Resumo

JIANMING, Guo et al. Integrating Geochemical Anomaly and Remote Sensing Methods to Predict Oil- and Gas-Bearing Areas in the Yanchang Oil Field, Ordos Basin, China. Earth Sci. Res. J. [online]. 2019, vol.23, n.1, pp.79-86. ISSN 1794-6190.  https://doi.org/10.15446/esrj.v23n1.57544.

Oil seepage is one of the most important characteristics of hydrocarbon formation, and understanding oil seepage is crucial for oil-gas exploration and the assessment of petroleum resources. Remote sensing and geochemical methods have the same material and theoretical bases for extracting oil and gas information from underlying strata and the identification of media features. As an emerging exploration method, hyperspectral remote sensing is efficient compared with traditional geochemistry because it is a finer, and sometimes more directly quantitative method for determining the specific mineral anomaly content. Hence, the use of both methods together is important. This paper describes the analysis of hyperspectral remote sensing data and the extraction of abnormal index information, including the level of carbonate alteration and the content of acidolytical hydrocarbons, pyrolysis hydrocarbons, headspace gas, and ferric and ferrous ions. The two methods have mutual authentication, and they are complementary and are useful in oil-bearing areas. When these methods are integrated, the acidolytical hydrocarbon index is the most effective geochemical index and is better at characterizing the oil field distribution than other indices. Also, hydrocarbon geochemical anomalies occurring in oil fields generally show continuous distribution points and are consistent with oil reservoirs. Consequently, a 3D model was established to comprehensively utilize hyperspectral remote sensing and geochemical data to determine the distribution of petroleum reservoirs efficiently as well as to delineate oil- and gas-bearing prospects. There is great potential for determining oil- and gas-bearing fields through the integration of hyperspectral and geochemical data.

Palavras-chave : oil seepage; hyperspectral remote sensing; geochemistry; anomaly; hydrocarbon; prospecting area.

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