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为了适当地完成储层表征的过程,一个有效的方法就是把现场所有可以利用的信息融合成一个一致性的模型。在实际生产中实现这种融合并非简单的任务,所以有必要运用如地震反演等特殊方法。应用地震反演可以使测井数据和地震数据的有效结合成为可能,并且可以得到一个模型,该模型在预测过程中可通过流体数字模拟来验证。地震反演可以通过多种方法进行,主要分为两大类:一类是确定性方法(其代表是回归反演和约束稀疏脉冲反演),另一类是随机方法(其代表是地质统计学反演)。在本次研究中,通过随机反演结果和确定性反演结果的对比展示了随机反演是如何改进储层表征过程的。事实上,随机反演,可以运用较高的采样率(和储层模型的网格大小相接近),来产生一个更可靠的模型。随机反演的另一个好处就是随机方法可产生一些基本的统计测量值来改进解释精度,并且在储层表征过程中能生成大量的实现,从而使储层模型的不确定性研究成为可能。
In order to properly complete the process of reservoir characterization, an effective method is to fuse all available information in the field into a consistent model. In the actual production to achieve this integration is not a simple task, it is necessary to use special methods such as seismic inversion. Applying seismic inversion makes it possible to combine log and seismic data efficiently and to obtain a model that can be validated by fluid digital modeling during the forecasting process. Seismic inversion can be performed by a variety of methods and can be divided into two main categories: one is a deterministic method (its representation is regression inversion and constrained sparse pulse inversion), and the other is a stochastic method (its representation is geostatistical Learn inversion). In this study, we show how stochastic inversion can improve reservoir characterization through comparison of stochastic and deterministic inversion results. In fact, stochastic inversion can produce a more reliable model with higher sampling rates (close to the grid size of the reservoir model). Another benefit of stochastic inversion is that stochastic methods produce some basic statistical measures to improve interpretation accuracy and can generate a large number of implementations in reservoir characterization, making it possible to study reservoir model uncertainties.