The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in carbonate reservoirs. Nowadays, quantitative interpretation (QI) is an essential part of hydrocarbon exploration in a complex reservoir, which needs adequate rock physics data at the well level. However, sometimes the relevant data are not available in earlier discovered oil and gas fields. We observed that the old oil and gas fields in the onshore parts of India have a scarcity of density and compressional velocity (V_p) data at the well level. Gardner's empirical expression provides the scope to estimate V_p from acquired density data and vice versa. However, there are two constants in this relationship, and these are different for different saturation cases of the reservoir due to different mineralogical content in the reservoir rock. The current study aims to identify suitable rock mineral mixing methods and their related uncertainty for estimating Gardner's constants. This uncertainty leads to the estimation of the degree of unwanted flexibility for V_p measurement. Improper selection of the rock mineral mixing method generates uncertainties during the fluid substitution model, mainly where available data are limited. A machine learning (ML) approach based on the naïve Bayes algorithm was adopted in this study to select the appropriate rock mineral mixing method from a limited data set. The study was performed in a carbonate reservoir in an onshore sedimentary basin of western India. The ML study shows that the Reuss rock mineral mixing method is suitable for the computation of Gardner's constant in different saturation models for this carbonate reservoir, with less uncertainty.

岩石物性的频繁变化使碳酸盐岩储层的油气勘探具有挑战性。如今，定量解释（QI）是复杂油藏油气勘探的重要组成部分，需要井级足够的岩石物理数据。但是，有时在较早发现的油气田中无法获得相关数据。我们观察到印度陆上部分的老油田和气田在井水平上缺乏密度和压缩速度（V _p）数据。Gardner的经验表达式为估算V _p提供了范围从采集的密度数据中得出，反之亦然。但是，这种关系中存在两个常数，并且由于储层岩石中的矿物含量不同，对于储层的不同饱和度情况，它们是不同的。当前的研究旨在确定合适的岩石矿物混合方法及其相关的不确定性，以估计Gardner常数。这种不确定性导致对V _p的不希望的柔韧性程度的估计。测量。岩石矿物混合方法选择不当会在流体替代模型期间产生不确定性，主要是在可用数据有限的情况下。本研究采用基于朴素贝叶斯算法的机器学习（ML）方法，从有限的数据集中选择合适的岩石矿物混合方法。该研究是在印度西部陆上沉积盆地的碳酸盐岩储层中进行的。ML研究表明，该碳酸盐岩储层在不同饱和度模型中，Reuss岩石矿物混合方法适合于计算Gardner常数。