Evaluation of reservoir properties in Kangan and Dalan Formation base on petrophysical data in one of Iranian gas fields

Extended abstract
Various sources and methods used to identify different zones of a field that can investigate different zones and layers of the reservoir. The purpose of this study is to determine electrofacies and HFUs in the Kangan and upper Dalan Formations in one of the southern Iranian gas fields.  The importance of determining and investigate the facies with the well quality and separation of this facies from other facies causes better knowledge of quality zones and also due to the use of available information can save time and money. In this study use well log, the concept of clustering and the MRGC method in Geolog software, the log petrophysical data were clustered and finally, 5 electrofacies were identified. Among these identified facies, electrofacies No. 4 with dolomite lithology was identified as the best reservoir zone due to high effective porosity and low shale volume. After determining electrofacies by using the aforementioned methods, HFUs were identified with core data. Finally, after determining the HFUs, to investigate the relationship between electrofacies and HFUs, the cross sections of wells and HFUs are placed next to the designated electrofacies. After examining depth to depth, a high correlation was observed between them. This result shows that, this electrofacies model can be used in all wells in this field.
Introduction
Investigating the geological units in a field, determining the slope of the classes, the areas with fracture and identifying faults and layering levels are crucial in discovering, managing and preserving hydrocarbon resources [1]. In order to evaluate and review the important parameters, in the early stages, data from various sources such as seismic, core, and petrophysical logs are used. The main advantages of well logging are solving the problems of costly coring, lack of core volume and information for various experiments.  Along with this information, it is possible to combine logs data, to determine facies. Since these facies are obtained from the logs data, the name of these facies are electrofacies [2]. By using the concept of electrofacies, data can be clustered. In fact, clustering is a structure within a collection of unlabeled data. The cluster is referred to a set of data that are similar to each other. In this technique, grouping samples are obtained such as those found in the same group (called a cluster) which are more similar (in one sense or another) to one another compared with those in other groups [3]. Data clustering methods include MRGC, SOM, AHC, and DYNCLUST models. The MRGC model is the best method for data clustering, due to its high resolution and high accuracy. Also there is no need for basic information from input data, stability of the result by changing the parameters, and it produces the optimal number of clusters.
MRGC Model
One of the methods for data clustering is MRGC method. The first step in this method is to determine the values of neighborhood indices for each point. Neighborhood values that are based on the neighborhood index are determined by using multi-dimensional pattern recognition. Finally, the point’s corner index is obtained and points are sorted according to this index. Then the number of clusters is given to the user. This type of model locates clusters using a multi-dimensional dot-pattern recognition method based on non-parametric k-nearest neighbors.
Material and Methods
Data clustering method, is a convenient method for classifying and verifying data. In this study, electrofacies are determined by using the concept of clustering. To determine the electrofacies, the model is first constructed in the base well. Selecting the base well is very important because in the end, the model is propagated to all wells. We have used well logging information of a field. It was found that gamma ray log, sonic log, density log and neutron log are available in all wells. Therefore, we used these logs to determine the electrofacies. After modeling by software, a total of 12,465 data were evaluated by selecting the mentioned logs. The software used MRGC model and proposed the number of clusters. After investigation, electrofacies that have the same properties should be merged together.
Results and Discussion
After merging electrofacies with the same properties, five electrofacies were selected.  Subsequently, the identified electrofacies were generalized to all zones of base well and other wells in the field.                                                                     
 
 
Fig. 1: Frequency input logs for facies model
 

Fig. 2: Assortment facies Based on input logs
 
After generalizing electrofacies to other wells, the accuracy of the used model was also proved. Using a combination of log data and a proper clustering method, provides valuable information about reservoir properties and different facies, which will give us a better view of the reservoir quality. To determine the petrophysical parameters of this field, lithology was determined using neutron / density diagram.
 
 
Fig. 3: crossplot of neutron / density
 
In order to study the statistical features and reservoir quality of the electrofacies, the box plot of these electrofacies can be used based on effective porosity, due to its high application in displaying the data and its easy interpretation. According to this plot, electrofacies number 4 with high effective porosity have the best reservoir quality.
 
 
Fig. 4: boxplot of PHE
 
 
 
Fig. 5: Correlation between transverse sections of wells
 
Conclusion
Identification of electrofacies is a valuable tool in reservoir quality and reservoir modeling.  Since in each well that is drilled in a field, logging must be taken in formations that have potential for hydrocarbon production, it can provide a relatively low cost comparing to other methods. This allows a better understanding of the reservoir. In this study, we have used log data to determine electrofacies in an Iranian gas field. By MRGC method, 5 electrofacies were identified. Each electrofacies represents specific lithology and different qualities of the reservoir. After comparing box plot of effective porosity, and interpreting petrophysical logs, electrofacies No. 4 was identified as yellow. It was selected as the best electrofacies with high production potential and quality.