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Article type: Research Article
Authors: Xu, Lina | Huang, Yueb | Cheng, Xian-Huac | Xu, Ming-Biaob; * | Xu, Jied | You, Fu-Change
Affiliations: [a] College of Petrochemical and Energetic Engineering, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China | [b] Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University, Wuhan 430100, Hubei, China | [c] CNPC Logging, Xinjiang Branch, Karamay 834000, Xinjiang, China | [d] Institute of Exploration Techniques, CAGS, Langfang 065000, Hebei, China | [e] Jiahua Technology Co., Ltd, Jingzhou 434000, Hubei, China
Correspondence: [*] Corresponding author: Ming-Biao Xu, Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University, University Road, Caidian Distinct, Wuhan 430100, Hubei, China. Tel.: +86 7168060863; E-mail: [email protected].
Abstract: An accurate HTHP rheological model is essential for safe and economical deep drilling. In this work, the water-based fluid applied in Southwest China Gas Fields is selected as a typical example to systematically explore rheological modeling at elevated temperature(up to 180∘C) and pressure(up to 100 MPa). The HTHP rheological behavior of water-based drilling fluid was firstly investigated. Then general rheological modeling approaches and specific HTHP modeling process have been employed to establish an accurate HTHP rheological model. The results showed that for general rheological models, the Bingham model appears to be more suitable to predict the transient rheology at constant conditions; under HTHP conditions, a dynamic rheological model has been established using the relative dial reading method modified by the direct fitting, whose prediction deviation was in the range of -7.13%∼10.63%, meeting in-field application requirements well. This research will be useful to direct a comprehensive rheological treatment, and will also enrich the modeling strategy of HTHP rheology for drilling fluids.
Keywords: HTHP, dynamic rheological models, water-based drilling fluid, deviation
DOI: 10.3233/JCM-180831
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 18, no. 3, pp. 801-817, 2018
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