پیش‌بینی تراوایی سنگ مخزن با استفاده از روش‌های عدم قطعیت: سیستم فازی نوع دو

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران، ایران

2 گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات ، تهران، ایران

3 دانشکده برق و کامپیوتر، دانشگاه تهران، تهران، ایران

چکیده

تراوایی یا نفوذپذیری خاصیتی از سنگ مخزن است که به جریان سیال از سنگ مخزن می‌پردازد و از فاکتورهای مهم در تولید نفت و گاز از مخزن به حساب می‌آید. این پارامتر در شرایط آزمایشگاهی از طریق مغزه‌گیری به دست می‌آید که روشی پرهزینه و زمان‌بر است و همچنین برای همه چاه‌های موجود در یک میدان نفتی امکان‌پذیر نیست. امروزه این پارامتر را با استفاده از داده‌های لاگ پتروفیزیکی به روش‌های آماری و هوشمند محاسبه می‌کنند. در این مقاله از الگوریتم‌های هوشمند جهت پیش‌بینی تراوایی با استفاده از لاگ‌های پتروفیزیکی استفاده شده است. این پژوهش بر روی داده‌های چهار چاه کنگان و دالان واقع در میدان پارس جنوبی انجام شده است. از مجموع هشت ویژگی استخراج شده از هر چاه، با استفاده از روش انتخاب ویژگی مبتنی بر همبستگی، چهار ویژگی مؤثر در هر چاه انتخاب شدند. سپس از روش‌های رگرسیون، شبکه عصبی چندلایه، شبکه عصبی RBF(Radial Basis Function) ، مدل درخت خطی محلی (LOLIMOT: Local Linear Model Trees)، سیستم فازی نوع یک و سیستم فازی نوع دو برای پیش‌‌بینی تراوایی استفاده شد. نتایج نشان داد که با توجه به وجود عدم قطعیت در پارامترهای پتروفیزیکی و تراوایی، سیستم فازی نوع دو عدم قطعیت‌ها را بهتر پوشش می‌دهد. این روش در حالت پایه، تراوایی را با دقت 9481/0 و ریشه دوم میانگین مربعات خطای 3060/0 پیش‌بینی کرد. با استفاده از روش ترکیبی GSA-GA (Gravitational Search Algorithm - Genetic algorithm)، تعداد قواعد فازی و نیز با استفاده از روش خوشه‌بندی K-means، توابع عضویت فازی بهبود یافت و این بهبودها منجر به افزایش دقت پیش‌بینی تراوایی با ضریب تعیین 9768/0 و کاهش ریشه دوم میانگین مربعات خطا به مقدار 1602/0 شد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of permeability using uncertainty methods: type -2 fuzzy system

نویسندگان [English]

  • Hamid Hakiminezhad 1
  • Mitra Mirzarezaee 2
  • Babak Nadjar Araabi 3
1 Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
2 Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
3 Faculty of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
چکیده [English]

Permeability is a property of the reservoir rock, which deals with the flow of fluid from the reservoir and is an important factor in oil and gas production. This parameter is measured via coring and core laboratory analysis, which is an expensive and time-consuming process and also is not a feasible approach for every oil and gas field. Nowadays, the permeability can also be calculated using the data of petrophysical logs by means of statistical and intelligent techniques. The present study uses four wells drilled in Kangan and Dalan formations within South Pars gas field to predict permeability using fuzzy logic. Out of totally eight features extracted from each well, four more effective features were selected using correlation-based feature selection tools. Then, regression, multi-layer perceptron, RBF neural network, Local Linear Model Trees (LOLIMOT), type-1 and type -2 fuzzy systems were utilized for permeability prediction. The results indicated that due to the uncertainty in the petrophysical and permeability parameters, type-2 Fuzzy systems cover better the uncertainties. The aforementioned method predicts the best number of rules using the GSA-GA (Gravitational Search Algorithm - Genetic algorithm) combined algorithms. Fuzzy membership functions were also improved using the K-means clustering algorithms. These improvements led to increased accuracy of the predicted permeability with a coefficient of 0.9768, and a decrease in the root mean square error to 0.1602.

کلیدواژه‌ها [English]

  • predicting reservoir rock type
  • Permeability
  • type -2 fuzzy system
  • Gravitational Search Algorithm
  • Genetic Algorithm
Ahmadi, M. A., 2015, Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: application to reservoir simulation: Fuel, 140, 429-439.
Al-Anazi, A. F., and Gates, I. D., 2012, Support vector regression to predict porosity and permeability: effect of sample size: Computers and Geosciences, 39, 64-76.
Castillo, O., and Melin, P., 2012, A review on the design and optimization of interval type-2 fuzzy controllers: Applied Soft Computing, 12(4), 1267-1278.
Castillo, O., and Melin, P., 2014, A review on interval type-2 fuzzy logic applications in intelligent control: Information Sciences, 279, 615-631.
Cilimkovic, M., 2015, Neural networks and back propagation algorithm: Institute of Technology Blanchardstown, Blanchardstown Road North Dublin, 15.
Ding, F., 2013, Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling: Applied Mathematical Modelling, 37(4), 1694-1704.
Dubois, D., and Prade, H., 2012, Gradualness, uncertainty and bipolarity: Making sense of fuzzy sets: Fuzzy sets and Systems, 192, 3-24.
Fatehi, K., Bozorgi, A., Zahedi, M. S., and Asgarian, E., 2015, Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback: Journal of Computing and Security, 1(4).
Finol, J., Guo, Y. K., and Jing, X. D., 2001, A rule based fuzzy model for the prediction of petrophysical rock parameters: Journal of Petroleum Science and Engineering, 29(2), 97-113.
Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S., and Hanachi, J., 2014, Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs: Journal of Petroleum Science and Engineering, 122, 643-656.
Homaifar, A., and McCormick, E., 1995, Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms: IEEE transactions on fuzzy systems, 3(2), 129-139.
İnkaya, T., Kayalıgil, S., and Özdemirel, N. E., 2015, Ant Colony Optimization based clustering methodology: Applied Soft Computing, 28, 301-311.
Jamshidian, M., Hadian, M., Zadeh, M. M., Kazempoor, Z., Bazargan, P., and Salehi, H., 2015, Prediction of free flowing porosity and permeability based on conventional well logging data using artificial neural networks optimized by Imperialist competitive algorithm–A case study in the South Pars gas field: Journal of Natural Gas Science and Engineering, 24, 89-98.
Kadkhodaie Ilkhchi, A., Rezaee, M. R., and Moallemi, S.A., 2006, A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field: Journal of Geophysics and Engineering, 3(4), 356-369, doi:10.1088/1742-2132/3/4/007.
Li, H., Yin, S., Pan, Y., and Lam, H. K., 2015, Model reduction for interval type-2 Takagi–Sugeno fuzzy systems: Automatica, 61, 308-314.
Lowe, D., 1989, Adaptive radial basis function nonlinearities, and the problem of generalisation: In Artificial Neural Networks, First IEE International Conference, Conf. Publ. No. 313, pp. 171-175, IET.
Lowe, D., 2015, Radial basis function networks-revisited: Mathematics Today, 51(3), 124-126.
Mendel, J. M., and Liu, X., 2013, Simplified interval type-2 fuzzy logic systems: IEEE transactions on fuzzy systems, 21(6), 1056-1069.
Nelles, O., 1998, Nonlinear system identification with local linear neuro-fuzzy models: Shaker.
Nelles, O., 2013, Nonlinear system identification: from classical approaches to neural networks and fuzzy models: Springer Science and Business Media.
Olatunji, S. O., Selamat, A., and Abdulraheem, A., 2014, A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction: Information fusion, 16, 29-45.
Olatunji, S. O., Selamat, A., and Azeez, A. R. A., 2015, Modeling permeability and PVT properties of oil and gas reservoir using hybrid model based on type-2 fuzzy logic systems: Neurocomputing, 157, 125-142.
Piret, C., and Hanert, E., 2013, A radial basis functions method for fractional diffusion equations: Journal of Computational Physics, 238, 71-81.
Rashedi, E., Nezamabadi-Pour, H., and Saryazdi, S., 2009, GSA: a gravitational search algorithm: Information sciences, 179(13), 2232-2248.
Rashid, F., Glover, P. W. J., Lorinczi, P., Hussein, D., Collier, R., and Lawrence, J., 2015, Permeability prediction in tight carbonate rocks using capillary pressure measurements: Marine and Petroleum Geology, 68, 536-550.
Rezaee, M. R., Jafari, A., and Kazemzadeh, E., 2006, Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks: Journal of Geophysics and Engineering, 3(4), 370.
Roy, A., Govil, S., and Miranda, R., 1997, A neural-network learning theory and a polynomial time RBF algorithm: IEEE Transactions on Neural Networks, 8(6), 1301-1313.
Saffarzadeh, S., and Shadizadeh, S. R., 2012, Reservoir rock permeability prediction using support vector regression in an Iranian oil field: Journal of Geophysics and Engineering, 9(3), 336.
Sibi, P., Jones, S. A., and Siddarth, P., 2013, Analysis of different activation functions using back propagation neural networks: Journal of Theoretical and Applied Information Technology, 47(3), 1264-1268.
Soleimani-B, H., Lucas, C., and Araabi, B. N., 2012, Fast evolving neuro-fuzzy model and its application in online classification and time series prediction: Pattern Analysis and Applications, 15(3), 279-288.
Sun, G., Zhang, A., Yao, Y., and Wang, Z., 2016, A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding: Applied Soft Computing, 46, 703-730.
Younus, Z. S., Mohamad, D., Saba, T., Alkawaz, M. H., Rehman, A., Al-Rodhaan, M., and Al-Dhelaan, A., 2015, Content-based image retrieval using PSO and k-means clustering algorithm: Arabian Journal of Geosciences, 8(8), 6211-6224.