Project Description

Project Title: Assessing Current and Future Infrastructure Hazards
Prime Performer: National Energy Technology Laboratory
Project Duration: 2018-2020

Overview

This research evaluates infrastructure to assess the current state of offshore infrastructure and identify technologies that can be used to extend infrastructure life as well as reduce hazards and maintenance costs.

More than 120,000 wells have been drilled in U.S. waters since the 1890s. These wells connect to a complex network of infrastructure that includes pipelines, platforms, subsea installations, ports, and terminals. The aging of this infrastructure, coupled with varying maintenance and monitoring strategies and extreme operational conditions, can impact the rate and severity of failure. As a result, industry is looking for technologies, analyses, and tools to help extend the life of the existing infrastructure and guide development of future installations to meet a life span of more than 50 years. This project utilizes data, big data computing, and advanced analytics to evaluate the condition of the current infrastructure, assess potential infrastructure hazards, and optimize the development and deployment of existing and new infrastructure technologies in the offshore environment. The data and analytical results produced from this task will offer additional information to industry and regulators regarding the state of the existing offshore infrastructure and will provide new insights that can enhance or establish new practices, policies, and regulations to improve safety, minimize equipment failure, reduce costs, and mitigate potential hazards.

Approach

This three-year project uses data and models from NETL’s offshore risk modeling (ORM) platform to perform advanced analyses on data pertinent to existing infrastructure. The findings aim to provide new analytical insight regarding the current integrity, hazard trends, and methods to extend the life of existing infrastructure using various mitigation and maintenance methods without significant reinvestment or shut-in.

In the first year, work centered on developing a robust database that incorporates information on the location, use, design, and condition of existing offshore infrastructure as well as trends in environmental conditions and experimental results detailing optimal operational conditions for current and new technologies. These data, along with data and tools available from the ORM platform, were used to assess the current state of existing offshore infrastructure. In addition, an advanced analytical framework was designed to support the assessment of potential infrastructure hazards and help evaluate the optimal development and use of existing and new infrastructure technologies.

In the second year, the project has continued to collect, integrate, and assess additional infrastructure, environmental, and technology information — expanding the effort to include data for all U.S. offshore waters. Novel methods and analytics documentation will begin, and the public version of online analytical platform for infrastructure analyses will be released.

In the third year, the project will continue to collect, integrate, and publish additional infrastructure, environmental, and technology information. Workflow to evaluate the state of current infrastructure and the public version of the online analytical platform for rapid offshore infrastructure analyses will be updated and released.

Expected Outcome

This project will leverage big data, spatio-temporal tools from the Offshore Phase 1 ORM platform and couple them with new data and algorithms to support intelligent analytics via an interactive, online platform to help identify and address critical hazards related to offshore infrastructure. It is also anticipated that analytical findings from this effort will offer crucial insights to reduce infrastructure hazards, costs, and extend infrastructure life for DOE, industry, regulators, and other stakeholders.

Novel, intelligent spatial analytics and models that leverage techniques from natural language processing, machine learning, and big data computing in a scientific, data-drive framework are an expected outcome of this work. Anticipated analytical outputs will offer new insights on the current state of offshore infrastructure and potential hazards, as well as support predictive analytics for optimized offshore infrastructure development.

An interactive online platform, offering access to key data and analytical tools from this project is another expected outcome of this work. This will allow DOE, industry, regulators, and other stakeholders to interact with the data and analytics to better inform decisions, such as those related to reducing infrastructure hazards, costs, and extend infrastructure life for current and alternative uses.

Research Products

When this project is completed it will result in the following products:

  • An interactive online platform that will offer access to the data and analytics developed for this project. This platform will allow users to identify and assess critical hazards related to offshore infrastructure and help inform decisions to reduce potential hazards, costs, and extend infrastructure life for DOE, industry, regulators, and other stakeholders.
  • Key findings from analytics summarized in various technical formats.

Explore research products that are related to this project.

*Image Source: NETL

Number of platform incidents across the Gulf of Mexico, between 2006–2017, by water depth (data from BOEM, BSEE).

*Image Source: NETL

Maximum velocity magnitude (range 0 to 2.75 m/s) recorded for each month (each column is roughly a season) from twelve years (2003-2014) of sea-surface velocity, presented as a function of longitude and latitude.

Contacts

Jennifer Bauer
Principal Investigator

Kelly Rose
Co-Principal Investigator
Offshore Portfolio Lead

Roy Long
Offshore Portfolio Technical Manager
Effective Resource Development

Grant Bromhal
Senior Fellow
Geological & Environmental Systems

Philip Reppert
Associate Director
Geological & Environmental Systems

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