利用分频RGB融合技术预测储层含油气性的可行性及应用效果

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

    • 摘要: 地震分频技术广泛应用于厚储层研究,砂体边界刻画等领域。分频RGB融合切片技术是一种比较成熟,应用较广的技术。笔者在应用该技术做储层研究过程中,发现该属性对储层含油气具有较好的指示作用,并在开发实践中,对属性显示油气异常区布置调整井,取得较好的油气发现,增强该属性应用于油气检测的信心。地震资料的反射信号与储层含油气性间有诸多未解决的问题,不同学者提出过诸多理论致力于地震资料的直接烃检工作。笔者从波动方程推导出含油气储层高频能量衰减加快,因此可以利用分频振幅能量的差异性来放大油气异常信息。设计正演模型论证储层充填油、气、水时振幅能量差异性以及不同频率下不同流体储层的振幅能量差异,证实含油、气、水层在不同频率下能量特征存在差异,砂体厚度在λ/8~λ/4波长区间段,含油气储层的低频增强,高频衰减特征较为明显。基于此结论,利用低、中、高频体进行RGB融合,结合实钻井的油气信息可以预测含油气潜力区。利用该思路在实际油田生产中,设计1口评价井取得100万m3储量的突破。

       

      Abstract: 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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