Safaei-Farouji Majid, Band Shahab S, Mosavi Amir
School of Geology, College of Science, University of Tehran 1417935840 Tehran, Iran.
Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 10 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC.
ACS Omega. 2022 Apr 2;7(14):11578-11586. doi: 10.1021/acsomega.1c05811. eCollection 2022 Apr 12.
Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel and not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indexes was employed on oil samples belonging to the Iranian part of the Persian Gulf oilfields. For the SOM network, at first, 10 default clusters were selected. Afterward, three effective clustering validity coefficients, namely, Calinski-Harabasz (CH), Silhouette (SH), and Davies-Bouldin (DB), were studied to find the optimum number of clusters. Accordingly, among 10 default clusters, the maximum CH (62) and SH (0.58) were acquired for 4 clusters. Similarly, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. According to the geochemical parameters, it can be deduced that the corresponding source rocks of four oil families have been deposited in a marine carbonate depositional environment under dysoxic-anoxic conditions. However, oil families show some differences based on geochemical data. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in oil family typing than those of common and overused methods of PCA and HCA.
确定石油盆地中石油族的数量，可为从勘探到开发的石油地球化学研究提供实用且有价值的信息。石油族分组有助于我们追踪运移路径、确定活跃烃源岩的数量，并考察油藏的连续性。迄今为止，几乎在所有的石油族类型划分研究中，都使用了主成分分析（PCA）和层次聚类分析（HCA）等常见统计方法。然而，尚无关于使用人工神经网络（ANN）来研究石油盆地中石油族的文献发表。因此，石油族类型划分需要新颖且未被过度使用的常用技术。本文首次报道了使用人工神经网络作为稳健计算方法进行石油族类型划分。为此，将与三个聚类有效性指标相关联的自组织映射（SOM）神经网络应用于波斯湾油田伊朗部分的油样。对于SOM网络，首先选择了10个默认聚类。随后，研究了三个有效的聚类有效性系数，即卡林斯基-哈拉巴斯（CH）、轮廓系数（SH）和戴维斯-布尔丁（DB），以确定最佳聚类数。据此，在10个默认聚类中，4个聚类获得了最大的CH值（62）和SH值（0.58）。同样，4个聚类的DB值最低（0.8）。因此，所有三个验证系数均表明4个聚类为最佳聚类数或石油族数。根据地球化学参数可以推断，四个石油族对应的烃源岩是在缺氧-厌氧条件下的海相碳酸盐沉积环境中形成的。然而，基于地球化学数据，石油族之间存在一些差异。本报告中确定的石油族数量与同一研究区域其他研究人员先前报道的结果一致。然而，本文所使用的技术（目前尚未得到应用），相比于PCA和HCA等常用且被过度使用的方法，在石油族类型划分的聚类目的方面，可以说是更为直接。
