The identification of potassium (K) and magnesium (Mg) salts prior to the well drilling is a key factor to avoid washouts, closing pipes, fluid loss damage, and borehole collapse. The Bayesian classification combines the outcomes from statistical rock physics and seismic inversion, providing the spatial occurrence of the most-probable salt types. It serves as a facies identifier of Mg–K-rich salts (bittern salts) before drilling. Nevertheless, the most-probable classification is limited to the seismic resolution which may underestimate seismically thin-layer thicknesses. Along with the most-probable facies, the Bayesian classification renders the facies probability volume. We demonstrate that the facies probability and facies-specific total thickness highly correlate to each other even under the threshold of seismic resolution. Thus, we employ the bittern-salts probability volume to predict thin-bed bittern-salts thickness in undrilled locations. To capture the variability of the seismic estimation, we resort to Monte Carlo-assisted simulations of wells that emulate the layering patterns of a site-specific deposition environment. These simulations are crucial to assist the estimation of the joint probability density function between the facies volume and the total thickness. Therefore, given the facies probability, the joint probability density function enables us to derive the conditional expectation and percentiles of thin-bed thicknesses. Furthermore, this paper proposes a method to quantify the negative influence of seismic noise in the estimation of thin-bed thicknesses. The blind well confirms the consistency of this technique to unfold the uncertainty in the seismic predictability of thin layers. We argue that this procedure is extendable to other facies.

在钻井之前识别钾 (K) 和镁 (Mg) 盐是避免冲刷、关闭管道、流体损失损坏和钻孔塌陷的关键因素。贝叶斯分类结合了统计岩石物理学和地震反演的结果，提供了最可能的盐类型的空间出现。它可作为钻井前富镁钾盐（卤盐）的相标识符。然而，最可能的分类仅限于可能低估地震薄层厚度的地震分辨率。与最可能的相一起，贝叶斯分类呈现相概率体积。我们证明即使在地震分辨率阈值下，相概率和特定相总厚度也高度相关。因此，我们使用卤盐概率体积来预测未钻孔位置的薄层卤盐厚度。为了捕捉地震估计的可变性，我们采用蒙特卡罗辅助模拟油井模拟特定地点沉积环境的分层模式。这些模拟对于帮助估计相体积和总厚度之间的联合概率密度函数至关重要。因此，给定相概率，联合概率密度函数使我们能够推导出薄层厚度的条件期望和百分位数。此外，本文提出了一种量化地震噪声对薄层厚度估计的负面影响的方法。盲井证实了该技术的一致性，以揭示薄层地震可预测性的不确定性。我们认为这个程序可以扩展到其他相。