بهینه‌سازی تولید نفت در چاه‌های هوشمند با روش طرح آزمایش‌ها

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده ریاضی و علوم کامپیوتر، دانشگاه صنعتی امیرکبیر

2 پژوهشکده مطالعات مخازن و توسعه میادین، پژوهشگاه صنعت نفت

چکیده

بهینه‌سازی تولید از میادین هیدروکربوری یکی از دغدغه‌های اصلی مدیریت مخازن نفت و گاز است. در این راستا از تکنولوژی چاه هوشمند که در دهه اخیر توسعه یافته، استفاده می‌شود. از جمله چالش‌های مهم این تکنولوژی، تنظیم بهینه شیرهای کنترلی هوشمند است. در این مقاله با هدف بهینه‌سازی تنظیمات شیرهای کنترلی هوشمند، نتایج حاصل با حالت معمولی مقایسه شده است. در این راستا از روش‌های طرح آزمایش تاگوچی و سطح پاسخ برای مدل‌سازی و تعیین ارتباط تنظیمات شیرهای کنترلی با مقدار تولید نفت و بهینه‌سازهای غیرخطی برای تعیین تنظیمات بهینه شیرهای کنترلی به منظور بیشینه کردن تولید نفت، کمینه کردن تولید آب و کاهش زمان محاسبات استفاده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Oil Production Optimization in Smart Wells Using Experimental Design Methodology

نویسندگان [English]

  • Seyed Ali Mirhassan 1
  • Morteza Hassanabadi 1
  • Seyed Mahdia Motahhari 2
  • Amir Abbas Askari 2
1 Faculty of Mathematics & Computer Sciences, Amirkabir University of Tecnololgy
2 Faculty of Reservoir Studies& Fields Development, Research Institute of Petroleum Industry (RIPI)
چکیده [English]

The optimization of hydrocarbon produced from the fields is one of the main concerns of the management of oil and gas reservoirs. In this respect, smart well technology developed in recent decades has been used. Among the main challenges of this important technology, one may refer to the optimized planning of intelligent control valves. This paper is based on the results of reservoir simulations, the behavior of smart wells, and use of experimental design Taguchi methods and response surface modeling and procedures are used to obtain and nonlinearly optimize the correlation between intelligent control valve settings and rate of oil production, and therefore to increase the rate of oil production while minimizing the rate of water production and calculation time.

کلیدواژه‌ها [English]

  • Intelligent Control Valves (ICV)
  • Smart Well
  • Optimization
  • Taguchi Method
  • Response Surface Method
مراجع

[1]. Gao C., Rajeswaran T., Curtin U., and Nakagawa E., “A literature Review on Smart-Well Technology”, presented to the SPE 106011 and Operations Symposium held in Oklahoma, U.S.A., 31 March-3 April, 2007 .

[2]. Yeten B., Durlofsky L.J., and Khalid A., Optimization of Smart Well Control, presented to the SPE.Conf,held in Calgary, Alberta ,Canada, pp. 4-7 November, 2002.

[3]. Oberwinker C., Stundner M., and Team D., “From real time data to production optimization” presented to the SPE 87008 Asia pacific Conference, pp. 29-30 March, 2004.

[4]. Naus M.M.J.J, Dolle N., and Jansen J.,-D “Optimization of commingled production using infinitely variable Inflow Control Valves”, presented to the SPE 90959 Annual technical Conference and Exhibition , Houston, pp. 26-29 September, 2005 .

5]. Yeten B., Brouwer D.R., Durlofsky L.J, and Aziz K. “Decision analysis under uncertainty for smart well deployment”, Journal of petroleum Science & Engineering, 43, pp. 183-199, 2004.

[6]. Aitokhuehi I., and Durlofsky L.J., “Optimization the performance of smart wells in complex reservoirs using continuously updated geological models”, Journal of petroleum Science & Engineering, 48, pp. 254-264, 2005.

[7]. Taware S., Sharme M., Alhuthali A.H., and Gupta A.D., “Optimal water flood management under geological uncertainty using accelerated production strategy”, presented to the SPE 133882 Annual technical Conference and Exhibition held in Florence, Italy, pp. 19-22, September 2010.

[8]. Alhuthali A.H., Gupta A.D., Yuen B., and Fontanilla J.P., “Field applications of waterflood optimization via optimal rate control with smart well”, presented to the SPE 118948 Reservoir Simulation Symposium held in The Woodlands, Texas, pp. 2-4, February 2009.

[9]. Van Essen G.M., Jansen J.D., Brouwer D.R., Douma S.G., Rollett K.I., and Harris D.P., “Optimization of smart wells in the St. Joseph Field”, presented to the SPE 123563 Asia Pacific Oil and Gas Conference and Exhibition held in Jakarta, Indonesia, pp. 4-6, August 2009.

[10]. Alhuthali A.H., Gupta A.D., Yuen B., and Fontanilla J.P., “Optimal rate under Geologic Uncertainty”, presented to the SPE/DOE 113628 improved oil recovery symposium held in Tulsa, Oklahoma, U.S.A, pp. 19-23, April 2008.

[11]. Shuai Y., White C.D., Zhang H., and Sun T., “Using multiscale regularization to obtain realistic optimal Control Strategies”, presented to the SPE 142043 Reservoir Simulation Symposium held in The Woodlands, Texas, USA, pp. 21-23 February 2011.

[12]. Moreno J.C., Bradley D., Gurpinar O., Richter P., Hussain A., Shammari M., and Garni S., “Optimized workflow for designing complex wells”, presented to the SPE 99999 Europec/EAGA conference and Exhibition held in Vienna, Austria, pp. 12-15, June 2006 .

[13]. Meun P., Tondel P., Godhavn J.M., and Aamo O.M., “Optimization of Smart Well production through nonlinear model predictive control”, presented to the SPE.Conf and Exhibition held in Amsterdam , The Netherlands, pp. 25-27, February 2008.

[14]. Al-Ghareeb Z.M., Horne R.N., Yuen B.B., and Shenawi S.H., “Proactive optimization of oil recovery in multilateral wells using real time production data”, presented to the SPE 124999 Annual Technical Conference and Exhibition held in New Orleans, USA, pp. 4-7, October 2009.

[15]. Al-Ghareeb Z.M., Monitoring and control of Smart Wells, MSc Dissertation, Stanford University, 2009.

[16]. Conejeros R., and Lenoach B., “Model-based optimal of dual completion wells”, Journal of Petroleum Science and Engineering Vol, 43, pp. 1-14, 2004.

[17]. Anderson M.J., Whitcomb P.J., “DOE Simplified: practical tools for effective experimentation”, by Productivity, DX7 manual, 2000.

[18]. Beielstein T.B., Chiarandini M., Paquete L., and Preuss M., Experimental Methods for the Analysis of Optimization Algorithms, Springer-Verlag Berlin Heidelberg, 2010.

[19]. Montgomery D.C., Design and Analysis of Experiments, John Wiley & Sons, 2001.

[20]. Roy R.K., A primer on the TAGUCHI method by van nostrand reinhold, 1990.

[21]. Singh H., and Kumar P., “Optimizing multi-machining characteristics through Taguchi’s approach and utility concept”, J. Manufact. Technol. Manage, 17, pp. 255–274, 2006.

[22]. Singh H., Kumar P., “Quality optimization of turned parts (En 24 steel) by Taguchi method”, Prod. J. 144, pp. 43–49, 2003.

[23]. Aggarwal A., Singh H., Kumar P., Singh M., “Optimization power consumption for CNC turned parts using response surface methodology and Taguchi technique-A comparative analysis”, Materials Processing Technology 200, pp. 373-384, 2008.

[24]. Myers R.H., and Montgomery D.C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments, by John Wiley & Sons, 2002.

[25]. Carley K.M., Natalia Y., Kamneva N.Y., and Reminga J., Response Surface Methodology, CASOS Technical Report, 2004.

[26]. Mehrabani J.V., Noaparast M., Mousavi S.M., Dehghan R., and Ghorbani A., “Process optimization and modeling of sphalerite flotation from a low-grade Zn-Pb ore using response surface methodology” Sphalerite Purification Technology 72, pp. 242-249, 2010.