为确保安全、长期封存CO 2并降低运营风险和封存管理而对封存库进行的监测正在发生动态变化，扩大了实施创新技术和应用的机会，特别是商业规模的部署。在整篇论文中，开发了一种多层分析方法，利用先进的机器学习 (ML) 技术来处理注入期间采集的被动地震监测数据以及伊利诺伊州盆地的抽水/注入压力和速率，以了解潜在的裂缝和情况。故障分析。
这项研究是美国能源部碳储存计划资助的科学信息机器学习加速地下应用实时决策 (SMART) 计划的一部分。SMART 计划的成果是基于科学的、基于机器学习的工具，可以应用于全国和世界各地的碳储存站点，以实现以下目标：
在作者的研究中，通过整合所有可用的测量数据来可视化裂缝网络，创建了一种多层的数据驱动方法。这些数据集包括钻井、测井和岩心测试、注入和井下压力测量以及五个垂直压力监测仪的测量结果，提供了有关裂缝网络的丰富细节。
The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis.
This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives:
In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks.
