering, 151: 224–234. [CrossRef] [Google Scholar]
- Amosu A., Imsalem M., Sun Y. 2021. Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale[J]. Journal of Applied Geophysics, 188, 10431. [Google Scholar]
- Saporetti C., Fonseca D., Oliveira L., et al. 2022. Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields[J]. Marine and Petroleum Geology, 143, 105783. [CrossRef] [Google Scholar]
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