Hydrocarbon microseepage information extracting and oil-gas prospective area prediction based on landsat-8 remote sensing images

Chaoyang Bian; Zhibin Li; Qiao Zhang

doi:10.1117/12.3010371

15 November 2023 Hydrocarbon microseepage information extracting and oil-gas prospective area prediction based on landsat-8 remote sensing images

Chaoyang Bian, Zhibin Li, Qiao Zhang

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128151K (2023) https://doi.org/10.1117/12.3010371
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

The phenomenon of hydrocarbon micro leakage in oil and gas reservoirs is common, and at least 85% of oil and gas fields in the world currently have hydrocarbon micro leakage. Remote sensing technology can quickly, efficiently and economically detect and extract surface mineral anomaly information caused by hydrocarbon leakage, so as to provide favorable exploration targets in the initial stage of oil and gas exploration, which has important social and economic significance for reducing exploration risks and accelerating exploration process. Based on the hydrocarbon microleakage theory, this study extracted clay mineralization, carbonation and red layer fading and alteration information in the study area using Landsat8 OLI image data, and used GIS tools for overlay analysis. Combined with the comprehensive analysis of geological structure and other factors, the oil and gas prospect areas in northeast Sichuan are predicted, designate 5 oil and gas prediction areas.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chaoyang Bian, Zhibin Li, and Qiao Zhang "Hydrocarbon microseepage information extracting and oil-gas prospective area prediction based on landsat-8 remote sensing images", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128151K (15 November 2023); https://doi.org/10.1117/12.3010371

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KEYWORDS

Minerals

Remote sensing

Carbonates

Landsat

Principal component analysis

Image processing

Reflectivity

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