Continuous Feedback Loop Between Surface and Downhole for Autonomously Drilling a Section

Author:

Liu Qiuhua1,Ba Samba1,Goel Prateek1,Kim Jinsoo1

Affiliation:

1. SLB, Houston, TX, USA

Abstract

Abstract Advanced AI planner and connected intelligent systems are used to obtain a continuous feedback workflow between surface and downhole. This significantly increases the consistency and efficiency of well construction operations while minimizing human involvement and footprint, advancing efforts towards autonomous operations, and lowering the operational carbon footprints by an order of magnitude. To send steering instructions to a rotary steerable system (RSS), one often uses modulations of flow or rotation per minute (RPM) called downlinks. The proposed workflow presented here includes an automated downlink detection and an AI planner to determine the optimum downlinking sequence for executing directional drilling decisions. The automated downlink detection is based on an intelligent system that will continuously monitor pressure and RPM waves at the surface and raise an automation event when it detects downlink patterns that match predefined patterns with a certain confidence level. Once the current steering mode is clearly identified and the geometric trajectory to follow has been generated, the next step is to define the optimum downlinking sequence to follow that trajectory path. This is done by the AI planner, which carefully analyzes the current state, the available downlinking map, and the desired state to derive the most favorable path to the desired state. The autonomous workflow has been tested in the field in many different countries across the world. The automated downlink detection located at the surface has an average detection rate of more than 98% with more than 10,000 downlinks to be detected every month across numerous field locations. On rare occasions, where the surface downlink detection is not picking up the downlink signal, the downhole downlink detection will send downlink confirmation from downhole to surface via the measurement-while-drilling telemetry system. This makes the overall system exceptionally robust. In addition, the intelligent downlink sequence optimization will often find the shortest path from the current tool state to the desired state, thus increasing the efficiency of the drilling operations. With the native cloud monitoring system, domain experts can access the system at any time from wherever they can log into the secure robust network. The synergy between surface systems and downhole systems has enabled the development of a unique autonomous well construction system that improves performance while reducing the carbon footprint, allowing domain experts from all over the world to remotely monitor their operations without any traveling needed.

Publisher

SPE

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3