A Data Driven Approach to Predict Frictional Pressure Losses in Polymer-Based Fluids
- Sercan Gul (The University of Texas at Austin) | Mitchell David Johnson (The University of Texas at Austin) | Ali Karimi Vajargah (The University of Texas at Austin) | Zheren Ma (The University of Texas at Austin) | Besmir Buranaj Hoxha (The University of Texas at Austin) | Eric van Oort (The University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE/IADC International Drilling Conference and Exhibition, 5-7 March, The Hague, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2019. SPE/IADC Drilling Conference and Exhibition
- 1.7 Pressure Management, 1.11.2 Drilling Fluid Selection and Formulation (Chemistry, Properties), 7.6.6 Artificial Intelligence, 7.6.4 Data Mining, 1.6 Drilling Operations, 1.11 Drilling Fluids and Materials, 7.6 Information Management and Systems, 3 Production and Well Operations, 7 Management and Information, 3 Production and Well Operations, 1.7.5 Well Control
- polymer based fluids, managed pressure drilling, turbulent flow, data analytics, machine learning
- 104 in the last 30 days
- 116 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Managing drilling margins in challenging wells requires precise prediction of frictional pressure losses and equivalent circulating density (ECD). Current hydraulic models in the industry fail to accurately predict the frictional pressure losses of certain mud formulations in turbulent flow due to the complex behavior of long-chain polymer additives. These additives facilitate friction reduction in certain flow regimes. This reduction depends on several parameters, such as molecular weight and chemical composition of the polymers, making it difficult to quantify using existing models. In this paper, a data-driven approach is proposed to precisely predict frictional pressure losses for polymer-based fluids.
A flow-loop was constructed to measure frictional pressure-losses of several polymer-based non-Newtonian fluids under laminar, transitional, and turbulent flow regimes. Pressure loss data was obtained for fluids with different polymer concentrations at various temperatures using differential pressure measurement. A database of the experimental data was compiled and used to build a predictive model for frictional pressure prediction using advanced machine learning techniques. The proposed approach has general validity and can be extended to any type of well construction fluid (used in drilling, completion, stimulation, or workover).
Results for frictional pressure loss predictions from the proposed data-driven approach were compared with both the experimental data and widely used industry models. An excellent agreement was observed between the proposed approach and the experimental results, demonstrating the applicability of this approach for hydraulic modeling of polymer-based fluids. The improvements are particularly noticeable at higher polymer concentrations in the turbulent flow regime, where the average percentage discrepancy between the existing models and the experimental data can be as high as 45%.
The proposed approach in this study is particularly valuable for wells with a narrow drilling margin and concerns about the ability to manage ECDs (such as slim hole wells, deepwater wells or extended reach wells). It can assist with better planning and avoiding non-productive time and drilling problems such as lost circulation, stuck pipe, wellbore instability, and well control events. Its adaptability to a wide range of fluids using an expanded database makes it particularly attractive as a practical solution to this challenging problem.
|File Size||1 MB||Number of Pages||20|
Bar, N., Bandyopadhyay, T.K., Biswas, M.N.. 2010. Prediction of Pressure Drop Using Artificial Neural Network for Non-Newtonian Liquid Flow Through Piping Components. Journal of Petroleum Science and Engineering 71 (3-4):187–194. http://dx.doi.org/10.1016/j.petrol.2010.02.001.
Bardestani S., Givehchi M., Younesi E.. 2017. Predicting Turbulent Flow Friction Coefficient Using ANFIS Technique. Signal, Image and Video Processing 11 (2): 341–347. http://dx.doi.org/10.1007/s11760-016-0948-8.
Blasius H. 1913. Das Aehnlichkeitsgesetz bei Reibungsvorgängen in Flüssigkeiten. In: Verein deutscher Ingenieure (eds) Mitteilungen über Forschungsarbeiten auf dem Gebiete des Ingenieurwesens. Mitteilungen über Forschungsarbeiten auf dem Gebiete des Ingenieurwesens (insbesondere aus den Laboratorien der technischen Hochschulen), vol 131. Berlin, Heidelberg: Springer.
Chhabra, R.P and Richardson, J.R. 1999. Heat Transfer Characteristics of Non-Newtonian Fluids in Pipes. In Non-Newtonian Flow in the Process Industries, First Edition, Chapter 6. 260-288. Butterworth-Heinemann: Elsevier. http://dx.doi.org/10.1016/B978-0-7506-3770-1.X5000-3.
Colebrook, C.F. 1939. Turbulent Flow in Pipes, with Particular Reference to the Transition Region Between the Smooth and Rough Pipe Laws. Journal of the Institution of Civil Engineers 11 (4): 133-156. http://dx.doi.org/10.1680/ijoti.1939.13150.
den Toonder, J.M.J. and Nieuwstadt, F.T.M. 1997. Reynolds number effects in a turbulent pipe flow for low to moderate Re. Phys Fluids 9 (11): 3398-3409. https://dx.doi.org/10.1063/1.869451.
Desouky, S.E.M, El-Emam, N. 1990. A Generalized Pipeline Design Correlation for Pseudoplastic Fluids. Journal of Canadian Petroleum Technology 29 (5): 48-54. http://dx.doi.org/10.2118/90-05-02.
Desouky, S.E.M. and Al-Awad, M.N. 1998, A New Laminar-to-Turbulent Transition Criterion for Yield-Pseudoplastic Fluids. Journal of Petroleum Science and Engineering 19 (3-4): 171-176. http://dx.doi.org/10.1016/S0920-4105(97)00044-2.
Dosunmu, I.T., Shah, S.N. 2013. Evaluation of Friction Factor Correlations and Equivalent Diameter Definitions for Pipe and Annular Flow of Non-Newtonian Fluids. Journal of Petroleum Science and Engineering 109: 80-86. http://dx.doi.org/10.1016/j.petrol.2013.02.007.
Duhen, T., Sagot, A., and Kerbart, Y. 1997. Deep Offshore Slim Hole Drilling. Presented at the Offshore Technology Conference, Houston, Texas, 5-8 May. OTC-8557-MS. http://dx.doi.org/10.4043/8557-MS.
Gordon, R.J. 1970. On the Explanation and Correlation of Turbulent Drag Reduction in Dilute Macromolecular Solutions. Journal of Applied Polymer Science 14 (8): 2097-2105. http://dx.doi.org/10.1002/app.1970.070140817.
Hanks, R.W. and Ricks, B.L. 1974. Laminar-Turbulent Transition in Flow of Pseudoplastic Fluids with Yield Stresses. Journal of Hydronautics 8 (4): 163-166. http://dx.doi.org/10.2514/3.61992.
Karimi Vajargah, A., & Oort, E. van. (2015, March 17). Automated Drilling Fluid Rheology Characterization with Downhole Pressure Sensor Data. Society of Petroleum Engineers. doi: 10.2118/173085-MS
Karimi Vajargah, A., Sullivan, G., & Oort, E. van. (2016, September 14). Automated Fluid Rheology and ECD Management. Society of Petroleum Engineers. doi: 10.2118/180331-MS
Kelessidis, V. C., Dalamarinis, P., and Maglione, R. 2011. Experimental Study and Predictions of Pressure Losses of Fluids Modeled as Herschel-Bulkley in Concentric and Eccentric Annuli in Laminar, Transitional and Turbulent Flows. Journal of Petroleum Science and Engineering 77 (3-4): 302-312. http://dx.doi.org/10.1016/j.petrol.2011.04.l004.
Kestin, J., Sokolov, M., and Wakeham, W.A. 1978. Viscosity of Liquid Water in the Range -8 °C to 150 °C. Journal of Physical and Chemical Reference Data 7 (941): 941-948. http://dx.doi.org/10.1063/1.555581.
Ma, Z., Vajargah, A. K., Chen, D., van Oort, E., May, R., MacPherson, J. D., … Curry, D. (2018, March 6). Gas Kicks in Non-Aqueous Drilling Fluids: A Well Control Challenge. Society of Petroleum Engineers. doi: 10.2118/189606-MS
Malin, M.R. 1998. Turbulent Pipe Flow of Herschel-Bulkley Fluids. Int. Comm. Heat Mass Transfer 25 (3): 321-330. http://dx.doi.org/10.1016/S0735-1933(98)00019-0.
Merlo, A., Maglione, R., & Piatti, C. (1995, January 1). An Innovative Model For Drilling Fluid Hydraulics. Society of Petroleum Engineers. doi: 10.2118/29259-MS
Mishra, P and Tripathi, G. 1971. Transition from Laminar to Turbulent Flow of Purely Viscous Non-Newtonian Fluids in Tubes. Chemical Engineering Science 26 (6): 915-921. http://dx.doi.org/10.1016/0009-2509(71)83051-8.
Mohr, W.D., Clapp, J.B., and Starr, F.C. 1961. Flow Patterns in a Non-Newtonian Fluid in a Single-Screw Extruder. Polymer Engineering and Science 1 (3): 113-120. http://dx.doi.org/10.1002/pen.760010306.
Najafzadeh, M., Shiri, J., Sadeghi, G.. 2018. Prediction of the Friction Factor in Pipes Using Model Tree. ISH Journal of Hydraulic Engineering 24 (1):9–15. http://dx.doi.org/10.1080/09715010.2017.1333926.
Ogugbue, C. C., & Shah, S. (2011, December 1). Laminar and Turbulent Friction Factors for Annular Flow of Drag-Reducing Polymer Solutions in Coiled-Tubing Operations. Society of Petroleum Engineers. doi: 10.2118/130579-PA
Özger, M. and Yildirim, G. 2009. Determining Turbulent Flow Friction Coefficient Using Adaptive Neuro-Fuzzy Computing Technique. Advances in Engineering Software 40 (4):281–287. http://dx.doi.org/10.1016/j.advengsoft.2008.04.006.
Ramakrishnan, T. S., Ramamoorthy, R., Fordham, E., Schwartz, L., Herron, M., Saito, N., & Rabaute, A. (2001, January 1). A Model-Based Interpretation Methodology for Evaluating Carbonate Reservoirs. Society of Petroleum Engineers. doi: 10.2118/71704-MS
Reed, T. D., & Pilehvari, A. A. (1993, January 1). A New Model for Laminar, Transitional, and Turbulent Flow of Drilling Muds. Society of Petroleum Engineers. doi: 10.2118/25456-MS
Ryan, N.W. and Johnson, M.M. 1959. Transition From Laminar to Turbulent Flow in Pipes. AIChE Journal 5 (4): 433-435. http://dx.doi.org/10.1002/aic.690050407.
Salmasi, F., Khatibi, R., and Ghorbani, M.A. 2012. A Study of Friction Factor Formulation in Pipes Using Artificial Intelligence Techniques and Explicit Equations. Turkish J. Eng. Env. Sci. 36:121–138. http://dx.doi.org/10.3906/muh-1008-30.
Samadianfard, S., Taghi, S.M., Kisi, O.. 2014. Determining Flow Friction Factor in Irrigation Pipes Using Data Mining and Artificial Intelligence Approaches. Applied Artificial Intelligence 28 (8):793–813. http://dx.doi.org/10.1080/08839514.2014.952923.
Savins, J. G. (1964, September 1). Drag Reduction Characteristics of Solutions of Macromolecules In Turbulent Pipe Flow. Society of Petroleum Engineers. doi: 10.2118/867-PA
Shah, S. N. (1990, May 1). Effects of Pipe Roughness on Friction Pressures of Fracturing Fluids. Society of Petroleum Engineers. doi: 10.2118/18821-PA
Shah, S., Asadi, M., Wheeler, R., Brannon, H., Kakadjian, S., Ainley, B., Archacki, D. (2018, February 7). Methodology for Evaluating Drag Reduction Characteristics of Friction Reducer. Society of Petroleum Engineers. doi: 10.2118/189537-MS
Slatter, P.T. 1997. The Rheological Characterization of Sludges. Wat Sci Tech 36 (11): 9-18. http://dx.doi.org/10.1016/S0273-1223(97)00663-X.
Subramanian, R., & Azar, J. J. (2000, January 1). Experimental Study on Friction Pressure Drop for Non-Newtonian Drilling Fluids in Pipe and Annular Flow. Society of Petroleum Engineers. doi: 10.2118/64647-MS
Szilas, A.P., Bobok, E., and Navratil, L. 1981. Determination of Turbulent Pressure Loss of Non-Newtonian Oil Flow in Rough Pipes. Rheologica Acta 20 (5): 487-496. http://dx.doi.org/10.1007/BF01503271.
Tomita, Y. 1959. On the Fundamental Formula of Non-Newtonian Flow. JSME 2 (7): 469-474. http://dx.doi.org/10.1299/jsme1958.2.469.
Toonder, J.M.J.D, Hulsen, M.A, Kuiken, G.D.C.. 1997. Drag Reduction by Polymer Additives in a Turbulent Pipe Flow: Numerical and Laboratory Experiments. Journal of Fluid Mechanics 337: 193-231. http://dx.doi.org/10.1017/50022112097004850.
Virk, P. S. 1975. Drag Reduction Fundamentals. AIChE Journal 21 (4): 625-656. http://dx.doi.org/10.1002/aic.690210402.
Walsh, M. 1967. Theory of Drag Reduction in Dilute High-Polymer Flows. International Shipbuilding Progress 14 (152):134-139. http://dx.doi.org/10.3233/ISP-1967-1415202.