Rock physics has proven its credibility for the quantitative seismic interpretation of reservoir and reservoir characterization. In this study, we implemented K-means clustering to group data points more objectively and without prior misconceptions into clusters. Furthermore, we used the grouping suggested by the algorithm for our study. We integrated rock physics and K-means cluster analysis to determine a possible hydrocarbon reservoir using three well logs data. Initially, K-means clustering was implemented on density logs and based on the arithmetic mean of each density log cluster, sandstone and shale-dominant parts were identified and separated. Then, rock physics parameters were computed for the sandstone-dominant part of the well logs. Based on the cross-plots of Lame’s constants with density product, and Lame’s constants ratio, additional lithology discrimination was done for the identification of a clean gas-sand zone. In the clean gas-sand zone, two different pore fluids zones were identified based on V_p/V_s ratio, Poisson ratio, P- and S-wave impedances. Finally, statistical analysis was carried out to observe the underlying frequency distribution of rock physics parameters. The integration of cluster analysis and rock physics gave us significant information about the presence of different fluids and the existence of a potential hydrocarbon reservoir.

岩石物理学已经证明了其在定量地震解释储层和表征储层方面的信誉。在这项研究中，我们实施了K- means聚类以更客观地对数据点进行分组，而无需事先将其误解为群集。此外，我们将算法建议的分组用于我们的研究。我们结合了岩石物理学和K-均值聚类分析，使用三个测井数据确定了可能的油气藏。最初，K在密度测井上进行均值聚类，并根据每个密度测井聚类的算术平均值，识别并分离出砂岩和页岩优势部分。然后，针对测井仪中以砂岩为主的部分计算岩石物理参数。基于Lame常数与密度乘积的交叉图以及Lame常数比，还进行了岩性判别，以识别干净的气砂带。在洁净的气砂区，根据V _p / V _s比，泊松比，P-和S确定了两个不同的孔隙流体区。波阻抗。最后，进行统计分析以观察岩石物理参数的潜在频率分布。聚类分析和岩石物理学的集成为我们提供了有关不同流体的存在以及潜在油气藏的重要信息。