System Dynamics and Control of Deep Drilling Systems

获取地下深处的资源仍然是许多行业面临的挑战，包括石油和天然气开采以及地热能源的利用。对于油气资源来说，容易开采的储层已经被开发出来，而新的目标则处于更具挑战性的环境和地层中。从历史上看，进入储层的井的轨迹很简单，要么是垂直的，要么是切线的，但现代井越来越多地具有复杂的三维轨迹，具有高弯曲度，或者是弯曲的井道。为了提高钻井效率，减少钻柱部件的故障，并通过减少钻井次数来最大限度地减少钻井过程对环境的影响，在钻井过程中控制钻柱的动态至关重要。钻井过程需要将扭矩和轴向力从地面的钻机传递到钻头上，沿着一条细长的（直径7-15cm）、长达数公里的钻柱，钻柱位于蜿蜒曲折的井道中。井下传感通常仅限于近钻头传感器，但低带宽（~10比特/秒）和高延迟（长达30秒）意味着传统的闭环反馈控制在控制钻头-岩石相互作用和钻柱动力学方面效率非常低。为了实现钻井过程的全闭环自动化控制，需要可靠的实时、基于物理的系统动力学模型和有效的在线参数拟合。现有的模型试图量化井眼倾角和弯曲度对钻柱静态行为的影响；然而，系统的动态行为直到现在才被充分测量、理解和量化。钻柱建模可以追溯到60年前，人们一直在努力建立全面的钻柱模型，但这些模型计算复杂，不适合实时优化或控制。拟议的研究计划旨在通过开发钻柱系统动态行为的一组降阶模型来开发一系列前馈或模型预测控制策略，使用实验室和现场记录的高质量校准、精确和连续的数据对其进行验证，并通过机器学习技术实现模型参数的在线拟合。这些精确的、经过验证的、计算效率高的钻井系统模型的开发将改进控制系统和设备设计，并将推动钻井过程效率的整体提高。开发的控制系统将进一步提高钻井作业的效率和安全性，并通过减轻耦合振动的不利影响、改善钻井性能和改善井眼质量来减少作业的碳足迹。

Accessing resources deep underground remains a challenge across a variety of industries, including oil and gas extraction and harnessing geothermal energy. For oil and gas resources, easy to access reservoirs are already being exploited and newer targets are in ever more challenging environments and formations. Historically, wells drilled to access reservoirs had simple trajectories - either vertical or tangent - but modern wells increasingly have complex three-dimensional trajectories with high tortuosity, or snaking of the wellpath. To improve drilling efficiency, reduce failures of drillstring components and to minimize the impact of the drilling process to the environment by reducing drilling times, it is paramount to control the dynamics of the drillstring during the well drilling process. The drilling process entails transmission of torque and axial force from the drilling rig at the surface to the drillbit along a thin (7-15cm in diameter), kilometers long drillstring that lies inside a snaking and tortuous wellpath. Downhole sensing is typically limited to near bit sensors, but low bandwidth (~10 bits / second) and high latency (up to 30 seconds) means that traditional closed loop feedback control is highly inefficient for control of bit-rock interaction and drillstring dynamics. To achieve fully closed loop, automated control of the drilling process, reliable real-time, physics-based models of the system dynamics and effective online parameter fitting are necessary. Models exist that seek to quantify the effects of borehole inclination and tortuosity on the static behavior of the drillstring; however, the dynamic behavior of the system is only now being fully measured, understood, and quantified. Drillstring modelling has stretched back sixty years, and there have been significant efforts to create comprehensive drillstring models, but these models are computationally complex and are unsuited to real-time optimization or control. The proposed research program seeks to develop a series of feedforward or model predictive control strategies by developing a set of reduced order models of the dynamic behavior of the drillstring system, validating them with high quality - calibrated, precise and continuous - data recorded in the laboratory and the field, and implementing online fitting of model parameters through machine learning techniques. The development of these accurate, validated and computationally efficient models of the drilling system will improve control systems and equipment design and will drive an overall increase in the efficiency of the drilling process. The control systems developed will further increase the efficiency and safety of drilling operations and reduce the carbon footprint of operations by mitigating the adverse effects of coupled vibrations, improving drilling performance and improving wellbore quality.