Autonomous Directional Drilling Planning and Execution Using an Industry 4.0 Platform

Samba Ba, M. Ignova, K. Mantle, Adrien Chassard, Tao Yu, Sylvain Chambon, Ziad Akkaoui, Lu Jiang, Richard Harmer, Olivia Barcelata, Jinsoo Kim, Mustapha Rhazaf
{"title":"Autonomous Directional Drilling Planning and Execution Using an Industry 4.0 Platform","authors":"Samba Ba, M. Ignova, K. Mantle, Adrien Chassard, Tao Yu, Sylvain Chambon, Ziad Akkaoui, Lu Jiang, Richard Harmer, Olivia Barcelata, Jinsoo Kim, Mustapha Rhazaf","doi":"10.2118/204607-ms","DOIUrl":null,"url":null,"abstract":"\n Today, directional drilling is considered a mix between art and science only performed by experts in the field. In this paper, we present an autonomous directional drilling framework using an industry 4.0 platform that is built on intelligent planning and execution capabilities and is supported by surface and downhole automation technologies to achieve consistently performing directional drilling operations accessible for easy remote operations.\n Intelligent planning builds on standard planning activities that are needed for directional drilling applications and advances them with rich data pipelines that feed predictive and prescriptive machine-learning (ML) models; this enables more accurate BHA tendencies, operating parameters, and trajectory plans that ultimately reduce executional risk and uncertainty.\n Intelligent execution provides technologies that facilitate decision-making activities, whether they be from the wellsite or town, by leveraging the digital-drilling program that is generated from the intelligent planning activities. The program connects planning expectations, real-time execution data from the surface and downhole equipment, and generates insights from data analytics, physics-based simulations, and offset analysis to achieve consistent directional drilling performance that is transparent to all stakeholders.\n This new framework enables a self-steering BHA for directional drilling operations. The workflow involves an automated evaluation of the current bit position with respect to the initial plan, automated evaluation of the maximum dogleg capability of the BHA, and the capability to examine the health of the BHA tools and, if needed, an automated re-planning of an optimized working plan. This is accomplished on a system level with interdependencies on the different elements that make up the complete workflow.\n This new autonomous directional drilling framework will minimize operational risk and cost-per-foot drilled; maximize performance, procedural adherence, and establish consistent results across fields, rigs, and trajectories while enabling modern remote operations.","PeriodicalId":11320,"journal":{"name":"Day 3 Tue, November 30, 2021","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 3 Tue, November 30, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/204607-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Today, directional drilling is considered a mix between art and science only performed by experts in the field. In this paper, we present an autonomous directional drilling framework using an industry 4.0 platform that is built on intelligent planning and execution capabilities and is supported by surface and downhole automation technologies to achieve consistently performing directional drilling operations accessible for easy remote operations. Intelligent planning builds on standard planning activities that are needed for directional drilling applications and advances them with rich data pipelines that feed predictive and prescriptive machine-learning (ML) models; this enables more accurate BHA tendencies, operating parameters, and trajectory plans that ultimately reduce executional risk and uncertainty. Intelligent execution provides technologies that facilitate decision-making activities, whether they be from the wellsite or town, by leveraging the digital-drilling program that is generated from the intelligent planning activities. The program connects planning expectations, real-time execution data from the surface and downhole equipment, and generates insights from data analytics, physics-based simulations, and offset analysis to achieve consistent directional drilling performance that is transparent to all stakeholders. This new framework enables a self-steering BHA for directional drilling operations. The workflow involves an automated evaluation of the current bit position with respect to the initial plan, automated evaluation of the maximum dogleg capability of the BHA, and the capability to examine the health of the BHA tools and, if needed, an automated re-planning of an optimized working plan. This is accomplished on a system level with interdependencies on the different elements that make up the complete workflow. This new autonomous directional drilling framework will minimize operational risk and cost-per-foot drilled; maximize performance, procedural adherence, and establish consistent results across fields, rigs, and trajectories while enabling modern remote operations.
基于工业4.0平台的自主定向钻井规划与执行
如今,定向钻井被认为是艺术与科学的结合,只能由该领域的专家来完成。在本文中,我们提出了一个基于工业4.0平台的自主定向钻井框架,该平台建立在智能规划和执行能力的基础上,并得到地面和井下自动化技术的支持,可以实现持续执行定向钻井作业,并且易于远程操作。智能规划建立在定向钻井应用所需的标准规划活动的基础上,并通过丰富的数据管道为预测和规范的机器学习(ML)模型提供支持。这可以实现更精确的BHA趋势、操作参数和轨迹规划,最终降低执行风险和不确定性。通过利用智能规划活动生成的数字钻井程序,智能执行提供了促进决策活动的技术,无论是来自井场还是城镇。该程序将规划预期、地面和井下设备的实时执行数据联系起来,并从数据分析、基于物理的模拟和偏移量分析中得出见解,以实现对所有利益相关者透明的一致定向钻井性能。这种新框架可以实现自导向BHA,用于定向钻井作业。该工作流程包括相对于初始计划的当前钻头位置的自动评估,BHA的最大狗腿能力的自动评估,以及检查BHA工具健康状况的能力,如果需要,还可以自动重新规划优化的工作计划。这是在系统级别上完成的,它与组成完整工作流的不同元素相互依赖。这种新的自主定向钻井框架将最大限度地降低作业风险和每英尺钻井成本;在实现现代远程操作的同时,最大限度地提高了性能、程序依从性,并在油田、钻机和轨迹之间建立一致的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信