Abstract

Petrophysical seismic inversion, aided by rock physics, aims at estimating reservoir properties based on reflection events, but it is generally based on the Gassmann equation, which precludes its applicability to complex reservoirs. To overcome this problem, we present a methodology based on the double-porosity Biot–Rayleigh (BR) model, which takes into account the rock heterogeneities. The volume ratio of inclusions in the BR model is treated as a spatially varying parameter, facilitating a better description of the pore microstructure. The method includes the Zoeppritz equations to extract reservoir properties from prestack data. To handle the ill-posedness of the inversion and achieve a stable solution, the algorithm is formulated as a multi-objective optimization based on the Bayes theorem, where the reservoir-property estimation is jointly conditioned to seismic and elastic data with multiple prior terms. The method is validated with field data of a tight gas sandstone reservoir, illustrating its effectiveness compared to the Gassmann-based estimation, reducing uncertainties and improving the accuracy of identifying gas zones.

Article Highlights

• The petrophysical seismic inversion is based on the double-porosity Biot–Rayleigh model

• Spatially varying inclusion volumes are used to describe complex pore structures

• A multi-objective optimization with joint data misfit enables stable results

中文翻译：

---

基于双孔隙度Biot-Rayleigh模型的多目标岩石物理地震反演

摘要

在岩石物理学的帮助下，岩石物理地震反演旨在基于反射事件估计储层性质，但它通常基于 Gassmann 方程，这排除了其对复杂储层的适用性。为了克服这个问题，我们提出了一种基于双孔隙度 Biot-Rayleigh (BR) 模型的方法，该模型考虑了岩石的非均质性。BR 模型中夹杂物的体积比被视为空间变化的参数，有助于更好地描述孔隙微观结构。该方法包括从叠前数据中提取储层特性的 Zoeppritz 方程。为了处理反演的不适定性并获得稳定的解，该算法被制定为基于贝叶斯定理的多目标优化，其中储层物性估计联合地以具有多个先验项的地震和弹性数据为条件。该方法通过致密气砂岩储层的现场数据进行了验证，与基于Gassmann的估计相比，该方法的有效性得到了验证，减少了不确定性，提高了气层识别的准确性。

文章重点

• 岩石物理地震反演基于双孔隙度 Biot-Rayleigh 模型

• 空间变化的包裹体体积用于描述复杂的孔隙结构

• 具有联合数据失配的多目标优化可实现稳定的结果