Scipediacontent: Scipediacontent moved page Review 135452346177 to Li et al 2026c

2026-05-18T08:47:22Z

Scipediacontent moved page Review 135452346177 to Li et al 2026c

Scipediacontent: Scipediacontent moved page Draft content 454120210 to Review 135452346177

2026-05-18T08:26:21Z

Scipediacontent moved page Draft content 454120210 to Review 135452346177

Scipediacontent: Created page with " == Abstract ==
Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration...."

2026-05-18T08:26:16Z

Created page with " == Abstract == <p>Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration...."

New page

== Abstract ==

<p>Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration. MEMS-based micro-measurers can collect fullwellbore temperature data by flowing with drilling fluid circulation, but converting their time-series records to well depth relies on idealized steady-motion assumptions that neglect wall collisions, sticking, and local flow disturbances, introducing systematic positioning errors. In this study, a deep learning-based time-depth conversion method is proposed. A gated recurrent unit (GRU)-based temporal neural network is developed to extract motion-state features from six-axis dynamic signals, and a bounded velocity correction mechanism is introduced to compensate deviations from the idealized terminal velocity. The results show that: (1) The proposed model effectively learns motion-state deviations from dynamic response data and provides stable velocity correction under complex downhole conditions. (2) The corrected time-velocity curve exhibits transient fluctuations relative to the idealized terminal velocity, accurately capturing non-ideal behaviors while preserving directional stability. (3) During model training, the loss function decreases progressively and stabilizes, indicating good convergence and training stability. (4) Comparative analysis shows that the proposed GRU-Full method achieves a mean absolute anchor-point error of 1.23 m (0.24% of the well depth), outperforming the MLP and LSTM alternatives. Ablation experiments confirm that both the physical constraints and regularization terms contribute substantially to the model accuracy. This study enhances the spatial mapping accuracy of temperature data acquired by micro-measurers under complex downhole dynamic conditions. The established physically constrained deep learning framework provides a new technical pathway for refined wellbore thermal-field characterization and intelligent drilling decision-making.</p>

== Document ==
<pdf>Media:Draft_content_454120210-2586-document.pdf</pdf>

← Older revision	Revision as of 08:47, 18 May 2026
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Li et al 2026c - Revision history

Scipediacontent: Scipediacontent moved page Review 135452346177 to Li et al 2026c

Scipediacontent: Scipediacontent moved page Draft content 454120210 to Review 135452346177

Scipediacontent: Created page with " == Abstract == Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration...."

Scipediacontent: Created page with " == Abstract ==
Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration...."