The Ocean Energy Safety Institute (OESI) is pleased to announce it will fund 14 proposals for innovation and technology development to improve the safety and environmental sustainability of offshore energy development. The OESI received 91 project proposals across the Marine, Oil & Gas, and Wind Energy sectors in response to the OESI Request for Proposals (RFP) Cycle II. Total funding for the projects selected will reach $5,485,945 pending successful contract negotiations and funds transfer from our sponsors.
Organized under an agreement between the Bureau of Safety and Environmental Enforcement (BSEE), Department of Energy (DOE), and Texas A&M Engineering Experiment Station (TEES), the OESI is a consortium of industry, national labs, non-governmental organizations (NGOs), and academia created to support the development of critical safety and environmental improvements for all offshore energy activities, including renewable and traditional energy.
Funding will be awarded to the following projects and lead organizations. Project synopses are also provided below.
Marine Energy Projects
| Marine Energy Project Title | Lead Organization | Requested Amount |
|---|---|---|
| M-1.1 Mapping use cases and connecting small-scale marine energy solutions to enhance safety, security and sustainability of offshore oil and gas operations | ||
| Integrating Wave Energy Convertor with Subsea Energy Storage Device: A Feasibility Study and Demonstration Case | American Bureau of Shipping | $479,362 |
| M-1.3 Identification of recommended minimum system and personnel safety considerations and potential standards for design of marine energy technology | ||
| Comprehensive marine energy safety framework with extensive workforce development and stakeholder engagement | American Bureau of Shipping | $377,719 |
| M-1.4 Assessment of lifting operational safety experience and training of best practices for marine energy technology fabrication, installation, operations and maintenance | ||
| Innovative Offshore Lifting Training: Integrating Mixed Reality and Machine Learning for Enhanced Safety and Skill | Argonne National Lab | $295,000 |
| M-1.5 Development of real-time monitoring tools and data dashboards for operational performance and integrity of marine energy technology | ||
| Real-Time Monitoring and Visualization System for Marine Energy Devices | Florida Atlantic University | $321,618 |
| TOTAL | $1,473,699 |
|
Oil & Gas Projects
| Oil & Gas Project Title | Lead Organization | Requested Amount |
|---|---|---|
| OG-1.5 Develop a widely applicable, low-cost, real-time appropriate approach for early kick detection (EKD) and well control over the life of the well | ||
| Low-Cost Early-Kick-Detection Method via While-Drilling-Measurement Tools | Louisiana State University | $290,629 |
| OG-3.1 Develop methods to monitor asset health and assess life extension using in-situ inspection data | ||
| A Novel Approach to Asset Life Extension based on Asset Health and Risk Models | ABL Group | $394,440 |
| OG-4.1 Automated remote inspection techniques to produce desired risk-based safety and integrity management outcomes | ||
| Integration of Percussion with Robotics for Offshore Bolted Structure Inspection | University of Houston | $400,000 |
| OG-5.1 Improve mechanical integrity performance of offshore lifting equipment | ||
| Enhancing Safety of Offshore Lifting Equipment: A Predictive Monitoring with mmWave Radar NDT, Robot and Digital Twin Modelling | Texas A&M Engineering Experiment Station | $405,280 |
| OG-5.4 Analysis of past fire incidents in offshore facility to identify and locate trends and develop protocol to reduce offshore fire incidents | ||
| Machine learning-driven analysis of fire incidents in offshore facilities: identifying trends and developing mitigation protocols | American Bureau of Shipping | $490,385 |
| TOTAL | $1,980,734 |
|
Wind Energy Projects
| Wind Energy Project Title | Lead Organization | Requested Amount |
|---|---|---|
| W-1.4 Modeling, measurement and forecasting of dynamic loading (ice, wind, wave, seismic, scour, and rain) on turbine systems, components and their connection to the grid | ||
| Health monitoring of offshore wind turbines using physics-based digital twin solutions | University of Massachusetts Amherst | $304,951 |
| Development of a predictive tool for fixed wind foundation integrity assessment under multi-hazard dynamic loading conditions | American Bureau of Shipping | $450,030 |
| Safe operations of offshore wind turbine blades through accurate modeling and measurement of dynamic wind and rain loading | Predyct, Inc. | $399,661 |
| Integrated Models for Predictive Modeling of Dynamic Wind and Wave Loads on Offshore Wind Turbines | Louisiana State University | $376,870 |
| Safety Additions to FAST for Extreme Weather, Icing, and Nonlinear Dynamics (SAFE WIND) | Texas A&M Engineering Experiment Station | $500,000 |
| TOTAL | $2,031,512 |
|
Marine Energy – Project Synopses
Integrating Wave Energy Convertor with Subsea Energy Storage Device: A Feasibility Study and Demonstration Case
PI: Mejdi Kammoun, American Bureau of Shipping
The wave energy convertor (WEC) along with a subsea energy storage device such as a hydrogen fuel cell can be used to provide emergency power for platform operations and communications during times when there may be power supply issues through the available umbilical cable. These intermittent outages can be costly, and a subsea energy storage device with power on demand could assist in allowing operations to “ride through” any such occurrences. However, there are several challenges and problems associated with the idea. Wave energy is highly variable and intermittent making it challenge to generate a consistent and reliable supply of electricity. Both WEC technology and subsea energy storage device are still developing. Integrating the two requires a complex and highly engineered system that can withstand harsh marine environments while efficiently charging the storage system.
The proposed work will: 1) Survey, identify and document existing R&D work that have done on subsea energy storage device powered by wave energy; 2) Summarize best practices, challenges, and gaps in current applications for subsea energy storage device charging purposes in the offshore oil and gas industry; 3) Engage with industry stakeholders, regulatory agency and OESI consortium to gather insights and feedback on the practicality of wave energy-powered fuel cell to ensure the practicality and safety of deployment; 4) Conduct feasibility study for integrating wave energy-powered systems with existing offshore infrastructure, focusing on technical feasibility, operational requirements, WEC structure integrity, safety consideration and limitations; 5) Validate the feasibility study of Subsea Energy Storage Use Case with a point absorber WEC through wave tank test in University of Michigan; and 6) Develop a comprehensive knowledge dissemination plan to share project.
Comprehensive marine energy safety framework with extensive workforce development and stakeholder engagement
PI: Kevin McSweeney, American Bureau of Shipping
Building off Phase 1 OESI research funding led by ABS, this team will utilize the project’s outputs/deliverables. These include, but are not limited to: stakeholder engagement activities, literature reviews, research on wave energy and tidal energy converters and their failure modes, digital twin activities, and associated use cases, as well as personnel considerations (e.g., equipment design and placement, maintenance considerations, and any associated management and organizational issues.
The project is led by ABS who works closely with BSEE and other maritime authorities worldwide to set and enforce rules and standards used in maritime assets. The team includes Stevens Institute of Technology (SIT) with extensive experience in marine energy (ME) R&D and workforce training; Bill Staby with over 18 years of experience in ME development and involvement with IEC TC 114 standards; and Megan Amsler from Self-Reliance Corp., who has a strong background in offshore wind energy safety and training. The team will engage a broad range of stakeholders to ensure diverse perspectives and comprehensive input.
To validate the framework, the project will implement a case study at the PacWave South test site. This study will focus on applying minimum system safety design parameters, with a particular emphasis on safety factors for critical components like structure, mooring and anchoring system deployment protocols. In long-term plan, a dedicated IEC working group will consider leveraging the current studies to develop a “Guide for Marine Safety Aspects” in IEC TC 114, ensuring the framework’s applicability to future projects.
Innovative Offshore Lifting Training: Integrating Mixed Reality and Machine Learning for Enhanced Safety and Skill
PI: Feng Qiu, Argonne National Lab
We plan to co-develop a mixed reality (MR) based offshore lifting training platform in collaboration with our industry partner Waves4Power. This platform will integrate advanced simulation, MR, machine learning (ML), and team- & competency-based training frameworks. As the industry evolves, these tools and methods are becoming increasingly vital for ensuring safety, skill proficiency, and compliance with international standards.
At the core of our framework are high-fidelity MR simulators, designed to replicate immersive, interactive, real-world offshore environments. These simulators allow operators to practice lifting operations under various conditions, such as high winds or rough seas, without compromising safety. Our MR models will be developed using a physics-based engine (Unity3D), to accurately model crane, load, and lifting gear movements, providing a realistic training experience. MR environments offer a fully immersive and interactive training experience. Operators are placed in virtual offshore scenarios while interacting with physical objects mapped to this digital world, allowing them to practice handling complex loads in lifelike conditions. This framework is particularly valuable in improving hazard identification, spatial awareness, and real-time decision-making.
Furthermore, MR facilitates both competency-based and team-based training, essential for offshore lifting operations. It also supports remote learning and leverages ML to enhance training platforms. Our platform emphasizes competency-based training, ensuring that operators acquire the necessary skills and knowledge before being certified for actual offshore tasks.
This approach follows the standards set by the International Marine Contractors Association (IMCA) and includes a mix of theoretical instruction, practical experience, and safety awareness. Team-based training is also critical, given the collaborative nature of these operations. We focus on enhancing communication, coordination, and teamwork—key elements in managing complex lifting operations and responding to unexpected challenges. By combining these innovative technologies with a competency-based approach, our platform ensures a comprehensive and forward-looking training solution for offshore lifting operations. The proposed tool will be developed with continuous supervision from our industrial partner to ensure that the platform can generate an industry-aligned training program.
Real-Time Monitoring and Visualization System for Marine Energy Devices
PI: Yufei Tang, Florida Atlantic University
Marine energy faces significant challenges, such as the high energy density of fluids leading to high torque and the corrosive marine environment (e.g., corrosion, biofouling), which increases life-cycle costs. Operation and maintenance of marine energy devices are expected to account for 26-32% of their levelized cost of electricity (LCOE). This project aims to develop real-time, low-cost prognostic condition monitoring (PCM) for marine energy devices, including wave energy converters and marine current turbines.
The project team consists of distinguished experts in marine energy and related fields. Prof. Yufei Tang from Florida Atlantic University (FAU), a leader in PCM for marine energy devices, will coordinate the team and guide the development of innovative monitoring tools. Prof. James VanZwieten, also from FAU, brings extensive field-testing expertise to ensure practical validation of the systems. Dr. Xingpeng Li, an Associate Professor at the University of Houston, specializes in power systems and renewable energy integration, contributing advanced computational techniques. Dr. Jia Mi, an Assistant Professor at Stevens Institute of Technology, adds valuable experience in WEC development. Consultant Jeremiah Mendez, Senior Director at Ocean Power Technologies (OPT), brings over two decades of expertise in real-time monitoring systems for marine energy devices, playing a key role in ensuring operational performance and system integrity.
This project will utilize real-time sensor data, applying advanced signal processing and deep learning techniques for cognitive detection and prediction. This will enable online device health monitoring, detect abnormal performance, and assess the rate of degradation, ensuring timely maintenance. Importantly, the PCM system will rely largely on existing control system data, eliminating the need for additional sensors, ensuring a low-cost nonintrusive solution. Additionally, we will develop a novel data visualization dashboard for the PCM system, leveraging OPT’s PowerBuoy control and management system and PowerGPT, a patent-pending product developed by the project lead PI. PowerGPT integrates natural language processing (NLP) with advanced data processing features, enabling users to submit prompts and receive detailed feedback. Its cloud-based engine ensures accessibility, and its user-friendly interface allows even those with limited knowledge to easily access powerful system monitoring functionalities.
Oil & Gas – Project Synopses
Low-Cost Early-Kick-Detection Method via While-Drilling-Measurement Tools
PI: Paulo Waltrich, Louisiana State University
Early detection of kicks, or unexpected influxes of gas into a well during drilling, is crucial to maintaining control and preventing dangerous blowouts. Existing methods such as Managed Pressure Drilling (MPD) and delta flow offer some ability to detect these gas influxes, but they are limited in their ability to detect smaller kicks and often experience delays of several minutes. While-drilling-measurement tools like Measurement While Drilling (MWD) and Logging While Drilling (LWD) have the potential to detect smaller kicks, including gas dissolved in drilling mud, with faster response times of just a few seconds to minutes. However, these tools have not been fully explored for early-kick detection (EKD), and there is little data on their reliability across various drilling conditions, nor on their ability to quantify the size of gas influxes in real-time.
The objective of this project is to study the feasibility of using MWD/LWD tools to detect and quantify gas influxes while drilling, in real-time, using borehole acoustics. This project aims to provide a low-cost solution by leveraging existing tools, understanding their operational limits, and offering the industry new insights into early-kick detection. By testing the tools in controlled environments, the project seeks to determine the accuracy of detecting gas influxes under different drilling scenarios and improve the confidence in using these tools for EKD.
A Novel Approach to Asset Life Extension based on Asset Health and Risk Models
PI: Luis Gonzalez, ABL Group
The offshore oil and gas industry faces significant challenges in maintaining aging infrastructure. Current methods for assessing asset health and predicting service life are often limited by incomplete data, inconsistent record-keeping, and high inspection costs. These limitations hinder proactive failure prevention, posing risks to worker safety, environmental protection, and operational viability.
The project aims to develop a novel software methodology to monitor asset health and assess life extension using inspection and maintenance data. By leveraging advanced data collection, processing, and predictive analytics, the solution will provide more accurate asset integrity assessments. Climate factors will be incorporated, and actionable insights will be offered to extend the life of offshore infrastructure.
Integration of Percussion with Robotics for Offshore Bolted Structure Inspection
PI: Zheng Chen, University of Houston
Pipeline infrastructures transport oil and gas across vast distances on land and offshore. The sheer length of the pipelines means high chances of failure. A timely inspection of subsea infrastructure, especially subsea connections, is the key to the prevention of oil spills. The goal of this project is to develop a robotic-assisted percussion method that enables a time-efficient and cost-effective system for integrity management of subsea connections. Via a remote operated vehicle (ROV) and a robotic mobile platform, which are equipped with a visual-servoing system, and a percussion component, we can detect bolt looseness in offshore and onshore flanges and Grayloc connections.
This innovative method will open the doors to applications for inspection of other kinds of subsea structures. Ultimately, the project will push the boundaries of what can be accomplished by integrating robotics and structural health monitoring technologies. The proposed method is potentially a game changing, cost effective and reliable solution to automated inspection of subsea structural connections.
Enhancing Safety of Offshore Lifting Equipment: A Predictive Monitoring with mmWave Radar NDT, Robot and Digital Twin Modelling
PI: Muhammad Faeyz Karim, Texas A&M Engineering Experiment Station
Offshore facilities face significant challenges in monitoring the integrity of critical lifting equipment like winches, cranes, wire ropes, shackles, and slings. The absence of predictive integrity indicators results in reactive maintenance, relying on operational hours or reported damages. This reactive approach neglects equipment-specific histories and unique risk profiles. Minor damages, such as corrosion or cracks beneath paint or coatings, often go undetected through routine visual inspections. Additionally, insufficient analysis of operational history and inadequate evaluation criteria hinder the ability to predict structural health, leading to unexpected failures. These failures can cause operational delays, equipment damage, and, in extreme cases, worker injury or death. There is a pressing need for predictive maintenance technologies that provide early warnings about potential failures. Such tools must deliver accurate assessments of mechanical integrity, account for factors like environmental conditions and load frequencies, and detect underlying damages before they become visible.
We propose integrating millimeter-wave radar sensors, robotics, and digital twins to implement a predictive, data-driven maintenance approach for offshore lifting equipment.
Machine learning-driven analysis of fire incidents in offshore facilities: identifying trends and developing mitigation protocols
PI: Harishbhai (Harish) Patel, American Bureau of Shipping
This proposal addresses fire incidents in offshore oil and gas facilities, which occur frequently and pose significant safety risks. Common causes include poor maintenance, failure to identify hazards, and procedural non-compliance. The problem is that systemic causes often remain undetermined, limiting effective risk management strategy development. Traditional strategies tend to focus on equipment and structural failures, neglecting human and organizational factors (HOF).
The proposed solution aims to conduct a comprehensive analysis of past fire incidents in offshore facilities to identify trends, causes and develop an effective risk management strategy to reduce such incidents.
The research team will collect and analyze data from multiple fire incident databases, looking for patterns in events leading up to each incident. They will develop a tailored analysis methodology, incorporating statistical techniques and machine learning algorithms.
A new framework for root cause analysis will be developed, emphasizing the integration of HOF. This will provide a more complete picture of factors contributing to fire incidents and identify gaps in current data collection practices.
The comprehensive risk management model will integrate various factors contributing to fire risks, including specific events, existing safety controls, management systems, and identified root causes. AI tools will be applied to make this analysis efficient and effective.
The research will categorize major types of fire incidents and identify key components of safety culture necessary for effective fire risk management.
Practical application is crucial. The developed framework and model will be tested against typical fire scenarios on offshore platforms, and two case studies will be conducted to validate the proposed measures.
Wind Energy – Project Synopses
Health monitoring of offshore wind turbines using physics-based digital twin solutions
PI: Emmanuel Branlard, University of Massachusetts Amherst
Offshore wind turbines face extreme conditions that lead to dynamic loads, affecting reliability, maintenance, and service life. Accurate load prediction and management are crucial for reducing O&M costs, improving safety, and ensuring long-term operability. Current models often lack accuracy due to limited real-time data integration and do not account for all environmental factors on the outer continental shelf (OCS). Advanced tools are needed for structural health monitoring (SHM) and predictive maintenance.
This project addresses these challenges by enhancing our open-source, physics-based digital twin for offshore wind turbines. The digital twin uses OpenFAST software and integrates real-time data to predict loads and environmental conditions via “virtual sensors.” Previous versions were demonstrated on full-scale onshore and floating turbines. This project aims to expand applicability and address gaps identified in prior studies by developing a real-time condition monitoring tool for SHM and O&M. Partnering with RTDT, we will collaborate to meet industry needs.
By developing advanced predictive models and collaborating with our partner, this project will produce a comprehensive digital twin solution, significantly improving load prediction
Development of a predictive tool for fixed wind foundation integrity assessment under multi-hazard dynamic loading conditions
PI: Xiaohong Chen, American Bureau of Shipping
The assessment of fixed offshore wind foundations under dynamic loading is a complex interplay of multiple nonlinear and time-varying forces such as wind, wave, current, vortex-induced vibration (VIV), seismic activity, ice and scour. The dynamic nature and complexity of these forces lead to difficulties in modelling accurate load predictions and soil-structure interaction, assessing structural integrity, safety, and service life of the structure. This must be solved through advanced computational methods, detailed field/lab data, and reliable structure and soil models. Moreover, the methods and tools applicable to the foundation assessment of fixed offshore wind structures are not fully understood or evaluated. This project will tackle this problem by developing a predictive tool for integrity assessment of fixed offshore wind foundations under multi-hazard dynamic loading conditions.
The combination of the loading cases and modeling analysis in this project will be conducted based on the current standards available for the offshore wind structures. For example, the VIV analysis will be based on the methods provided in the IEC standards. The design load cases for ice conditions with combined wind, wave and current conditions and water level will be in accordance with both IEC and ABS standards.
Safe operations of offshore wind turbine blades through accurate modeling and measurement of dynamic wind and rain loading
PI: Nathan Post, Predyct, Inc.
Currently, the U.S. Offshore Wind energy market has 0.2 GW of production and is targeting a substantial increase to 30 GW by 2030. Simultaneously, significant losses and public concern have arisen due to failures and associated environmental damage of the large offshore blades (e.g., Vineyard Wind 1 blade failure, July 2024). This rapid expansion in blade demand, coupled with increasing blade size, necessitates advances in modeling and measurement throughout the lifecycle of the offshore wind turbine blades.
Typically, operational teams rely on schedule-based, labor-intensive inspections, leading to costly downtime, increased emissions, and exposing personnel to hazardous offshore conditions. Our solution will provide a complementary and on-demand predictive operations management approach for offshore wind turbine blades. The methodology will utilize continually updated dynamic model, supported by measurement of wind and rain loading, for accurate estimation of blade conditions.
Our proposed solution will improve dynamic loading models that accurately capture the effects of wind and rain on offshore wind turbine blades. The IEA 15 MW blade model will be trained with the high-fidelity simulation data, rain erosion testing data and comprehensive environmental data including rainfall amounts, rain rates, windspeed, wind direction and temperature in the U.S. OCS. The model will also integrate low-fidelity field measurements of cumulative loading to provide real-time guidance for safe operations.
For the field measurements, an easy-to-install, multi-sensor package will be deployed will be deployed to gather test data on Sandia National Lab’s SWiFT facility for the model validation. The sensor package provides data on blade operations (e.g. tip speed, pitch angle and vibrations) and accumulated impact of rain on the blade leading edge to enable accurate estimation of the blade condition.
The results will be presented using the blade damage classification system aligned with EPRI blade damage classification, widely used in the wind industry. This approach provides a quantitative and complementary approach to visual inspection, guiding operators in inspection planning, leading edge repair and early warning for anomalous conditions, preventing catastrophic failures. The insights gained from the resulting advanced dynamic models will provide a basis for enhanced future design, continued safe operations and re-use of the blades.
Integrated Models for Predictive Modeling of Dynamic Wind and Wave Loads on Offshore Wind Turbines
PI: Chao Sun, Louisiana State University
Accurate modeling and prediction of wind and wave loads on offshore wind turbines under operational and extreme cases is essential for resilient structural design and safe operation. Currently, the dynamic wind and wave loads on turbines are calculated offline, which cannot reflect the real-time metocean and weather evolving conditions to proactively inform and adjust operational strategies. There is an urgent need to develop integrated modeling tools for accurate predictive modeling of dynamic wind and wave loads on turbines.
This research aims to integrate metocean and weather forecast data with aerodynamic and hydrodynamic models to develop integrated modeling tools to predict near term (6 hour to 7 days) real-time dynamic wind and wave loads on offshore wind turbines to proactively inform and adjust operational strategies. Weather forecast wind and wave data (wind speeds, directions, and gusts, significant wave heights, directions, and period) with a resolution of 3 km will be extracted from the real-time updated National Centers for Environmental Prediction database. Then micro-scale modeling will be implemented to provide high-resolution (~10 m) wind field data (wind speed, direction, turbulence, and gust) and wave data for each wind turbine in wind farms. Next, the dynamic wind loads on turbine blades and towers will be predicted in a real-time manner for the next several hours and days. The dynamic wave load will be predicted using a similar approach. The developed integrated modeling tools will proactively inform and adjust operational changes in regular and extreme conditions (e.g., hurricanes).
Safety Additions to FAST for Extreme Weather, Icing, and Nonlinear Dynamics (SAFE WIND)
PI: Mirjam Furth, Texas A&M Engineering Experiment Station
Offshore wind farms will be a cornerstone of the “Blue Economy.” While there are a range of tools available for prototyping wind farms, they are almost exclusively focused on overall design efficiency rather than safety. Due to their primitive numerical schemes current tools are unable to assess designs for many potential hazards. Hazards that can result in a risk to life and/or property include: extreme weather events which result in breaking waves, ice accretion on turbine blades, and mooring fatigue from vortex-induced-vibration.
The goal of the SAFE WIND project is to enable safety to be an active consideration during all stages of the design process of an offshore wind farm. To this end, the primary deliverable of the project will be a comprehensive suite of safety-centric physics extensions for the OpenFAST framework. OpenFAST is currently the leading framework for offshore whole-turbine and wind-farm simulation and is widely used for design evaluation.
To realize these safety extensions, OpenFAST will be coupled with the high-fidelity Computational Fluid Dynamics (CFD) tool OpenFOAM. OpenFOAM is a leading open source software for CFD, and is suitable for simulating unsteady flows in the vicinity of complex and deforming geometries. Our approach seamlessly integrates this capability into OpenFAST. This will ensure that industrial practitioners do not need to invest time and resources learning a new tool.
Given that these new high-fidelity models are more computationally expensive than those currently employed by OpenFAST, we will develop a data-derived “fidelity expert system.” The role of this system is to automatically determine the level of fidelity required at each stage of the design process. For example, a wind farm configuration that is discarded after it was deemed to be inefficient doesn’t have to be evaluated for safety. The role of the expert system is to ensure that expensive safety-centric simulations are performed as soon in the design process as practical, but not so early as to potentially waste computational resources on ineffective configurations.
To further enhance safety, predictive tools should be combined with real-time asset monitoring. Therefore, the analyses of off-design loads performed in the SAFE-WIND project will also be used to identify areas where real-time structural monitoring will be most effective in predicting turbine failures. This will result in a set of guidelines for sensor placement.
