System Dynamics and Control of Deep Drilling Systems

在石油和天然气开采和利用地热能等各种行业中，获取地下深处的资源仍然是一个挑战。对于石油和天然气资源，容易获得的储油层已经在开采，新的目标处于更具挑战性的环境和地层中。在历史上，为接近储层而钻出的井具有垂直或相切的简单轨迹，但现代井越来越多地具有复杂的三维轨迹，具有高度曲折的或曲折的井径。为了提高钻井效率，减少钻柱部件的失效，减少钻井次数，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力学是至关重要的。 钻井过程需要将扭矩和轴向力从地面的钻机沿着一根细长(直径7-15厘米)的钻柱传递到钻头，该钻柱位于蜿蜒曲折的井眼内。井下传感通常仅限于近位传感器，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对于控制钻头-岩石相互作用和钻柱动力学是非常低效的。为了实现全闭环，钻井过程的自动控制、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是必不可少的。 现有的模型试图量化井斜和弯曲对钻柱静态行为的影响；然而，直到现在才完全测量、理解和量化了系统的动态行为。钻柱建模已有六十年的历史，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。该研究计划旨在开发一系列前馈或模型预测控制策略，方法是建立一套钻柱系统动态行为的降阶模型，用实验室和现场记录的高质量校准、精确和连续的数据进行验证，并通过机器学习技术实现模型参数的在线拟合。 这些准确的、经过验证的、计算高效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。开发的控制系统将通过减轻耦合振动的不利影响、改善钻井性能和改善井筒质量，进一步提高钻井作业的效率和安全性，并减少作业的碳足迹。

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.