How Big Data Analytics Can Help Future Regulatory Issues in Carbon Capture and Sequestration CCS Projects
- Karthik Balaji (University of North Dakota) | Zifu Zhou (University of North Dakota) | Minou Rabiei (University of North Dakota)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 April, San Jose, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Artificial Intelligence, Machine Learning, Regulations & Policy, Algorithmic Regulations, Carbon Capture and Sequestration
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- 122 since 2007
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In this age of data, there is a significant need for tracking and prediction of non-compliance of rules & regulations in various industries including the oil & gas sector. In this paper, we will be reviewing some of the anticipated regulatory issues in commercial implementations of carbon capture & sequestration (CCS) and discuss how machine learning and big data analytics can diminish future non-compliance incidents.
With the rising awareness of advanced data-driven technologies such as "Big Data Analytics" and "Machine Learning", a contemporary approach to regulation and compliance is developing. This emerging approach, called "Algorithmic Regulation", defines an alternative framework for systematic collection of data (real-time or near real-time) and continuous generation of knowledge through intelligent computational algorithms in order to regulate a domain of activities.
In this study, we will look at some of the major data management challenges in the pilot CCS operations with regards to rules and regulations. We will then discuss how an algorithmic regulatory framework can help in conducting CCS operations in a manner that are compliant with environmental, safety and health policies and regulations.
Field operators collect a lot of data which needs to be formatted and modelled in a fashion acceptable to understand the operator's compliance with regulation. Generally, such compliance qualification criteria are verified using human intellect and basic querying software. In other industries, the idea of converting rules & regulations in a format understandable by machines is gaining momentum and great strides have been taken. The technological progresses made possible by data-driven analytical techniques can create a paradigm shift in the way rules and regulations are designed and implemented. Large-scale deployment of CCS projects is bound to bring with it a number of regulatory issues, making it a necessity to proactively explore and address the anticipated issues. These technologies can equip regulated entities as well as regulators with advanced tools for managing complexity in CCS projects. These improved solutions will help companies to better meet the regulatory data collection, reporting and governance requirements in large scale CCS operations.
This paper looks into advanced data management and modeling techniques like "algorithmic regulations" to increase compliance in carbon capture and sequestration projects. The concept of handling rules and regulations in the form of big data will change the outlook and compliance management will become increasingly more agile and responsive.
|File Size||620 KB||Number of Pages||9|