Analysis of flowback data using artificial intelligence for regulating and optimizing hydraulic fracturing operations

使用人工智能分析返排数据以调节和优化水力压裂作业

• 批准号: 543998-2019
• 负责人: Dehghanpour, Hassan
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 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=680269
• 关键词: Analysis; flowback; data; using; artificial

Canada's rapidly declining conventional oil and gas resources has been a stimulus for the development of itsunconventional resources. This development has become possible using horizontal drilling and multi-stage hydraulic fracturing. There has been an increase in hydraulic fracturing operations during the last decade. Hence, it is important to optimize these operations and minimize their potential environmental impacts. Recently, the use of artificial intelligence (AI) to address the challenges associated with flowback data analysis for fracturing optimization is gaining attention. One key advantage of the AI technique over existing methods is that it can handle larger quantities of noisy data without necessarily requiring a prior assumption of the physics of fluid flow. Therefore, they can handle incomplete input data, which is often a challenge in flowback data analysis. Therefore, this project's objective is to optimize fracturing operations in Canadian tight reservoirs. It will use advanced AI techniques which will be developed by the University of Alberta team to analyze the large completion, fracturing and flowback data sets that will be provided by geoLOGIC Systems Ltd. The resulting AI software will benefit the industry and government regulators by 1) maximizing efficiency and profitability of fracturing operations using optimal fracturing and completion designs, 2) minimizing fresh water consumption and reducing environmental impacts of fracturing operations, 3) preventing uncontrolled creation of inefficient fractures and possible groundwater contamination, 4) assessing public health and environmental risks related to groundwater contamination by fracturing fluid, and 5) improving the knowledge database for government agencies to update fracturing-related regulations in Canada.

加拿大常规油气资源的迅速减少刺激了其非常规资源的开发。通过水平钻井和多级水力压裂，这一发展成为可能。在过去十年中，水力压裂作业有所增加。因此，优化这些运营并尽量减少其潜在的环境影响非常重要。最近，使用人工智能（AI）来解决压裂优化返排数据分析相关的挑战越来越受到关注。与现有方法相比，人工智能技术的一个关键优势是它可以处理大量噪声数据，而无需事先假设流体流动的物理原理。因此，它们可以处理不完整的输入数据，这通常是回流数据分析中的挑战。因此，该项目的目标是优化加拿大致密油藏的压裂作业。它将使用阿尔伯塔大学团队开发的先进人工智能技术来分析由geoLOGIC Systems Ltd提供的大型完井、压裂和返排数据集。由此产生的人工智能软件将使行业和政府监管机构受益：1）使用最佳压裂和完井设计最大限度地提高压裂作业的效率和盈利能力，2）最大限度地减少淡水消耗并减少压裂对环境的影响 操作，3）防止低效裂缝的失控形成和可能的地下水污染，4）评估与压裂液污染地下水相关的公共卫生和环境风险，以及5）改进政府机构的知识数据库，以更新加拿大压裂相关法规。