通过引入岩石骨架参数表达式到Gassmann近似流体方程中,实现了基于该方程的地震孔隙度反演。但是岩石骨架参数表达式有很多种,使得地震孔隙度反演公式也各不相同,实际应用不方便,反演结果也难以评价。作者在研究现有常用岩石骨架参数表达式如Esheby-Walsh,Pride,Geertsma,Nur,Keys-Xu以及Krief等的基础上,提出了含有调节参数的岩石骨架模型统一表示式,发展了基于Gassmann方程的地震孔隙度反演方法。该方法应用范围宽,参数调节方便、灵活。为验证所提公式和方法的实用性,作者们结合ZJ地区钻井岩心的岩石物理样品测试数据和测井数据,进行了地震孔隙度反演。由于该地区油气储层的存在与中等程度孔隙度的分布有关,因此地震孔隙度反演对预测和识别油气层、干层和含水层有重要作用。反演结果与工区内孔隙度测井瞌线吻合度高,说明本文所提公式与方法是有效的,可靠的。
By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite different from each other, resuling in different seismic porosity inversion equations, potentially leading to difficulties in correctly applying them and evaluating their results. In response to this, a uniform relation with two adjusting parameters suitable for all rock skeleton models is established from an analysis and comparison of various conventional rock skeleton models and their expressions including the Eshelby-Walsh, Pride, Geertsma, Nur, Keys-Xu, and Krief models. By giving the two adjusting parameters specific values, different rock skeleton models with specific physical characteristics can be generated. This allows us to select the most appropriate rock skeleton model based on geological and geophysical conditions, and to develop more wise seismic porosity inversion. As an example of using this method for hydrocarbon prediction and fluid identification, we apply this improved porosity inversion, associated with rock physical data and well log data, to the ZJ basin. Research shows that the existence of an abundant hydrocarbon reservoir is dependent on a moderate porosity range, which means we can use the results of seismic porosity inversion to identify oil reservoirs and dry or water-saturated reservoirs. The seismic inversion results are closely correspond to well log porosity curves in the ZJ area, indicating that the uniform relations and inversion methods proposed in this paper are reliable and effective.