پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 709 |
| تعداد مقالات | 10,213 |
| تعداد مشاهده مقاله | 71,680,424 |
| تعداد دریافت فایل اصل مقاله | 63,496,294 |
ارزیابی و مقایسه تأثیر افزودن پوسته برنج پودر شده و پودر نشده به گل حفاری پایه آبی بر خواص رئولوژیکی گل به همراه یک مدل شبکه عصبی مصنوعی | ||
| مدل سازی در مهندسی | ||
| دوره 24، شماره 84، فروردین 1405، صفحه 169-184 اصل مقاله (1.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2025.36647.2795 | ||
| نویسندگان | ||
| کیوان بختیاری منش1؛ مجتبی رحیمی2؛ علی مختاریان* 3 | ||
| 1گروه مهندسی نفت، واحد خمینیشهر، دانشگاه آزاد اسلامی، خمینیشهر، ایران | ||
| 2مرکز تحقیقات سنگ، واحد خمینی شهر، دانشگاه آزاد اسلامی، ایران | ||
| 3گروه مهندسی مکانیک، واحد خمینیشهر، دانشگاه آزاد اسلامی، خمینیشهر، ایران | ||
| تاریخ دریافت: 01 بهمن 1403، تاریخ بازنگری: 29 اردیبهشت 1404، تاریخ پذیرش: 25 خرداد 1404 | ||
| چکیده | ||
| خواص رئولوژیکی سیالات حفاری، پارامترهایی اساسی در بهینهسازی عملیات حفاری و کاهش هزینه کل حفاری هستند. در این تحقیق، در مرحله اول به بررسی و مقایسه تأثیر افزودن هر یک از پلیمرهای گیاهی پوسته برنج پودر شده و پوسته برنج پودر نشده بر میزان تنش برشی گل حفاری پایه آبی (مخلوط آب و بنتونایت) در نرخهای برش متفاوت پرداخته شده است. پس از تعیین ویسکوزیته پلاستیک و نقطه تسلیم نمونهها بر مبنای مدل بینگهام، روند یکنواختی در تغییرات خواص رئولوژیکی گل حفاری با افزایش جرم هر یک از افزودنیها به سیال پایه مشاهده نشد. در گام بعدی تحقیق، یک مدل مبتنی بر شبکه عصبی مصنوعی پیشخور دو لایه جهت پیشبینی تنش برشی گلهای حفاری مورد مطالعه برای ورودیهای مقدار دلخواه جرم پلیمر افزودنی و نرخ برش دلخواه نمونه گل طراحی شد و برای هر دسته از دادههای متناظر با هر یک از افزودنیها تحت آموزش قرار گرفت که نتایج گویای تخمینی دقیق و مطلوب هستند. بطوریکه میزان درصد خطای نسبی میانگین و بیشینه بدست آمده برای مقادیر خروجی متناظر با دادههای آزمون شبکهها در مقایسه با نتایج اعمال مدل پرکاربرد هرشل- بالکلی کمتر میباشد. همچنین در پی انجام آنالیز حساسیت شبکهها، اهمیت و درجه تأثیر بالاتر نرخ برش بر تغییرات تنش برشی سیال در مقایسه با جرم افزودنی مشاهده شد. | ||
| کلیدواژهها | ||
| پوسته برنج پودر شده؛ پوسته برنج پودر نشده؛ خواص رئولوژیکی؛ نرخ برش؛ شبکه عصبی مصنوعی پیشخور دو لایه | ||
| عنوان مقاله [English] | ||
| Evaluation and Comparison of the Effect of Adding Pulverized and Unpulverized Rice Husk into Water-Based Drilling Mud on Rheological Properties Along with Presentation of an Artificial Neural Network Model | ||
| نویسندگان [English] | ||
| Keivan Bakhtiyari Manesh1؛ Mojtaba Rahimi2؛ Ali Mokhtarian3 | ||
| 1Department of Petroleum Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran | ||
| 2Department of Mechanical Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran | ||
| 3Stone Research Center, Kho.C., Islamic Azad University, Khomeinishahr, Iran | ||
| چکیده [English] | ||
| The rheological properties of drilling fluids are essential parameters in optimizing drilling operations and reducing the total cost of drilling. In this research, in the first stage, the effect of adding herbal polymers of pulverized and unpulverized rice husk on the amount of shear stress of water-based drilling mud (a mixture of water and bentonite) at different shear rates has been investigated and compared. After determining the plastic viscosity (PV) and yield point (YP) of the samples based on the Bingham model, no uniform trend was observed in the changes in the rheological properties of the drilling mud with the increase in the mass of each additive to the base fluid. In the next step of the research, a model based on a two-layer feedforward artificial neural network is designed to predict the shear stress of the studied drilling muds for the input of the arbitrary mass of the additive polymer and arbitrary shear rate of the mud sample, and the network was trained for each set of data corresponding to each of the additives, which resulted in accurate and favorable estimation results. The percentage of average and maximum error obtained for the output values corresponding to the network test data is smaller compared to the results of applying the widely used Herschel-Bulkley model. Moreover, we found through sensitivity analysis that the importance and degree of influence of the shear rate on changes in shear stress are higher compared to the additive mass. | ||
| کلیدواژهها [English] | ||
| Pulverized rice husk, Unpulverized rice husk, Rheological properties, Shear rate, Two-layered feedforward artificial neural network | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
[1] Rabia, H. Oilwell Drilling Engineering: Principles & Practice. London: Graham and Trotman, 1985. [2] Bourgoyne, A. T., M. E. Cheever, K. K. Mulheim, and F. S. Young. Applied Drilling Engineering. SPE Textbook Series, Vol. 2. Richardson, TX: Society of Petroleum Engineers, 1986. [3] Ahmad, Hafiz Mudaser, Muhammad Shahzad Kamal, and Mamdouh A. Al-Harthi. “Rheological and Filtration Properties of Clay-Polymer Systems: Impact of Polymer Structure.” Applied Clay Science 160 (2018): 226–237. [4] Sadeghalvaad, M., and S. Sabbaghi. “The Effect of the TiO₂/Polyacrylamide Nanocomposite on Water-Based Drilling Fluid Properties.” Powder Technology 272 (2015): 113–119. [5] Zhang, X., G. Jiang, Y. Xuan, L. Wang, and X. Huang. “Associating Copolymer Acrylamide/Diallyldimethylammonium Chloride/Butyl Acrylate/2-Acrylamido-2-Methylpropanesulfonic Acid as a Tackifier in Clay-Free and Water-Based Drilling Fluids.” Energy & Fuels 31, no. 5 (2017): 4655–4662. [6] Luo, Zhihua., Jingjing Pei, Longxiang Wang, Pei-Zhi Yu, and Zhangxin Chen. “Influence of an Ionic Liquid on Rheological and Filtration Properties of Water-Based Drilling Fluids at High Temperatures.” Applied Clay Science 136 (2017): 96–102. [7] Orodu, Oyinkepreye D., Kale B. Orodu, Richard O. Afolabi, and Eboh A. Dafe. “Rheology of Gum Arabic Polymer and Gum Arabic Coated Nanoparticle for Enhanced Recovery of Nigerian Medium Crude Oil under Varying Temperatures.” Data in Brief 19 (2018): 1773–1778. [8] Adams, Neal., and Tommie Charrier. Drilling Engineering: A Complete Well Planning Approach. Tulsa, OK: Penn Well Publishing Company, 1985. [9] Kersten, Glenn. “Results and Use of Oil-Base Fluids in Drilling and Completing Wells.” In Drilling and Production Practice. OnePetro, 1946. [10] Luo, Yuejin, P. A. Bern, B. D. Chambers, and D. S. Kellingray. “Simple Charts to Determine Hole Cleaning Requirements in Deviated Wells.” In SPE/IADC Drilling Conference and Exhibition, Paper SPE-27486. Richardson, TX: SPE, 1994. [11] Zhang, Li-Ming, Tan Yanbing, and Z. M. Li. “Application of a New Family of Amphoteric Cellulose-Based Graft Copolymers as Drilling-Mud Additives.” Colloid and Polymer Science 277 (1999): 1001–1004. [12] Raipuria, Vivek, Nisha Rani, V. P. Sharma, and Tarun Kumar Naiya. “Use of Nanoparticle Derived from Natural Source and Its Application in Drilling Fluid.” International Journal of Oil, Gas and Coal Technology 19, no. 3 (2018): 283–295. [13] Zarei, Vahid, and A. Nasiri. “Stabilizing Asmari Formation Interlayer Shales Using Water-Based Mud Containing Biogenic Silica Oxide Nanoparticles Synthesized.” Journal of Natural Gas Science and Engineering 91 (2021): 103928. [14] Akinyemi, O. P., and A. Abdulhadi. “Effects of Temperature on the Rheological Properties of Rice Husk Modified Bentonite Drilling Fluids.” International Journal of Engineering and Management Research 12, no. 6 (2022): 240–250. [15] Peter, A. O., and L. O. Olamilekan. “Comparative Study of Rice Husk and Xanthan Gum as Viscosifier in Water-Based Drilling Fluid.” International Journal of Advanced Research in Science, Engineering and Technology 9, no. 1 (2022). [16] Akinyemi, O. P., A. Abdulhadi, and A. Aliyu. “Impacts of Additives on Rheological Properties of Rice Husk and Xanthan Gum Modified Drilling Fluid.” International Advanced Research Journal in Science, Engineering and Technology 10, no. 3 (2023). [17] Ali, J. K. “Neural Networks: A New Tool for the Petroleum Industry.” In SPE European Petroleum Computer Conference, Paper SPE-27561. Richardson, TX: SPE, 1994. [18] González, A., Maria A. Barrufet, and R. Startzman. “Improved Neural-Network Model Predicts Dewpoint Pressure of Retrograde Gases.” Journal of Petroleum Science and Engineering 37, no. 3–4 (2003): 183–194. [19] Lotfi, Ehsan., and HamidReza Navidi. “A New Model for Determining OPEC Oil Production Level Based on Price Forecast and Game Theory.” Journal of Modeling in Engineering 11, no. 35 (2014): 63–76. (in Persian) [20] Berenjkar, Ehsan. “Evaluation of the Performance of Artificial Neural Networks Combined with Meta-Heuristic Algorithms of Wall and Ants in Estimating the Penetration Rate of Drilling and Comparison with Simple Neural Networks and Common Mathematical Models.” Journal of Modeling in Engineering 19, no. 65 (2021): 115–135. (in Persian) [21] Mirshah, HamidReza., Ramin Ghasemiasl, Nahid Raoufi, and Mehrdad Malekzadeh. “Modeling and Prediction of Flash Point of Hydrocarbon Compounds Using Neural Network.” Journal of Modeling in Engineering 19, no. 64 (2021): 109–116. (in Persian) [22] Mohaghegh, S. “Neural Network: What It Can Do for Petroleum Engineers.” Journal of Petroleum Technology 47, no. 1 (1995): 42–42. [23] Mohaghegh, S. “Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks.” Journal of Petroleum Technology 52, no. 9 (2000): 64–73. [24] Avcı, E. “An Artificial Neural Network Approach for the Prediction of Water-Based Drilling Fluid Rheological Behaviour.” International Advanced Researches and Engineering Journal 2, no. 2 (2018): 124–131. [25] Elkatatny, Salaheldin. “Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks.” Sustainability 11, no. 18 (2019): 5008. [26] Oguntade, T., T. Ojo, E. Efajemue, B. Oni, and J. Idaka. “Application of ANN in Predicting Water Based Mud Rheology and Filtration Properties.” In SPE Nigeria Annual International Conference and Exhibition, Paper D013S009R012. Richardson, TX: SPE, August 2020. [27] Gomaa, Ibrahim, Salaheldin Elkatatny, and AbdulAzeez Abdulraheem. “Real-Time Determination of Rheological Properties of High Over-Balanced Drilling Fluid Used for Drilling Ultra-Deep Gas Wells Using Artificial Neural Network.” Journal of Natural Gas Science and Engineering 77 (2020): 103224. [28] Ismail, Atif, Hafiz Muhammad Awais Rashid, Raoof Gholami, and Arshad Raza. “Characterization-Based Machine Learning Modeling for the Prediction of the Rheological Properties of Water-Based Drilling Mud: An Experimental Study on Grass as an Environmental Friendly Additive.” Journal of Petroleum Exploration and Production Technology 12, no. 6 (2022): 1677–1695. [29] Strømø, S. M. Formulation of New Drilling Fluids and Characterization in HPHT. Master’s thesis, University of Stavanger, Norway, 2019. [30] Stubble, F. William A., and George F. Luger. Strategies for Complex Problem Solving & Artificial Intelligence Structures. Wesley Longman Inc., 2000. [31] Rashidi, Mohammad Mehdi, O. Anwar Bég, Amir Basiri Parsa, and Forouzan Nazari. “Analysis and Optimization of a Transcritical Power Cycle with Regenerator Using Artificial Neural Networks and Genetic Algorithms.” Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 225, no. 6 (2011): 701–717. [32] Khalil, Munawar, and B. Mohamed Jan. “Herschel-Bulkley Rheological Parameters of a Novel Environmentally Friendly Lightweight Biopolymer Drilling Fluid from Xanthan Gum and Starch.” Journal of Applied Polymer Science 124, no. 1 (2012): 595–606. | ||
|
آمار تعداد مشاهده مقاله: 486 تعداد دریافت فایل اصل مقاله: 152 |
||
