Reamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology.

Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning.

Separate ROP models are trained for reamer-controlled and bit-controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit-reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well.

The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.

扩孔器是墨西哥湾深水钻井（GOM）不可或缺的部分，其性能显着影响了油井建设的经济性。本文提出了一种新颖的程序化方法来为铰刀建模渗透率（ROP）并提高钻井效率。三种现场实施方法证明了铰刀钻削优化（RDO）方法的附加值。

在用户界面面板的推动下，RDO工作流程包括地面和井下钻井数据过滤和可视化，岩层边界检测，摩擦扭矩（FTRQ）和侵略性估算，具有分析方程式和机械学习（ML）算法的ROP建模[回归，随机森林，支持向量机（SVM）和神经网络]，以及钻井参数的优化。ROP模型系数以及钻头和扩孔钻的侵略性取决于岩性，并根据偏移井数据计算得出。随后，当计划附近的一口井时，将根据钻探性能以及切削结构之间的重量和扭矩分布来评估井底组件（BHA）设计，以避免早期铰刀磨损和功能失调。

分别针对铰刀控制和钻头控制的ROP区域训练了单独的ROP模型，这些区域由在每个岩层中施加在铰刀上的钻头表面重量（WOB）的比例定义。这种新颖的概念使ROP预测能够针对每个井段采用合适的模型，具体取决于哪种切割结构限制了钻井速度。在具有来自深水GOM井的现场数据的三个RDO应用程序的第一个中，通过分析均匀盐段中的铰刀重量载荷来确定最佳的钻头铰刀距离。接下来，在与井设计对比的过程中，使用了在BHA中添加铰刀或从铰刀中去除铰刀的ROP模型，可以想象地减轻了1600万美元的套管库存盈余。最后，研究主动优化约束以揭示钻井性能限制因素，

提议的创新工作流程和方法适用于任何钻井优化方案。通过提供有关不同切削结构尺寸如何影响ROP行为的见解，并最终帮助选择合适的钻头和铰刀直径以及最佳操作参数，它们使对钻探性能优化感兴趣的执业工程师受益。