Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

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, 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 indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, 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. 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 the oil family typing than those of common and overused methods of PCA and HCA.

Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

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, 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 indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, 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. 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 the oil family typing than those of common and overused methods of PCA and HCA.

A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.