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Inversion method for multi-section friction factors in horizontal wells based on Fisher optimal segmentation and SSA
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Hu Yina, Qianyi Xiaa, *, Qiang Weib, Xiuwen Zhaoa, Qian Lia
Petroleum Research | 2026, 11(3) : 1021 - 1036
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Petroleum Research | 2026, 11(3): 1021-1036
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Inversion method for multi-section friction factors in horizontal wells based on Fisher optimal segmentation and SSA
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Hu Yina, Qianyi Xiaa, *, Qiang Weib, Xiuwen Zhaoa, Qian Lia
Affiliations
  • aPetroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, China
  • bChuanxi Drilling Company, CNPC Chuanqing Drilling Engineering Company Limited, Chengdu, 610051, China
Published: 2026-09-10 doi: 10.1016/j.ptlrs.2026.01.008
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The friction factor plays a critical role in drag and torque calculations. In the drag and torque model, the friction factor is typically treated as an average value, or the wellbore is roughly divided into casedhole and openhole segments with different values assigned. Such simplified model parameters fail to account for variations in the friction factor across different well segments, impacting the accuracy of the computations. Precise drag and torque calculations are significant, and the prerequisite for accurate drag prediction is the inverse analysis of realistic friction factor parameters. This study proposes a friction factor inverse analysis method based on morphological characteristics of measured data. The Fisher optimal segmentation method is employed to divide the field-measured data into multiple reasonable subsegments based on curve morphology features, indirectly determining the segmentation of the friction factor. The 3D stiff-string drag and torque model is used to establish friction factors inverse model, with corresponding friction factors introduced separately for the segmented well sections. The sparrow search algorithm, enhanced by opposition-based learning strategy, is utilized to optimize the segmented friction factors, with the optimization evaluation criteria simultaneously assessing the deviation and similarity between simulated and actual curves. Case studies demonstrate that the friction factor inverse analysis method proposed in this study, leveraging the curve morphology features of realtime drilling data, can efficiently obtain high-precision segmented friction coefficients. This method significantly reduces the error between simulated and field-measured data, improving calculation accuracy by about 50%, thus providing robust data support for accurate drag and torque prediction. Furthermore, the division approach can be applied during a round trip to optimize drilling parameters, enhancing the efficiency of drilling parameter control.

Friction factor  /  Drag and torque  /  Inverse analysis  /  Fisher optimal segmentation  /  Sparrow search algorithm
Hu Yin, Qianyi Xia, Qiang Wei, Xiuwen Zhao, Qian Li. Inversion method for multi-section friction factors in horizontal wells based on Fisher optimal segmentation and SSA[J]. Petroleum Research, 2026 , 11 (3) : 1021 -1036 . DOI: 10.1016/j.ptlrs.2026.01.008
  • National Science and Technology Major Project(2025ZD1405900)
  • Natural Science Foundation of Sichuan, China(2024NSFSC0205)
  • National Key R&D Program of China(2019YFA0708302)
Year 2026 volume 11 Issue 3
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Article Info
doi: 10.1016/j.ptlrs.2026.01.008
  • Receive Date:2025-08-20
  • Online Date:2026-10-14
  • Published:2026-09-10
Article Data
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History
  • Received:2025-08-20
  • Revised:2026-01-13
  • Accepted:2026-01-15
Funding
National Science and Technology Major Project(2025ZD1405900)
Natural Science Foundation of Sichuan, China(2024NSFSC0205)
National Key R&D Program of China(2019YFA0708302)
Affiliations
    aPetroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, China
    bChuanxi Drilling Company, CNPC Chuanqing Drilling Engineering Company Limited, Chengdu, 610051, China

Corresponding:

* E-mail address: (Q. Xia).
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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