Prediction of Total Organic Carbon Content in Deep Marine Shale Reservoirs Based on a Super Hybrid Machine Learning Model-Reference-Cited by-同舟云学术

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Prediction of Total Organic Carbon Content in Deep Marine Shale Reservoirs Based on a Super Hybrid Machine Learning Model

Published:2024-08-29 Issue: Volume: Page:
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Liu Yi¹^ORCID,Li Na¹^ORCID,Li Chengyong¹,Jiang Jiayu²,Wu Xiuhui³,Liang Haipeng¹,Zhang Dongxu¹,Hu Xiuquan¹

Affiliation:

1. Energy College, Chengdu University of Technology, Chengdu 610059, China

2. Shale Gas Research Institute, PetroChina Southwest Oil & Gas Field Company, Chengdu, 610051, China

3. Northeast Sichuan Gas Field, PetroChina Southwest Oil & Gas Field Company, Chengdu, 610051, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.4c02135

Reference48 articles.

1. Pore Structure Heterogeneity of Deep Marine Shale in Complex Tectonic Zones and Its Implications for Shale Gas Exploration

2. Effects of pore connectivity and water saturation on matrix permeability of deep gas shale

3. Quantitative Studies on the Characterization and Evaluation of Adsorbed Gas and Free Gas in Deep Shale Reservoirs

4. Shale gas in China: Characteristics, challenges and prospects (II)

5. Organic and Inorganic Geochemistry of Upper Paleozoic Marine-to-Continental Transitional Shales and the Implications for Organic Matter Enrichment, Ordos Basin, China

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