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Sensing and computational frameworks for improving drill-string dynamics estimation

机译：改善钻串动态估计的传感和计算框架

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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.
机译：在本文中，我们考虑方向多截面钻柱的轴向运动。钻串动态由分布式动态模型（波方程）表示，该模型（波动方程）与井下边界（比特岩相互作用）的常微分方程耦合。钻头和岩石之间的相互作用可以在钻柱中引入严重的振动并导致安全性和性能问题。因此，钻井的性能与我们对地下的了解交织。为了解决这些问题，我们提出了一种用于估计钻弦动力学和钻井的特定内在能量的感官和计算框架。通过利用派生模型的特定结构，我们将钻串动态建模与顶部驱动器钩 - 负载（力）和钩速（速度）测量相结合，以估计力的钻头，而不需要对子的知识-表面。然后，我们记录和模拟表面附近的钻头岩相互作用的地震辐射模式（即钻孔时，地震）。这样的想法允许在钻井时产生适当的岩石内在能量的估计。我们介绍了基于直接参数估计和机器学习概念的两种替代岩石属性估计算法来完成分析。在模拟中测试并验证了不同的方法。我们讨论了各自的优势和缺点。最后，我们展示了如何在存在非线性库仑摩擦条款和耦合的轴向扭转振荡的情况下扩展我们的方法。

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