STTR Phase I: A Reinforcement Learning-Based Automated Self-Guiding Drilling Tool

STTR第一阶段：基于强化学习的自动化自引导钻井工具

• 批准号: 2108048
• 负责人: Enrique Zarate Losoya
• 金额: $ 25.55万
• 依托单位: TEALE ENGINEERING LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108048&HistoricalAwards=false
• 关键词: STTR; Phase; Reinforcement; Learning; Based

The broader impact/commercial potential of this Small Business Technology Transfer Program Phase I (STTR) project is to provide an environmentally friendly, sustainable, and cost-effective access to energy by improving information flow during drilling processes. The US geothermal energy industry is expected to grow by 48% to $6.8 billion by 2026 and will benefit from the significant savings of drilling activities, which currently constitute 40-60% of the costs of the entire geothermal plant and field development. In addition, oil and gas (O&G) drilling operations will benefit from faster and cleaner operations. This STTR Phase I project addresses the inability to transmit large amounts of data from downhole to the surface by performing most of the processing at the drill-bit. The proposed state-of-the-art drilling tool incorporates advanced processing power and intelligence downhole. It is built on physics-based machine learning algorithms centered on Reinforcement Learning (RL) techniques, used previously in complex problems such as autonomous vehicles and lunar landing. The system will model the highly complex drilling environment as a Markov Decision Process, then trained on supercomputing facilities and validated in a laboratory-scale drilling setup using off-the-shelf electronics. This project will lead to an automated sequential decision-making process in drilling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业技术转让计划第一阶段（STTR）项目的更广泛的影响/商业潜力是通过改善钻井过程中的信息流来提供环境友好，可持续和具有成本效益的能源。 到2026年，美国地热能行业预计将增长48%，达到68亿美元，并将受益于钻井活动的显著节省，目前占整个地热发电厂和油田开发成本的40-60%。 此外，石油和天然气（O G）钻井作业将受益于更快和更清洁的操作。该STTR第一阶段项目解决了无法通过在钻头处执行大部分处理来将大量数据从井下传输到地面的问题。拟议的最先进的钻井工具融合了先进的处理能力和井下智能。 它建立在以强化学习（RL）技术为中心的基于物理的机器学习算法之上，以前用于自动驾驶汽车和月球着陆等复杂问题。该系统将高度复杂的钻井环境建模为马尔可夫决策过程，然后在超级计算设施上进行训练，并使用现成的电子设备在实验室规模的钻井装置中进行验证。该项目将导致一个自动化的顺序决策过程中的钻井。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。