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.

这个小型企业技术转移计划I阶段（STTR）项目的更广泛的影响/商业潜力是，通过在钻井过程中改善信息流来提供环保，可持续和具有成本效益的能源访问。 预计到2026年，美国地热能行业预计将增长48％，至68亿美元，并将受益于钻井活动的大量节省，目前占整个地热工厂和现场开发成本的40-60％。 此外，石油和天然气（O＆G）钻井操作将受益于更快，更清洁的操作。该STTR I期项目无法通过在钻头上执行大部分处理来将大量数据从井下传输到表面。拟议的最先进的钻井工具结合了高级处理能力和智能下井。 它建立在基于物理的机器学习算法上，以增强性学习为中心（RL）技术，以前用于复杂问题，例如自动驾驶汽车和月球着陆。该系统将将高度复杂的钻井环境建模为马尔可夫决策过程，然后对超级计算设施进行培训，并在实验室规模的钻井设置中使用现成的电子设备进行了验证。该项目将导致钻井中的自动连续决策过程。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。