bluuebunny/crossref_metadata_2025_split huggingface dataset:
Centralized Predictive Analytics & Diagnostics (CPAD) Program
Authors: Al Hendi Mariam Abdulla , Al Alawai Faisal Masoud , Eisawy Shahin Abdelsamad , Al Abdouli Abdulla Omar 
Published: April 2023

Abstract
Major Rotating equipment assets availability and reliability is critical for oil and gas companies. Current practice to ensure high availability and reliability is carrying out fixed running hours based preventive maintenance, which then dictates the Plant shutdown intervals, and increase the operating cost.
Operational excellence, high efficiency and reliability, can be maintained and even enhanced further with latest digital technology enablers, Digital technology that uses advanced analytical and modeling techniques that understand equipment and operating behavior – to predict deviations in "optimal performance" versus "current performance" months ahead of time ("hidden" plant opportunities early identified).
The company in its pursuit to operation excellency has identified and deployed solutions to enhance efficiency, reliability, improve performance and reduce operating costs related to Major rotating equipment operation and maintenance (Centrifugal, screw & Reciprocating & Compressors, Gas turbines, Steam turbines, Pumps, Power turbine and turbo Expander). The solution installed, commissioned and operates Smart Predictive analytics & diagnostics solutions (CPAD) to monitor analysis, anomaly predict and assess the conditions of Major Rotating Equipment.
The Predictive diagnostics are based on modelling techniques coupled with machine learning algorithms deployed to achieve the early prediction of conditions and performance degradation causes & symptoms, ensures timely intervention by making small changes on line to prevent subsequent failures and extend the equipment remnant live, expand the planned shutdown intervals and reduce unplanned shutdowns.
The solution is a data-driven to leverage existing data and infrastructure, provide insights and actionable warnings on equipment performance, reliability and process problems using the existing instrumentation on any type of equipment or processes, regardless of equipment manufacturer or agnostic to equipment age or class.
Evaluation of Numerical Methodologies and Physical Effects on a Performance-Scaled Floating Offshore Wind Turbine Rotor
Authors: Bouman Gijs , Serraris Jorrit-Jan , Lim Chin Lee , Xing Xiuqing , Kang Chang Wei , Zhang Xiaoqin , Gopalan Harish , Nguyen Vinh Tan , Liu Xuan , S.G. Raghavan Venugopalan , Poh Chien Sheng , Yu Kai 
Published: April 2023

Abstract
Performing numerical simulations of a scaled-down experimental setup of a floating offshore wind turbine allows for a two-way validation between the two approaches. Furthermore, additional insight in the physical behavior of the system can be obtained by tuning numerical results on experimental measurements, and exploring the results. Numerical simulations of a 10MW floating offshore wind turbine model in a wave basin are performed at model scale using computational fluid dynamics. The performance-scaled turbine is designed to match the Froude-scaled thrust force in a Froude-scaled wind field. Initial results show a discrepancy in calculated turbine thrust and torque results compared with experimental results. Two main challenges are identified: (1) selecting a numerical approach appropriate for the low-Reynolds number flow, and (2) accurately modelling the environmental conditions in the basin. In this paper, a numerical sensitivity study is carried out by varying systematically the turbulence models and inflow conditions. A standard k − ε turbulence model is used, as well as a more extensive γ – Reθ turbulence transition model. Different inflow conditions are set up to model the turbulent jet wind field in the wave basin. It is found that the k-ε turbulence model is unsuitable to match the model test results, while satisfying results are obtained using the γ – Reθ model. Furthermore, it is seen that the turbulent jet inflow is represented well by both a vertical power law profile and a radial profile fitted to wind field measurements, while uniform and tabulated inflow conditions are a poor representation of the experimental conditions. It is concluded that effects from surface roughness, the Reynolds number, the inflow velocity and turbulence distribution must be included in the numerical evaluation.
US Feeder Solutions for Offshore Wind
Authors: Wang Suqin , Sreenivasan Atheendra , Saint James Michael , Yu Qing , Huang Wei , Voogt Arjan 
Published: April 2023

Abstract
Feeder vessels are one of the solutions being considered to facilitate Jones Act-compliant installation of offshore wind turbines on the US outer continental shelf (OCS). Feeder vessels transport wind turbine components from US ports to offshore locations and position next to an installation vessel, where the components are then lifted off using a crane on the installation vessel. The lifting operation is a highly critical and transient process. A successful lift depends on the lifting system's capability, the feeder vessel's performance, environmental conditions, operational processes and other factors.
In 2021, the National Offshore Wind Research and Development Consortium (NOWRDC) initiated Project #107, Comparative Operability of Floating Feeder Solutions, that was undertaken by MARIN, ABS and Saint James Marine. The project primarily entailed computer simulations of offshore wind turbine component lifts and the development of comparative operability assessment procedures for floating feeder solutions. Time domain simulations were used to model the lifting process and results were analyzed relating to wind turbine component accelerations, loads on the lifting system and the re-hit of wind turbine components with the feeder vessel. The study has demonstrated that the combination of time-domain analysis for lifting simulation and frequency-domain analysis for feeder vessel motions can be an effective approach to comparatively analyze a wide range of feeder solutions. Comparative weather downtime and seasonal utilization can also be effectively derived. This paper describes the development process of and provides recommendations for assessing the operability of offshore wind feeder vessels.
The King's Quay Project – A Best Practice Model to Deliver Major Offshore Projects Ahead of Schedule and Under Budget
Authors: Arciero Bryan Vincent , James Robert Clayton 
Published: April 2023

Abstract
The King's Quay facility was fabricated and commissioned in South Korea and installed in the Gulf of Mexico to receive production from the Khaleesi, Mormont and Samurai fields. A mixture of project and operations personnel were tasked with executing deliverables to ensure a successful progression from engineering, construction, commissioning and operations to ultimately achieve first oil in April 2022.
The facility design was based on an existing design already under operation, with modifications limited to improving safety and reliability and reducing emissions. The subsea umbilicals, risers and flowlines (SURF) contract was awarded to include mooring and installation work, minimizing interfaces and reducing risk exposure to the operator. Technical functions fell under one project delivery team, ensuring decisions were made based on the overall benefit to the project rather than individual disciplines. Operations were involved early on during the construction phase in South Korea, and took ownership to integrate improvements throughout the project lifecycle. Subsurface design allowed for a shift from single zone to commingled production to maximize net present value (NPV) and reduce well design complexity. SURF components were standardized as much as possible across all three fields to allow for flexibility during the installation phase.
Between the on-site construction team and a strong cohort of local inspectors, the facility left the shipyard on schedule and 97% complete, with minimal carry-over work in the Gulf of Mexico, and over 3.5 million work hours without a lost time incident. With the project team relying on industry-standard designs and best practices, they were able to optimize cost, schedule and functionality based on fit-for-purpose equipment designs. The flexibility of installation allowed the minimizing of simultaneous operations (SIMOPS) between pipelay and drilling activities. In the event of SIMOPS, communication protocols were established and strictly followed, minimizing non-productive time.
Murphy's King's Quay development achieved first oil in April 2022, less than three years after the project's final investment decision (FID). This paper will highlight the execution plan and lessons learned to maintain continuity through all phases of the project to deliver a facility and subsea infrastructure ahead of schedule while achieving 97% uptime, with production rates exceeding expectations within six months of start-up.
Energy Optimization for Dynamically Positioned Vessels through Closed Bus Operations
Authors: Breedlove Tyson R , Nguyen Peter Q , Hensley Michael 
Published: April 2023

Abstract
Increasing pressure to reduce greenhouse gas emissions has led owners of dynamically positioned (DP) vessels to investigate new and innovative technologies and configurations that can optimize the energy utilization and fuel consumption. One such method is to operate the power plant in a closed bus configuration, meaning to have all major switchboards connected via bus ties (also known as closed ring or linear bus). In many operational scenarios, this approach leads to less engines running on higher loads and therefore greater energy efficiency. However, this configuration can lead to a higher risk of electrical fault, such as short circuit, cascading throughout the electrical system and causing a total black out of the vessel.
ABS (American Bureau of Shipping) has updated the Guide for Classing of Dynamic Positioning Vessels with an optional notation, EHS-E, (EHS=enhanced, E= electrical) which include optional requirements specifically addressing the risks associated with operating closed bus. The requirements are unique in that they are written to address current vessels that are prime candidates for electrical upgrades.
At a high level, the minimum upgrades or configurations required to achieve an enhanced electrical DP notation are:
Enhanced Generator Protection (EGP) (also known as Advanced Generator Protection (AGP)) Power management system to include bus protection scheme. DP Control System to include thruster fast phase back. Configuration changes to ensure equipment can remain operable during a fault ride through test.
EGP upgrades are important because it ensures a quick response to electrical disturbances at one generator and prevents this disturbance from causing other healthy generators from being affected. If a single generator is faulty, the EGP works to disconnect it quickly to prevent disturbing the rest of the power plant and causing a blackout which can subsequently cause a loss of position and/or heading.
The adequacy of the upgrades is confirmed by DP FMEA (Failure Modes and Effects Analysis) validation testing as well as a live short circuit fault ride through test to verify that the system is capable of riding through an actual short circuit. The fault ride through tests often discover additional configurations and settings that must be tuned to keep equipment online and prevent a total blackout of the vessel. To achieve EHS-E notation, any blackout must recover within 60 seconds.
This paper provides a pathway for existing units to retrofit their power plant and DP system to operate in a closed bus tie configuration. If properly upgraded, this configuration can add significant fuel savings and reduce greenhouse gas emissions resulting in lower cost and lower Scope 1 carbon intensity.
Estimation of Wind Resource Assessment at High-Resolution Using SAR Observations, Validated with Lidar Measurements
Authors: Cathelain Marie , Husson Romain , Berger Henrick , Fragoso Mauricio 
Published: April 2023

Abstract
The 18-year database of Envisat & Sentinel 1A-1B Synthetic Aperture Radar (SAR) provides worldwide surface wind measurements at a 1-km resolution. Through an innovative vertical extrapolation methodology published in 2022, these long-term, wide, and high-resolution observations can complement in-situ observations and mesoscale modelling for offshore wind resource assessment. The methodology is based on three steps: (i) derivation of the 10-min SAR surface winds from SAR sea surface roughness, and a site-independent machine learning algorithm based on a large buoy network to correct SAR winds due to biases inherent to the characteristics of the satellite sensors and wind retrieval methodology, (ii) extrapolation up to a few hundred meters based on a second machine learning algorithm trained with in-situ observations and physical parameters from a high-resolution mesoscale model related to e.g. atmospheric stability, and (iii) a final post-processing step to correct for low temporal sampling of the SAR database (one passage every two days for one satellite) and retrieve wind statistics. The resulting output is a 1-km resolution wind atlas in a large (&gt; 3000 km2) offshore and/or coastal area where strong coastal gradients will be accounted for, or it can integrate directly the estimation of the extractible wind power. Here, an improvement of the extrapolation method is presented and applied to a French offshore area in South Brittany. The wind atlases obtained with SAR are found to display a much finer level of details and estimate more precisely the coastal gradient.
Leveraging Targeted Machine Learning for Early Warning and Prevention of Stuck Pipe, Tight Holes, Pack Offs, Hole Cleaning Issues and Other Potential Drilling Hazards
Authors: Payrazyan Vlad Karen , Robinson Timothy S. 
Published: April 2023

Abstract
Stuck pipe and other related drilling hazards are major causes of non-productive time while drilling. Being able to spot early indications of potential drilling risks manually by analyzing drilling parameters in real-time has been a significant challenge for engineers. However, this task can be successfully executed by modern data analytics tools based on machine learning (ML) technologies. The objective of the presented study is to prove and demonstrate the ability of such machine learning algorithms to process and analyze simultaneously a variety of surface drilling data in real-time in order to: a) detect anomalies, that are in most cases invisible to a human eye; and b) provide early warnings of possible upcoming drilling risks with sufficient time in advance, so that the rig crew can execute the appropriate mitigation actions.
The algorithms developed have favorable characteristics, such as adaptiveness to real-time data and agnosticism to well types, BHAs, mud types, lithologies or any other specific well characteristics. This supports out-of-the-box usage, which enables scalability to large numbers of wells. Targeted sub-systems detect the current operation type (tripping, drilling, reaming, etc), and detect symptoms related to differential sticking, hole cleaning issues, mechanical sticking, pack offs, tight holes, obstructions and other risks by analyzing standard surface drilling time logs in real-time, such as hookload, WOB, RPM, bit depth, mud pressure, etc. The ML models and wider risk detection system have been demonstrated to generalize to new wells, and consistently produce high performance across those tested, without any need to pre-train the models on historical data from offset wells.
The system connects to WITSML data stores and outputs warnings with specific information regarding the identified symptom of the potential drilling incident, leaving it up to the rig crew or drilling supervisor to decide how to act on those warnings. The system provides drilling engineers with live warnings on average 1.5-4 hours prior to incidents, giving rig crews enough time to react. This also allows drilling engineers to know in advance a specific source of potential risk, which assists in selecting the right strategy for implementing corrective actions.
The technology's performance was successfully verified in live operations and post-drill studies on historical data on over 300 wells worldwide during the past 2.5 years, with mean recall and precision metrics of 0.986 ± 0.050 and 0.712 ± 0.181 respectively across historical test wells, and significantly reduced occurrence rates of stuck pipe incidents in both onshore and offshore operations. Real case studies for onshore, offshore, conventional and unconventional assets will be presented and discussed.
Mooring Line Failure Detection Using Artificial Neural Networks: An Application to Field Data Including Artificial Failure Cases
Authors: Sidarta Djoni Eka , Auburtin Erwan , Ledoux Alain , Lim Ho-Joon , Leridon Aurelien , Tcherniguin Nicolas 
Published: April 2023

Abstract
Alternative methods to using physical sensors for monitoring the integrity of mooring lines on floating offshore platforms are of great interest in the Offshore O&G industry. These alternative methods can be used in parallel with the physical sensors at the start of the service life of the asset, measurements from the physical sensors can be used to validate these methods, and the validated methods can be vital tools when the physical sensors are no longer working.
Technip Energies has presented ALANN (Anchor Lines monitoring using Artificial Neural Networks) system in a 2021 OTC paper to detect mooring line failure using a dry monitoring system. The system requires only monitoring the positions and headings of the vessel, and it requires information on the draft of the vessel for an FPSO. The system combines status-based detection and event detection to determine mooring line condition, whether they are intact or there is mooring line failure. Artificial Neural Networks (ANN) play a very important role in the status detection with the ability to detect subtle shifts in patterns of vessel motions from intact lines condition to a mooring line failure condition. Numerical algorithms are used for status detection that complements ANN for benign environmental conditions and for event detection.
An ALANN system has been developed, including training ANN models using numerical simulations, for a spread moored FPSO in West Africa. The system has been tested using field data that include time series of easting and northing positions of the vessel, vessel headings and vessel drafts. In addition, field data have been modified to include artificial mooring line failure cases, and these altered field data are used to further test the system. ALANN system performs very well for measured data in good quality independently from the FPSO loading condition, and it is able to detect all the artificial failure cases. The tests confirm and demonstrate how components of the ALANN system contribute to and improve the robustness of the overall solution. Important lessons learned, including challenges that have been encountered due to typical real-life sensor and communication issues (leading to gaps, drift, spikes and/or varying sampling within the data), along with comparison of measured and simulated data are presented in this paper.
Building the Assurance Case for Recovery of Autonomous Underwater Vehicles from Offshore Platforms
Authors: Deeb Amy , Garner Toby , Kuchekar Dipali 
Published: April 2023

Abstract
Digitalization was identified by the International Regulators’ Forum (IRF) as one of three key problems in 2021 due to the petroleum industry becoming increasingly reliant on automated systems. Autonomous underwater vehicles (AUVs) are of particular interest for inspection and maintenance of offshore platform infrastructure; however, the integration of such a complex system requires careful mitigation of safety health and environment risks associated with its operation. Analysis of human factors, including ergonomics, job organization, and cognitive considerations, has been recommended by the IRF to increase confidence that the interaction of the operators and the equipment (manually controlled, automated, or autonomous) permits safe delivery of the system capability in a safe working environment. The established CRitical Intervention and OPerability (CRIOP) analysis methodology is chosen as the basis for constructing an assurance case for an AUV control station on an offshore platform. CRIOP's goal is to show that the control center is able to safely manage all modes of operation.
This paper proposes a CRIOP Assurance Case Template for a control station on an offshore platform. The proposed template uses goal structuring notation (GSN) with arguments, assumptions, and solutions, following the strategy of the CRIOP methodology. This template supports a critique of CRIOP's completeness arguments, goal-based structure, and solution acceptance criteria. Next, an assurance case for an AUV recovery operation is instantiated from the template for three cases with different autonomy levels: manual, automated with a human-in-the-loop, and autonomous. The increasing level of autonomy was shown to result in both technical and methodological variances to the assurance case including: layout changes as the control station moves from the open deck of a boat to a control room; changes in the acceptance criteria for the expertise of analysis team members; and the recommendation for additional scenarios that address situations in which the level of autonomy must be decreased due to safety concerns. Interviews with experienced AUV operators and developers revealed the value in including operators in the CRIOP analysis team to increase operator confidence in being able to maintain meaningful human control in all modes of operation. It is only with this confidence that autonomous systems will be used for the ambitious missions they can perform.