bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expanding the Boundaries on Material Selection for Additive Manufacturing (AM) Technologies: An Investigation of N08904 AM Manufactured Alloy Mechanical & Corrosion Properties as an Alternative to Ni-Based AM Manufactured Alloys
Authors: Sanchez-Valverde A. , Yuang X. , Signorelli R. , Elsayed A. 
Published: April 2024

Abstract
This paper explores the potential of N08904 as an alternative alloy to Ni-based alloys in the Oil & Gas industries by characterizing the mechanical and corrosion properties of Laser Beam Powder Bed Fusion (PBF-LB) manufactured samples and comparing the results against rolled flat material of the same alloy. As of today, Oil & gas industries are slowly adopting AM technologies to produce some of their parts. However, regarding material selection, the possibilities are scarce, as the investigations are focused on proving the feasibility of different AM technologies using mainly highly alloyed Ni-based materials or standard stainless steels such as S31603.
Technology Deployment in Deepwater Projects – The Bonga Example
Authors: Aramanadka S , Adebola D. , Amuda D. T. , Anyankpele D. , Giwa O. F. , Wemimo O. , Osolu O. , Ishie A. , Agbabiaka O. 
Published: April 2024

Abstract
This paper presents some of the technologies that were deployed by the Deepwater Projects & Engineering (DWP) team in the Bonga field to enhance production, reduce deferment and maintain asset integrity. This paper is one of a series of Bonga themed papers for OTC 2024. Noteworthy amongst the deployed technologies are:
A Smart Isolation Plug (smart plug) which was deployed during the first-of-its kind replacement of the gas export (GE) riser flex joint (FJ) in the Gulf of Guinea to reduce production deferment and flaring while mitigating the risk of loss of containment during continuous production. The smart plug eliminated the need to depressurize the 100 km GE pipeline (an ~6-8 days activity), which would have resulted in ~30 MMscf/d gas flaring for ~4 days and significantly reduced the GHG (Greenhouse Gases) emission impact of the FJ on Bonga. This also eliminated the requirement to repressurize upon project completion which would have required an additional 1-2 days. Thus, the Smart Plug provided ~16 days saving to the project execution schedule.
The laser scanning which is built on imaging technology and enabled faster project delivery through Asset visualization, ensuring faster and increased accuracy in Engineering and Fabrication through dimensional control and for corrosion management. Other immediate benefits included quick turnaround of As-built updates, reduction of GHG emissions and safety exposure from offshore flights/travels through the Bonga i-site Asset Visualization Portal.
The Structural Digital Twin (SDT) which is capable of on-demand assessment of the Hull and the Topsides structures of the Bonga FPSO (Floating Production Storage and Offloading). It provides leading performance indicators, ascertains the current health of our facilities including the Safety Critical Structures, enables proactive maintenance planning and evaluation for future conditions related to tie back and life extension. It also enables an organized way of reporting FPSO performance that improvises the approval process with Class society.
ROV installable Anodes is vendor technology that was available for subsea structures. It was considered for mooring, internalized for use on the Bonga FPSO mooring chains and has helped to slow down the uniform corrosion rate. Implementation of this technology as barrier against material loss provides an in-situ remedy for brownfield application thereby enhancing assurance related to asset integrity to mitigate the loss of station keeping.
This paper provides an overview of the successful deployment of some technologies by the DWP team on the Bonga field's journey to the production of one billion barrels of oil. Industry professionals can leverage information in this paper to successfully execute projects on their own journey to achieve more production/less deferment while maintaining asset integrity.
CFD-Based Numerical Wave Basin for Floating Offshore Wind Turbines
Authors: Jang Hyunchul , Jang Hakun , Teng YihJeng 
Published: April 2024

Abstract
This paper introduces a novel coupled analysis methodology for predicting the global responses of floating offshore wind turbine (FOWT) platforms using high-fidelity Computational Fluid Dynamics (CFD) analysis. The hydrodynamic behavior of the floating platform subjected to waves and current is solved by T.EN's CFD-based numerical wave basin tool (MrNWB), and the aerodynamic, aeroelastic, and servo-dynamic behaviors of a wind turbine are solved by NREL's wind turbine simulation tool (OpenFAST). The present numerical wave basin tool is called Wind Turbine Numerical Wave Basin (WTNWB) and is capable of providing wind-turbine performances and tower structural responses as well as 6-DOF platform motions and mooring tensions in high-fidelity. The coupled analysis demonstrates excellent agreement with the model test in the prediction of platform motions and mooring line tensions. WTNWB can overcome known drawbacks of two types of physical model testing methods: full physical model testing and real-time hybrid model testing. WTNWB is now ready to be used in FOWT projects for global performance analysis and is capable of more complicated test cases to consider blade elasticity, pitch control, and other possible sensitivity tests available in OpenFAST.
Integrating Dual-Parameter Soil Classification with CPT-Driven Machine Learning for Site Investigation of Offshore Wind
Authors: Li Zhichuan , Zhang Zijian , Lei Xinhai , Lao Jingshui , Li Ya 
Published: April 2024

Abstract
Accurate geological characterization is crucial in offshore engineering projects. This study aims to develop an advanced methodology that extends the application of Cone Penetration Testing (CPT), providing a more precise and detailed classification of subsurface soils in the locations without coring. Then, the methodology integrates advanced techniques to achieve precise geological characterization. It initiates with a dual-parameter clustering analysis, a process pivotal in uncovering nuanced geological insights. Submerged unit weight and undrained shear strength, recognized as primary indicators, are meticulously examined in the designated coring sites. The K-means algorithm, a unsupervised machine learning technique, is employed in this clustering analysis. It iteratively refines the classification by strategically grouping data points based on their proximity in the dual-parameter domain. Then, a Convolutional Neural Networks (CNN) architecture is designed to synthesize the outcomes of the clustering analysis with the extensive dataset obtained from Cone Penetration Testing (CPT). This network undergoes a training process using a combination of current program data and historical datasets from various regions.
This integrated approach capitalizes on the strengths of both Kmeans clustering and neural networks. While Kmeans excels in identifying patterns and grouping data points in a two-dimensional space, neural networks are adept at learning complex relationships within data. By combining these methodologies, the study achieves a synergistic effect, resulting in a highly accurate and detailed classification of subsea soils. Compared to conventional CPT classification methods, our approach demonstrates superior precision and granularity. By incorporating dual-parameter clustering and machine learning, the methodology refines geological classification and description, providing a more comprehensive understanding of the physical properties of subsea soils.
Inner Wear Liner for Top Tensioned Riser Sidetrack Drilling Operations
Authors: Sundararaman S. , Stiel B. , Thethi R. , Guglielmo C. 
Published: April 2024

Abstract
To increase well productivity, sidetrack drilling operations can be conducted through existing production top-tensioned risers (TTRs). Since 2014, Eni Petroleum have successfully conducted plug back, sidetrack, and recompletion of three wells at the Devil's Tower spar platform in offshore Gulf of Mexico. The platform has eight TTRs with subsea wellheads, which were subsequently tied back to the spar and completed in surface wellheads with dry trees.
This paper presents the riser system design, challenges and the results from the pre-drill dynamic riser analysis and post-drill riser monitoring assessment due to environmental, vessel, and operational loads. This paper also addresses the key benefits and challenges involving the use of a temporary wear liner through the existing spar top tensioned riser system while working within the available aircan tension capacity and dimensional clearance through the riser pipe.
Dynamic global riser analysis is conducted to ensure that there is adequate strength capacity with the wear liner in place. Furthermore, the riser system should tolerate loss of buoyancy through a flooded compartment in the aircan and a shut-in scenario where the drill string is supported by the surface BOP. Results from wear and fatigue damage due to vortex induced vibrations (VIV), vessel induced motion (VIM), drilling induced vibrations (DIV) and wave induced motions are presented based on design motions and monitoring data.
Design, Fabrication, Simulation, and Initial Testing of an Unmanned Surface Vehicle for Oil Spill Sampling
Authors: Ramos R. , Li H. , Olson I. , Diagne L. , Gutierrez D. , Mower R. , Earwood J. , Mendoza M. , Garcia-Pineda O. 
Published: April 2024

AbstractDetection of oil spills with aerial or space-born remote sensing resources has been deeply studied and developed over recent years. However, all these technologies still rely on in-situ verification with direct monitoring and sampling for validation. This paper presents the design, fabrication, simulation, and testing of an unmanned surface vehicle (USV), specifically designed for hydrodynamic efficiency and effective oil spill sampling. The USV's design, featuring a customized platform for the oil sampling mechanism, underwent an extensive simulation process using computational fluid dynamics (CFD) to validate its stability and analyze its impact on water flow dynamics. The design and fabrication process involved hull construction, integration of the control and power systems, and testing in a laboratory environment. The CFD analysis and testing revealed exceptional stability in the USV, showing only minimal rocking and pitching, which translates to consistent speed and precise navigation for the USV during operation. Using CFD for the design analysis allowed for the optimization of water flow dynamics between the two hulls, resulting in reduced drag and enhanced maneuverability for the USV. Turbulent flow patterns were observed at higher speeds, which notably provided valuable insights into the USV's hydrodynamic behavior and its possible interaction with any unforeseen marine conditions, such as encounters with marine life or debris. The key findings demonstrate the USV's potential to revolutionize environmental response efforts, especially in the context of oil spill disasters in remote areas. The accuracy of the CFD simulations was pivotal in anticipating the USV's performance in various marine conditions, which demonstrates the importance of collaboration between theoretical models and practical applications. This paper introduces an innovative integration between autonomous technology and environmental responsiveness, which showcases the possibilities of advancements in the field of autonomous maritime solutions. The involvement of this research extends beyond its immediate scope and offers a new prototype for monitoring and protecting marine environments. This advancement significantly contributes to the broader discussion for safer, more sustainable offshore exploration and environmental management.
Mechanical Seal Integrity for Geological Storage of CO2
Authors: Ait Ettajer T. , Ibañez Martinez E. , Alvarellos Iglesias J. , Rodriguez Morillas N. , Martin Rodriguez H. , Martin Carrillo M. , Mora S. , Vayssaire A. 
Published: April 2024

Summary
The geological storage of CO2 was identified, by the International Energy Agency (IEA) as one of the key levers for meeting the target of carbon neutrality in 2050 (IEA 2022). The main pillars of the value chain of the geological storage are: 1. Capacity, 2. Containment, 3.Transport and Injectivity, 4. Monitoring and Remediation, and 5. Stakeholders Management. Although the first pillar is key as it defines the range of volumes of CO2 that can be stored, the regulatory framework requires that the injected CO2 must remain permanently underground, and that the activity of the CO2 injection does not trigger any induced seismicity (US Department of Energy 2021).
An Application of Machine Learning to Predicting Shear Wave Velocity of Marine Clay
Authors: Taboada V. M. , Sanchez-Hernandez Y. , Zhang Z. , Cruz Roque D. , Barrera Nabor P. , Gan K. C. 
Published: April 2024

ABSTRACT
In geotechnical earthquake engineering, shear wave velocity (VS) is one of the most important parameters. Several empirical correlations have been developed to estimate VS based on in situ tests, such as P-S logging tests and piezocone penetration tests (PCPTs), and soil properties determined both offshore in a field laboratory and onshore in a geotechnical laboratory. Recently, artificial intelligence (AI), machine learning, and deep learning have been the subject of intense media hype and therefore it was deemed appropriate to develop a deep-learning algorithm (DLA) to predict VS and compare its performance with conventional empirical methods of determining VS. A database of 11 sites in the Bay of Campeche with in situ measurements of VS using P-S logging and 8 clay soil parameters, including in situ measurements of net cone resistance (qnet), water content (w), submerged unit weight (g’), void ratio (eo), undrained shear strength (su), effective overburden pressure (σ′vo), over consolidation ratio (OCR), and plasticity index (Ip), was used to train a DLA and develop empirical equations based on multiple regression analysis (MRA) to predict VS. The accuracy of the results (i.e., how close the predicted VS values are to the in situ measured VS values) illustrates the great potential DLA has to become another class of computational tool to predict VS, and DLA has the advantage that it can be continuously updated as new data become available. The DLA was found to outperform MRA when the three input variables are measurements of qnet, w, and σ′vo and when the eight clay soil parameters are used as input parameters. The results suggest deep learning has the potential to overcome prediction limitations associated with linear or polynomial trends and adjust to discrete variabilities in soil conditions. They also reveal potential applications for deep learning in reducing uncertainty in the characterization of areas with complex soils without drastically increasing the scope of a site investigation.
Monitoring of Drilling Fluids and Cuttings as an Environmental Management Tool for Offshore Fluid Operations
Authors: Marinho L. S. , Pereira B. C. , Guandalim F. P. , Cavalcante L. M. 
Published: April 2024

Abstract
This study aims to present the comprehensive outcomes of drilling fluids and cuttings monitoring conducted in the Santos Basin, Brazil, during drilling operations. The research sheds light on the environmental implications and management strategies related to offshore drilling activities. These insights can support decisions guided by Life Cycle Assessment (LCA), aiding in informed choices to enhance efficiency and sustainability. The drilling fluids and cuttings monitoring project (DFCMP) encompasses data on drilling discharged into the sea. The monitoring took place during the drilling of 41 deep water wells in Santos Basin, Brazil. Samples were collected at various stages of drilling operations. Data includes discharge quantities, ecotoxicological profiles of the fluids, as well as determinations of metals and polycyclic aromatic hydrocarbons (PAHs) in the samples. These methods enabled the evaluation of environmental impacts throughout the drilling lifecycle. This comprehensive approach provides valuable insights into the effects of drilling activities, contributing to informed decision-making in line with Life Cycle Assessment principles. The monitoring of fluids and cuttings discharged into the sea from 41 drilling operations carried out between 2022 and 2023 in the Santos Basin, Brazil, generated representative samples of fluids and cuttings. These samples account for the offshore discharge of 16000 m³ of cuttings and apporximately142000 m³ of aqueous fluids. Chemical and ecotoxicological analyses conducted on these samples confirm the environmental compliance and quality of these discharges, affirming their adherence to the legal requirements established in Brazil. Regarding the waste generated for onshore disposal, the compiled results indicate an average annual generation of around1590 tonnes of waste, with about 56 % comprising liquid waste from fluid operations, primarily aqueous fluids. Despite fluid and cuttings characterization being present in scientific literature, few contributions emphasize samples collected prior to sea discharge. Many characterizations focus on terrestrial disposal routes and cater to relevant criteria for this purpose. Our study's findings enhance understanding of qualified fluid and cuttings discharged at sea, highlighting industry best practices that meet Brazilian regulations. These locally relevant data are crucial for life cycle analysis, aiding environmental profiles and impact assessment for different waste management routes.
Risk Mitigation for Punch through of XXL Monopiles – MPM Validation and Field Observations
Authors: Thijssen R. , Martinelli M. , Luger D. 
Published: April 2024

Abstract
Uncontrolled punch through during set down of an XXL monopile can be disastrous for a (floating) installation vessel. An analytical model was developed to predict punch through capacity for monopiles on sand-over-clay conditions. Material Point Method (MPM) analyses were used for validation of the model and a proposed risk mitigating measure that allows for a controlled punch through, with reduced risk to vessel and equipment. Field observations from construction of a wind farm off the US East coast allowed for full scale validation of the model and reflect the effectiveness of the risk mitigating measures taken.
Quantifying the Mechanical Specific Energy (MSE) with Differential Wellbore Pressure for Real-Time Well Automation
Authors: Sahood Tariq , Myers Michael T. , Hathon Lori , Unomah Gabriel C. 
Published: April 2024

AbstractDrilling efficiency is the ratio of unconfined compressive strength (UCS) to mechanical specific energy (MSE); this parameter is important for understanding the drilling process and essential for attempts at drilling automation. MSE is a measure of the energy required to remove a unit volume of rock and depends on many parameters, including the rate of penetration (ROP). However, the standard use of MSE is limited in optimizing drilling efficiency because it fails to account for the effect of wellbore differential pressure (wellbore pressure minus pore pressure) on ROP.UCS has an obvious influence on drilling efficiency, but it is difficult and expensive to measure UCS values under differential pressure. To eliminate the need for measurement of UCS, we applied a modified MSE (MMSE), which accounts for the effect of differential pressure. This is performed by multiplying the MSE by the ratio of pore pressure to bottom hole pressure. Bottom hole pressure is calculated from the mud weight and well depth and corrected for frictional losses. The pore pressure is calculated using the hydrostatic gradient.We overlaid resistivity logs from eight wells, which are sensitive to pore pressure, and compared the trends of MSE and MMSE. The MMSE shows better correlations with resistivity data, especially in over-pressured reservoirs, which results in a more accurate prediction of drilling efficiency. In normal pressured zones, the MMSE and MSE values show similar trends. As expected, they deviate from the typical compaction trend lines for the over-pressured zones. The depth and pore pressure data in the over-pressured zones are typically obtained from the drilling record. A higher correlation between MMSE and the resistivity log data occurs in overpressured intervals.The comparison between MSE and MMSE was also tested on historical well data. The calculated MMSE again closely correlates with the resistivity. Therefore, MMSE is expected to predict abnormal pore pressures (overpressure) using the degree of divergence of the MMSE from the MSE values. Also, during underbalanced conditions or indicating a potential kick, the value of the MMSE is less than one. This predicts a large rate of penetration (ROP) and potentially gives an improved method of predicting a kick. In general, the MMSE significantly improves the understanding of drilling rates and pore pressure prediction.
Real-Time Automated Drilling Optimization with Digital Twins: Enhancing Performance, Mitigating Risks, and Reducing Costs
Authors: Cao Jie , Gocmen Emre , Nabavi Josef , Oedegaard Sven Inge , Kristiansen Tron Golder , Khosravanian Rasool , Solem Kristian 
Published: April 2024

Abstract
This paper presents an innovative approach to digital and automated drilling, offering cost reduction, improved development efficiency, and enhanced risk management. The focal point is an automated Rate of Penetration (ROP) optimization system powered by digital twin and artificial intelligence (AI) technologies, which effectively enhances drilling performance while mitigating potential issues.
At the core of this system are digital twin transient models, continuously calibrated using real-time data, and a fast auto-optimization module. Leveraging model-based reasoning in AI, the system provides real-time, forward-looking advice on operational parameters. It incorporates predictive analytics and multiple what-if scenarios to optimize drilling operations while considering imminent risks and anticipated hole-cleaning requirements for ROP optimization.
The effectiveness of the advisory system has been rigorously tested and verified, with a specific focus on the auto-calibration and auto-optimization modules. In this research, the system calculates time savings for each drilling section by comparing real-time ROP with advised values. The results clearly demonstrate the value created through reduced drilling time, empowering the rig team to operate independently and minimizing reliance on back-office support. Moreover, the system evaluates the potential drilling risks and hole cleanness for proposed operational parameters, ensuring the optimized results are risk-free in pursuing higher ROP.
Overall, this real-time optimization system offers a transformative approach to drilling operations, yielding significant time and cost reductions. Its supervisory capabilities streamline the implementation of advanced digital twin models and parameter optimization, thereby improving operational performance and driving further integrated drilling automation.