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https://doi.org/10.58195/iaet.v3i1.150
Jia, Y., & Lei, J. . (2024). Experimental Study on the Performance of Frictional Drag Reducer with Low Gravity Solids. Innovations in Applied Engineering and Technology, 3(1), 1–22. https://doi.org/10.58195/iaet.v3i1.150
Volume 3, Issue 1

Experimental Study on the Performance of Frictional Drag Reducer with Low Gravity Solids

Reducing energy consumption during drilling operations is beneficial to both the environment and economy. Frictional drag reducers (FDR) are widely used to reduce the energy loss caused by turbulent flow. FDR plays an important role in flow lines as they can reduce the frictional pressure drop effectively, and benefit the selection of circulating fluid and pump. However, several factors can influence the performance of FDR, including fluid additives and incorporated solids, such as drill solids. Thus, the main objective of this paper is to study the influence of low gravity solids (LGS) on the performance of the FDR. This paper is mainly based on experimental study. The experimental work contains two parts: rheology characterization and flow loop tests. Rheology characterization tests were performed to calculate the flow consistency index (K) and flow behavior index (n). Flow loop experiments were conducted for two geometry (0.457 inch and 0.797 inch diameter). Xanthan gum was used as a fractional drag reducer. Bentonite and quartz sand were added as low gravity solids. Three designed water-based mud systems are tested for drag reduction efficiency of Xanthan gum. Flow rate of the mud varied from 3 gpm to 16 gpm. Concentration of Xanthan ranged from 0.1 lbm/bbl to 0.6 lbm/bbl. Low weight solids were added with weight percentage of 0.5%, 1%, 2% and 2.5%. The result shows that xanthan gum is an efficient drag reducer for adequate reasons. Firstly, even at al low concentration, xanthan gum shows high resistance to degradation. Secondly, the maximum drag reduction with xanthan gum is up to 70.54% with a concentration of 0.6 lbm/bbl. However, the existence of different low gravity solids influence the efficiency of xanthan gum in different styles. Experiment results indicate that the higher the weight percentage of bentonite, the lower the drag reduction effectiveness. While with the increasing concentration of quartz sand, the drag reduction does not show an intense change. This study intents to give an instructive guidance on usage of frictional drag reducers in drilling mud system design. Removal of low gravity solids from the mud is difficult, which pose a danger to the drilling fluid. By understanding the effectiveness of FDR, we can reduce energy consumption when irremovable low gravity solids exist. FDR can be used for modifying the mud contents to develop a lower pressure gradient under turbulent flow condition. In the same scenario, adding FDR can suppress turbulent at a constant pressure gradient but with a higher flow rate.

energy consumption reduction xanthan gum drag reducer friction factor pressure loss

References

  1. Zheng D, Ozbayoglu E, Miska SZ, Liu Y, Li Y. Cement Sheath Fatigue Failure Prediction By ANN-Based Model. In Offshore Technology Conference, Houston, TX, USA, 2–5 May 2022.
  2. Zheng D, Ozbayoglu EM, Miska SZ, Liu Y. Cement Sheath Fatigue Failure Prediction By Support Vector Machine Based Model. SPE Eastern Regional Meeting, Wheeling, WV, USA, 18–20 October 2022. DOI: https://doi.org/10.2118/211880-MS
  3. Liu Y, Upchurch ER, Ozbayoglu EM, Baldino S, Zheng D, Wang J. Gas Migration Model for Non-Newtonian Fluids under Shut-in Well Conditions. SPE/IADC Drilling Conference and Exhibition, Stavanger, Norway, 7–9 March 2023. DOI: https://doi.org/10.2118/212466-MS
  4. Liu Y, Upchurch ER, Ozbayoglu EM, Baldino S, Wang J, Zheng D. Design and Calculation of Process Parameters in Bullheading and Pressurized Mud Cap Drilling. SPE/IADC Drilling Conference and Exhibition, Stavanger, Norway, 7–9 March 2023. DOI: https://doi.org/10.2118/212455-MS
  5. Luo C, Cao Y, Liu Y, Zhong S, Zhao S, Liu Z, Liu Y, Zheng D. Experimental and Modeling Investigation on Gas-Liquid Two-Phase Flow in Horizontal Gas Wells. Journal of Energy Resources Technology 2023; 145(1): 013102. DOI: https://doi.org/10.1115/1.4055223
  6. Ke W, Zeng H, Wang Z, Yu H, Liu Y, Zheng D, Zhu J, Zhu H. A Numerical Study on Labyrinth Screw Pump (LSP) Performance under Viscous Fluid Flow. Energies 2023; 16(16): 5997. DOI: https://doi.org/10.3390/en16165997
  7. Zhu H, Liu Y, Tychus D, Zheng D, Adiraju S, Tatu J, Zhu J, Zhang H. Sensitivity Analysis on Tailpipe Design for Mitigating Terrain Slug in Horizontal Wells. Geoenergy Science and Engineering 2024; 235(2): 212598. DOI: https://doi.org/10.1016/j.geoen.2023.212598
  8. Virk PS. Drag Reduction Fundamentals. AIChE Journal 1975; 21(4): 625–656. DOI: https://doi.org/10.1002/aic.690210402
  9. Lumley JL. Drag Reduction in Turbulent Flow By Polymer Additives. Journal of Polymer Science: Macromolecular Reviews 1973; 7(1): 263–290. DOI: https://doi.org/10.1002/pol.1973.230070104
  10. Hershey HC, Zakin JL. A Molecular Approach to Predicting the Onset of Drag Reduction in the Turbulent Flflow of Dilute Polymer Solutions. Chemical Engineering Science 1967; 22: 1847–1857. DOI: https://doi.org/10.1016/0009-2509(67)80215-X
  11. Zimm BH. Dynamics of Polymer Molecules in Dilute Solutions: Viscoelasticity, Flow Birefringence and Dielectric Loss. The Journal of Chemical Physics 1956; 24: 269–79. DOI: https://doi.org/10.1063/1.1742462
  12. Metzner AB, Metzner AP. Stress Levels in Rapid Extensional Flows of Polymeric Fluids. Rheologica Acta 1970; 9(2): 174–181. DOI: https://doi.org/10.1007/BF01973476
  13. Virk PS, Merrill EW, Mickley HS, Smith KA, Mollo-Christensen EL. The Toms Phenomenon: Turbulent Pipe Flow of Dilute Polymer Solutions. Journal of Fluid Mechanics 1967; 30(2): 305–328. DOI: https://doi.org/10.1017/S0022112067001442
  14. De Gennes PG. Introduction to Polymer Dynamics; Cambridge University Press: Cambridge, UK, 1990. DOI: https://doi.org/10.1017/CBO9780511569463
  15. Tabor M, De Gennes PG. A Cascade Theory of Drag Reduction. EPL (Europhysics Letters) 1986; 2(7): 519. DOI: https://doi.org/10.1209/0295-5075/2/7/005
  16. Sreenivasan KR, White CM. The Onset of Drag Reduction By Dilute Polymer Additives, and the Maximum Drag Reduction Asymptote. Journal of Fluid Mechanics 2000; 409: 149–164. DOI: https://doi.org/10.1017/S0022112099007818
  17. Zheng D, Miska SZ, Ozbayoglu E. The Influence of Formation Creeping on Wellbore Integrity. SPE 2021 Symposium Compilation, Virtual, 26 November 2021. DOI: https://doi.org/10.2118/208442-MS
  18. Zheng D, Miska Z, Ozbayoglu, Zhang Y. The Influence of Elliptical-Geometry Wellbore on Zonal Isolation. 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, NM, USA, 26–29June 2022. DOI: https://doi.org/10.56952/ARMA-2022-0117
  19. L’vov VS, Pomyalov A, Procaccia I, Tiberkevich V. Drag Reduction By Polymers in Wall Bounded Turbulence. Physical review letters 2004; 92(24): 244503. DOI: https://doi.org/10.1103/PhysRevLett.92.244503
  20. Lumley JL. Drag Reduction By Additives. Annual Review of Fluid Mechanics 1969; 1: 367–84. DOI: https://doi.org/10.1146/annurev.fl.01.010169.002055
  21. Ryskin G. Turbulent Drag Reduction By Polymers: a Quantitative Theory. Physical review letters 1987; 59(18): 2059. DOI: https://doi.org/10.1103/PhysRevLett.59.2059
  22. Hansen RJ, Little RC. Early Turbulence and Drag Reduction Phenomena in Larger Pipes. Nature 1974; 252: 690. DOI: https://doi.org/10.1038/252690a0
  23. Forame PC, Hansen RJ, Little RC. Observations of Early Turbulence in the Pipe Flow of Drag Reducing Polymer Solutions. AIChE Journal 1972; 18: 213–217. DOI: https://doi.org/10.1002/aic.690180139
  24. Samanta D, Dubief Y, Holzner M, Sch¨afer C, Morozov A N, Wagner C, Hof B. Elasto-Inertial Turbulence. Proceedings of the National Academy of Sciences 2013; 110(26): 10557–10562. DOI: https://doi.org/10.1073/pnas.1219666110
  25. Zheng D, Miska S, Ziaja M, Zhang J. Study of Anisotropic Strength Properties of Shale. AGH Drilling Oil Gas 2019; 36(1): 93–112. DOI: https://doi.org/10.7494/drill.2019.36.1.93
  26. Zheng D, Ozbayoglu E, Miska S, Zhang J. Experimental Study of Anisotropic Strength Properties of Shale. In ARMA US Rock Mechanics/Geomechanics Symposium, Atlanta, GA, USA, 25–28 June 2023. DOI: https://doi.org/10.56952/ARMA-2023-0128
  27. Rafieepour S, Zheng D, Miska S, Ozbayoglu E, Takach N, Yu M, Zhang J. Combined Experimental and Well Log Evaluation of Anisotropic Mechanical Properties of Shales: An Application to Wellbore Stability in Bakken Formation. SPE Annual Technical Conference and Exhibition, Virtual, 26–29 October 2020. DOI: https://doi.org/10.2118/201334-MS
  28. Wang J, Ozbayoglu E, Baldino S, Liu Y, Zheng D. Time Series Data Analysis with Recurrent Neural Network for Early Kick Detection. Offshore Technology Conference, Houston, TX, USA, 1–4 May 2023. DOI: https://doi.org/10.4043/32428-MS
  29. Zheng D, Miska S, Ozbayoglu E, Zhang J. Combined Experimental and Well Log Study of Anisotropic Strength of Shale. SPE Annual Technical Conference and Exhibition, San Antonio, TX, USA, 16–18 October 2023. DOI: https://doi.org/10.2118/214981-MS
  30. Peng C, Pang J, Fu J, Cao Q, Zhang J, Li Q, Deng Z, Yang Y, Yu Z, Zheng D. Predicting Rate of Penetration in Ultra-Deep Wells Based on Deep Learning Method. Arabian Journal for Science and Engineering 2023; 48(12): 16753–16768. DOI: https://doi.org/10.1007/s13369-023-08043-w
  31. Zheng D, Ozbayoglu E, Miska S, Silvio B, Liu Y, Wang J. The Influence of Casing Eccentricity on Zonal Isolation. In ARMA US Rock Mechanics/Geomechanics Symposium, Atlanta, GA, USA, 25–28 June 2023. DOI: https://doi.org/10.56952/ARMA-2023-0159
  32. Lin R, Yu Z, Zhao J, Ren L, Wu J, Wu J, Song Y, Zheng D. Cluster Spacing Optimization of Deep Shale Gas Fracturing With Non-Uniform Geostress. Petroleum Science and Technology 2022; 1–18. DOI: 10.1080/10916466.2022.2148694. DOI: https://doi.org/10.1080/10916466.2022.2148694
  33. Chen X, Shi Z, Zheng D. Application of Pre-Stack Elastic Impedance Inversion to Metamorphic Reservoir Prediction in JZ25-1 Oilfield. Editorial Department of Petroleum Geology and Recovery Efficiency 2015; 22(4): 69–73.
  34. Ren L, Yu Z, Zheng D, Lin R, Xie M. Towards Intelligent Shale-Gas Horizontal Well Fracturing: Neural Network and Particle Swarm Optimization for Predicting the Test Production of Shale Gas Horizontal Well. Energy in Data Conference, Austin, TX, USA, 20–23 February 2022. DOI: https://doi.org/10.7462/eid2022-08.1
  35. Guo S, Li Y, Chen T, Zheng D, Du J, Bu Y, Zhou A, Pang X, Liu H. Controlling the Hydration Rate of Alkali-Activated Slag By the Slow Release of NaOH. Geoenergy Science and Engineering 2023; 228: 211960. DOI: https://doi.org/10.1016/j.geoen.2023.211960
  36. Zheng, Danzhu, Turhan, Cinar, Wang, Ningyu, Ashok, Pradeepkumar, and Eric van Oort. Prioritizing Wells for Repurposing or Permanent Abandonment Based on Generalized Well Integrity Risk Analysis. IADC/SPE International Drilling Conference and Exhibition, Galveston, TX, USA, 5–7March 2024. DOI: https://doi.org/10.2118/217738-MS
  37. Liu Y, Upchurch ER, Ozbayoglu EM, Baldino S, Zheng D, Wang J. Prediction of Rise Velocity of Taylor Bubbles in Pipes Using an Artificial Neural Network. Authorea. January 16, 2024. DOI: 10.22541/au.170536908.87694225/v1. DOI: https://doi.org/10.22541/au.170536908.87694225/v1
  38. Yang Bi, Shenglai Guo, Tian Chen, Danzhu Zheng, Kai Wang, Yuhuan Bu, Huajie Liu. Preparation and Characteristics of a pH Intelligent Response Blocking Agent for the Microcrack in the Cement Sheath. ACS Omega 2023; 8(39):36284–36291. DOI: 10.1021/acsomega.3c04865. DOI: https://doi.org/10.1021/acsomega.3c04865
  39. Luo Z, Xu H, Chen F. Audio Sentiment Analysis by Heterogeneous Signal Features Learned from Utterance-Based Parallel Neural Network. Proceedings of the 2nd Workshop on Affective Content Analysis (AffCon 2019) co-located with Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, HI, USA, 27 January 2019. DOI: https://doi.org/10.29007/7mhj
  40. Chen F, Luo Z, Xu Y, Ke D. Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis. AAAI-2020 Workshop on Affective Content Analysis, New York, NY, USA, 7 February 2020.
  41. Luo Z, Zeng X, Bao Z, Xu M. Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019. DOI: https://doi.org/10.1109/IJCNN.2019.8851972
  42. Luo Z. Knowledge-guided Aspect-based Summarization. 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), Shanghai, China, 23–25 June 2023. DOI: https://doi.org/10.1109/CCCAI59026.2023.00012
  43. Tao G, Wang H, Shen Y, Zhai L, Liu B, Wang B, Chen W, Xing S, Chen Y, Gu HM, Qin S, Zhang DW. Surf4 Deficiency Reduces Intestinal Lipid Absorption and Secretion and Decreases Metabolism in Mice. Arterioscler Thromb Vasc Biol 2023; 43(4): 562–580. DOI: https://doi.org/10.1161/ATVBAHA.123.318980
  44. Shen Y, Gu HM, Qin S, Zhang DW. Surf4, Cargo Trafficking, Lipid Metabolism, and Therapeutic Implications. J Mol Cell Biol. 2022; 14(9): mjac063. DOI: https://doi.org/10.1093/jmcb/mjac063
  45. Wang M, Alabi A, Gu HM, Gill G, Zhang Z, Jarad S, Xia X, Shen Y, Wang G, Zhang DW. Identification of the Amino Acid Residues in the MT-Loop of MT1-MMP Critical for Its Ability to Cleave Low-Density Lipoprotein Receptor. Frontiers in Cardiovascular Medicine 2022; 9: 917238. DOI: https://doi.org/10.3389/fcvm.2022.917238
  46. Shen Y, Gu H, Zhai L, Wang B, Qin S, Zhang DW. The Role of Hepatic Surf4 in Lipoprotein Metabolism and the Development of Atherosclerosis in apoE -/- mice. Biochim Biophys Acta Mol Cell Biol Lipids 2022; 1867(10): 159196. DOI: https://doi.org/10.1016/j.bbalip.2022.159196
  47. Wang B, Shen Y, Zhai L, Xia X, Gu H-M, Wang M, Zhao Y, Chang X, Alabi A, Xing S, Deng S, Liu B, Wang G, Qin S, Zhang D-W. Atherosclerosis-Associated Hepatic Secretion of VLDL but Not PCSK9 Is Dependent on Cargo Receptor Protein Surf4. J Lipid Res 2021; 62: 100091. DOI: https://doi.org/10.1016/j.jlr.2021.100091
  48. Deng SJ, Shen Y, Gu HM, Guo S, Wu SR, Zhang DW. The Role of the C-Terminal Domain of PCSK9 and SEC24 Isoforms in PCSK9 Secretion. Biochim Biophys Acta Mol Cell Biol Lipids 2020; 1865(6): 158660. DOI: https://doi.org/10.1016/j.bbalip.2020.158660
  49. Shen Y, Wang B, Deng S, Zhai L, Gu HM, Alabi A, Xia X, Zhao Y, Chang X, Qin S, Zhang DW. Surf4 Regulates Expression of Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) but Is Not Required for PCSK9 Secretion in Cultured Human Hepatocytes. Biochim Biophys Acta Mol Cell Biol Lipids 2019; 1865(2): 158555. DOI: https://doi.org/10.1016/j.bbalip.2019.158555
  50. Dai W, Marieh F, Hamed GT, Hossein M, Loke KF. Application of Six Neural Network-Based Solutions on Bearing Capacity of Shallow Footing on Double-Layer Soils. Steel and Composite Structures 2023; 49(2): 231–244.
  51. Zhao Y, Dai W, Wang Z, Ragab AE. Application of Computer Simulation to Model Transient Vibration Responses of GPLs Reinforced Doubly Curved Concrete Panel Under Instantaneous Heating. Materials Today Communications 2023; 39: 107949. DOI: https://doi.org/10.1016/j.mtcomm.2023.107949

Supporting Agencies

  1. Funding: Not applicable.