Oil Reservoirs Classification Using Fuzzy Clustering

Enhanced Oil Recovery (EOR) is a well-known method to increase oil production from oil reservoirs. Applying EOR to a new reservoir is a costly and time consuming process. Incorporating available knowledge of oil reservoirs in the EOR process eliminates these costs and saves operational time and work. This work presents a universal method to apply EOR to reservoirs based on the available data by clustering the data into compact and well-separated groups. A label is then assigned to each cluster which is in fact class of the data points belonging to that cluster. When EOR is intended to be applied to a new reservoir, class of the reservoir is determined and then EOR method used for the reservoirs of that class is applied to this one with no further field studies and operations. In contrast to classification, clustering is unsupervised and number of clusters within the data is not known a priori. Some well-known methods for determining number of clusters are tried but they failed. A novel method is presented in this work for number of clusters based on difference of membership grades of the data points in the clusters. It is applied to both synthetic and real life data including reservoirs data and it is shown that this method finds number of clusters accurately. It is also shown the raw data could be easily represented as fuzzy rule-base for better understanding and interpretation of the data.