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Sensing and computational frameworks for improving drill-string dynamics estimation
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DOI：10.1016/j.ymssp.2021.107836.png)
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摘要
En 中文
In this paper, we consider the axial motion of a directional multi-sectional drill-string. The drill-string dynamics are represented by a distributed dynamical model (wave equations) coupled with an ordinary differential equation at the downhole boundary (bit-rock interaction). The interaction between the drill-bit and rock can introduce severe vibrations in the drill-string and result in safety and performance issues. Consequently, the performance of drilling is interwoven with our knowledge of the subsurface. To address these problems, we propose a sensing and computational framework for estimating the drill-string dynamics and the specific intrinsic energy of the rocks while drilling. By exploiting the derived models' particular structure, we combine the drill-string dynamics modeling with top drive hook-load (force) and hook-speed (velocity) measurements to estimate the force on-bit without requiring the knowledge of the sub-surface. Then, we record and model the seismic radiation patterns of drill-bit rock interactions near the surface (i.e., seismic while drilling). Such an idea allows deriving an appropriate estimation of the rocks' intrinsic energy while drilling. We introduce two alternative rock property estimation algorithms based on direct parameter estimation and machine learning concepts to complete the analysis. The different approaches are tested and validated in simulations. We discuss their respective advantages and drawbacks. Finally, we show how to extend our methodologies in the presence of non-linear Coulomb friction terms and of coupled axial-torsional oscillations. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Dynamical system
Distributed system
Backstepping
Seismic-While-Drilling
Parameter estimation
Neural Networks
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期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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