钻井自动化

物理模型与机器学习算法的融合提升了自动滑动性能

本文提出了一种多方面的方法，利用精确的钻机控制、物理模型和机器学习技术，以可扩展的方式为滑移提供持续的高性能。

极坐标热图表示一系列幻灯片的冯·米塞斯概率密度，黑线表示估计的伪值。每个环对应一张幻灯片。左侧显示单个幻灯片的估计值，右侧显示滤波后的估计值。
极坐标热图表示一系列幻灯片的冯·米塞斯概率密度，黑线表示估计的伪值。每个环对应一张幻灯片。左侧显示单个幻灯片的估计值，右侧显示滤波后的估计值。
来源：SPE 227896。

自动滑移技术在钻井行业已存在多年。在早期应用中，即使以牺牲钻井性能为代价，只要能够以近乎100%的自动化程度满足导向要求，就被视为一项技术上的成功。如今，人们期望算法能够达到甚至超越一流钻井人员设定的性能标准。一种结合了精确钻机控制、物理模型和机器学习技术的多方面方法，旨在以可扩展的方式提供持续的高性能。

介绍

一旦刀具通过旋转工作台，钻孔过程只有两个方面会对结果产生有意义的影响：钻速 (ROP) 和刀具面控制。

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原文链接/JPT
Drilling automation

Integration of Physics Models and ML Algorithms Enhances Automated Sliding Performance

This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.

Polar coordinate heat maps representing the Von Mises probability densityfor a series of slides, with black lines denoting estimated α. Each ring corresponds to aslide. The left shows individual slide estimates, and the right shows the filtered estimates
Polar coordinate heat maps representing the Von Mises probability densityfor a series of slides, with black lines denoting estimated α. Each ring corresponds to aslide. The left shows individual slide estimates, and the right shows the filtered estimates.
Source: SPE 227896.

Automated sliding has existed in the industry for years. In early deployments, simply meeting steering requirements with nearly 100% automation, even at the cost of drilling performance, was considered a technical success. Now, algorithms are expected to meet or exceed performance standards set by the best drillers. A multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques aims to deliver consistent high-level performance in a scalable manner.

Introduction

Once tools run through the rotary table, only two aspects of the drilling process have a meaningful effect on the outcome: rate of penetration (ROP) and tool-face control.

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