MA Jiaguo, WANG Jianli, ZHOU Jianke, LIU Lei. PREDICTION OF OIL AND GAS POTENTIAL USING FREQUENCY DIVISION RGB FUSION TECHNOLOGY[J]. Marine Geology Frontiers, 2020, 36(6): 64-68. DOI: 10.16028/j.1009-2722.2019.091
    Citation: MA Jiaguo, WANG Jianli, ZHOU Jianke, LIU Lei. PREDICTION OF OIL AND GAS POTENTIAL USING FREQUENCY DIVISION RGB FUSION TECHNOLOGY[J]. Marine Geology Frontiers, 2020, 36(6): 64-68. DOI: 10.16028/j.1009-2722.2019.091

    PREDICTION OF OIL AND GAS POTENTIAL USING FREQUENCY DIVISION RGB FUSION TECHNOLOGY

    • Frequency division RGB fusion method is very effective in describing the boundary of complex sand bodies and river courses. In the study, the authors found that this attribute can also indicate the potential oil and gas accumulation. One drilling well was done in the potential area using the attribute, and one oil reservoir was found with an additional proven reserve of 1 million tons. The success enhanced our confidence to make evaluation wells using this attribute. Starting with the analysis of wave equation and validation by forward modeling, this paper studies the amplitude intensity of different frequencies and demonstrates the feasibility of dividing frequency RGB fusion attributes to indicate oil and gas. Seismic reflection intensity is related to sand body thickness, porosity, oil and gas saturation and other factors, but there is no obvious change in physical parameters of sand body under the same sedimentary period. Therefore, it is considered that sand body thickness and fluid properties are the main factors affecting the amplitude. The forward model in this paper aims at sand body thickness and fluid properties. It concludes that the thickness of sand body is in the wavelength range of λ/8~λ/4, and the amplitude intensity is positively correlated with oil and gas saturation. The amplitude of oil and gas reservoirs in low frequency band is obviously enhanced, and the amplitude in high frequency band is obviously attenuated. Therefore, the difference can be amplified by using RGB frequency division fusion attributes, which can be used for fluid prediction.
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