ارزیابی خصوصیات مخزنی سازند‌های کنگان و دالان‌ با استفاده از داده‌های پتروفیزیکی در یکی از میادین گازی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی نفت، دانشکده مهندسی شیمی، نفت و گاز، دانشگاه شیراز، شیراز، ایران

2 بخش علوم زمین، دانشکده علوم، دانشگاه شیراز، شیراز، ایران

3 گروه علوم زمین، دانشکده علوم طبیعی، دانشگاه تبریز، تبریز، ایران

4 گروه پتروفیزیک، شرکت بهره‌برداری نفت و گاز زاگرس جنوبی، شیراز، ایران

چکیده

جهت بررسی و شناسایی نواحی مختلف یک میدان از منابع و روش‌های متنوعی استفاده می‌شود که این روش­ها باعث شناخت بهتر نواحی و لایه­های مختلف مخزن می­شود. هدف این مطالعه تعیین رخساره‌های الکتریکی و واحد­های جریانی در سازندهای کنگان و دالان بالایی در یکی از میادین گازی جنوب ایران است. اهمیت تعیین و بررسی رخساره با کیفیت و تفکیک این رخساره از دیگر رخساره‌ها باعث شناخت بهتر نواحی با کیفیت و همچنین به دلیل استفاده از اطلاعات موجود و دردسترس می­توان در وقت و هزینه صرفه­جویی کرد. در این پژوهش با استفاده از داده­های نمودارگیری و مفهوم خوشه‌بندی از روش چند تفکیکی گرافیکی (MRGC) در نرم­افزار ژئولاگ، داده‌های نمودارهای پتروفیزیکی خوشه‌بندی و در نهایت 5 رخساره الکتریکی تشخیص داده شد. از میان رخساره‌های تعیین شده، رخساره الکتریکی شماره 4 با لیتولوژی آهک دولومیتی به‌عنوان بهترین رخساره مخزنی با توجه به تخلخل مؤثر بالا و حجم شیل پایین تشخیص داده شد. در این مطالعه پس از تعیین رخساره­های الکتریکی با استفاده از روش­های مختلف واحد­های جریانی از اطلاعات مغزه  به دست آمده­اند. در نهایت پس از تعیین واحد­های جریانی، برای بررسی ارتباط میان رخساره­های الکتریکی و واحدهای جریانی با استفاده از مقاطع عرضی چاه­ها، واحدهای جریانی در کنار رخساره­های الکتریکی تعیین شده قرار داده می­شوند. پس از بررسی عمق به عمق ارتباط بالایی بین آن­ها مشاهده شد. این نتیجه نشان می­دهد که از این مدل رخساره الکتریکی می­توان در تمام چاه­های این میدان استفاده کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Ali Dehghan Abnavi 1
  • Amir Karimian Torghabeh 2
  • Jafar Qajar 1
  • Rahim Kadkhodaii Ilkhchi 3
  • Ali Talebnejad 4
1 Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran
2 Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran
3 Department of Earth sciences, Faculty of Natural Science, Tabriz University, Tabriz, Iran
4 Department of Petrophysics, South Zagros Oil and Gas Production Company, Shiraz, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Cluster analysis
  • Electrofacies
  • Kangan and Dalan Formations
  • HFU
  • Well log data
-افضلی، ل.، 1396. ارزیابی پتروفیزیکی مخازن هیدروکربنی به روش قطعی و احتمالی، مرجع نرم افزار ژولاگ.
-ایزدی، م.، 1391. محاسبه تراوایی در چاه‌های فاقد مغزه با استفاده از مفهوم واحدهای جریانی هیدرولیکی اکتشاف و تولید 98، ص 65-68.
-باقری، ح.، 1392. استفاده از خوشه­سازی لاگ­ها به منظور زون­بندی مخزنی سازند فهلیان در یکی از میادین جنوب غرب ایران، پژوهش نفت، شماره 82، ص 45-59.
-خوشبخت، ف.، 1384. شناخت ویژگی­های شکستگی­ها و پارامترهای پتروفیزیکی مخازن نفتی با استفاده از لاگ­های تصویری، پایان­نامه کارشناسی­ارشد، دانشکده فنی دانشگاه تهران، ایران.
-رهسپار، ا.، 1393. تعیین رخساره‌های الکتریکی مخزنی با استفاده از روش­های خوشه­سازی MRGC, AHC, SOM   و DYNCLUST در بخش عرب در چاه میدان نفتی سلمان، پژوهش نفت، شماره 87، ص 107-125.
 
 
 
-Amaefule, J.O., Altunbay, M., Tiab, D., Kersey, D.G. and Keelan, D.K., 1993. Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. SPE annual technical conference and exhibition, Society of Petroleum Engineers.
-Ahrimankosh, M., Kasiri, N. and Mousavi, S., 2011. Improved permeability prediction of a heterogeneous carbonate reservoir using artificial neural networks based on the flow zone index approach. Petroleum Science and Technology, v. 29(23), p. 2494-2506.
-Baneshi, M., Behzadijo, M.R. and Soroushnia, M., 2016. Evaluation of the performance of ANN in predicting of electrofacies (estimated by SOM, AHC, and MRGC models). Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, v. 38.8, p. 1081-1088.
-Burki, M. and Darwish, M., 2017. Electrofacies vs. lithofacies sandstone reservoir characterization Campanian sequence, Arshad gas/oil field, Central Sirt Basin, Libya. Journal of African Earth Sciences, v. 130, p. 319-336.
-Correia, G.G. and Schiozer, D.J., Reservoir characterization using electrofacies analysis in the sandstone reservoir of the Norne Field (offshore Norway). Petroleum Geoscience, v. 22(2), p. 165-176.
-Cross, T.A. and Homewood, P.W., 1997. Amanz Gressly's role in founding modern stratigraphy Geological Society of America Bulletin, v. 109(12), p. 1617-1630.
-Enayati-Bidgoli, A.H., Rahimpour-Bonab, H. and Mehrabi, H., 2014. Flow Unit Characterisation in The Permian-Triassic Carbonate Reservoir Succession at South Pars Gasfield, Offshore Iran, Journal of Petroleum Geology, v. 37(3), p. 205-230.
-Ghadami, N., Rasaei, M.R., Hejri, S., Sajedian, A. and Afsari, K., 2015. Consistent porosity–permeability modeling, reservoir rock typing and hydraulic flow unitization in a giant carbonate reservoir. Journal of Petroleum Science and Engineering, p. 58-69.
-Jafarzadeh, N., Kadkhodaie, A., Jan Ahmad, B. and Karimi, M., 2019. Identification of electrical and petrophysical rock types based on core and well logs: utilizing the results to delineate prolific zones in deep water sandy packages from the Shah Deniz gas field in the South Caspian Sea Basin. Journal of Natural Gas Science and Engineering, DOI: 10.1016/j.jngse.2019.102923.‏
-Karimian Torghabeh, A., Rezaee, R., Moussavi-Harami, R., Pradhan, B., Kamali, M. and Kadkhodaie-Ilkhchi, A., 2014. Electrofacies in gas shale from well log data via cluster analysis: A case study of the Perth Basin, Western Australia Open Geosciences, v. 6(3), p. 393-402
-Khoshbakht, F. and Mohammadnia, M., 2010. Assessment of clustering methods for predicting permeability in a heterogeneous carbonate reservoir." 4th EAGE St. Petersburg International Conference and Exhibition on Geosciences-New Discoveries through Integration of Geosciences.
Pittman, E.D., 1992. Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone (1), AAPG bulletin, v. 76(2), p. 191-198.
-Serra, O.T. and Abbott, H.T., 1982. The contribution of logging data to sedimentology and stratigraphy, Society of Petroleum Engineers Journal, v. 22, p. 117-131.
-Shazly, T.F. and Ramadan, M., 2011. Well Logs Application in Determining the Impact of Mineral Types and Proportions on the Reservoir Performance of Bahariya Formation of Bassel-1x Well, Western Desert, Egypt. Journal of American Science, DOI: 10.13140/RG.2.2.11167.46244.