Accelerating and Visualizing Machine Learning for Reservoir Simulation

加速和可视化用于油藏模拟的机器学习

• 批准号: 543869-2019
• 负责人: Chen, Zhangxing
• 金额: $ 3.28万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688837
• 关键词: Accelerating; Visualizing; Machine; Learning; Reservoir

Artificial intelligence (AI) techniques have been identified as having great potential to provide value to the petroleum industry due to the complexities of managing, processing and interpreting vast amounts of available reservoir geophysical, geological and production data. Machine learning, in particular, is on a rapid ascent enabled by the growth of advanced computing power. In the past several years, volatility in the price of oil has driven petroleum producers and service companies to strive for higher energy efficiency, lower operational costs, and less unscheduled downtime. To achieve these objectives, many technical challenges related to the development of machine learning techniques in reservoir simulations must be addressed with a comprehensive research program. In our industrial research chair program, we are investigating many of these challenges. As artificial intelligence and machine learning techniques are used to enhance Reservoir Engineering workflows, it is important to understand how these techniques may be enhanced through the use of acceleration techniques, both traditional and emerging, to mitigate the computational challenges. This enables faster results and more rigorous simulation studies. It is also important to understand how visualization and interaction with the machine learning algorithms may help produce more accurate results and provide greater insight into complex reservoir engineering scenarios. The objective of this research project is to investigate the development of acceleration and visualization enhancements to improve and accelerate machine learning techniques applied to reservoir simulations. The outcome will advance machine learning algorithms, quantum algorithms, visualization techniques, hardware acceleration techniques, history matching, and optimization techniques used in the simulations. Techniques will be developed that directly support the enhancement of the machine learning components of our novel reservoir simulator.

人工智能（AI）技术被认为具有为石油工业提供价值的巨大潜力，因为管理、处理和解释大量可用的油藏地球物理、地质和生产数据非常复杂。特别是机器学习，由于先进计算能力的增长，它正在快速上升。在过去几年中，石油价格的波动促使石油生产商和服务公司努力提高能源效率，降低运营成本，减少计划外停机时间。为了实现这些目标，必须通过全面的研究计划来解决与油藏模拟中机器学习技术开发相关的许多技术挑战。在我们的工业研究椅计划中，我们正在调查许多这些挑战。随着人工智能和机器学习技术被用于增强油藏工程工作流程，重要的是要了解如何通过使用传统和新兴的加速技术来增强这些技术，以减轻计算挑战。这使得更快的结果和更严格的模拟研究。 同样重要的是要了解可视化和与机器学习算法的交互如何有助于产生更准确的结果，并为复杂的油藏工程场景提供更深入的见解。本研究项目的目标是研究加速和可视化增强的发展，以改进和加速应用于油藏模拟的机器学习技术。其成果将推进机器学习算法、量子算法、可视化技术、硬件加速技术、历史匹配和模拟中使用的优化技术。将开发直接支持我们的新型储层模拟器的机器学习组件的增强的技术。