bluuebunny/crossref_metadata_2025_split huggingface dataset:
Offshore Implementation of Temperature Microchip Under Critical Well Conditions
Authors: Li Bodong , Dokhani Vahid , Gooneratne Chinthaka , Zhan Guodong , Shi Zhaorui 
Published: September 2021

Abstract
Drilling microchips are millimeter-size sensing devices, capable of measuring in-situ downhole temperature, and at the same time, withstanding harsh downhole conditions. In this work, 140 microchips were dropped from the drill pipe during the connections. The devices travel through the bottomhole assembly (BHA), drill bit, annulus, and eventually get recovered at the shale shaker. A total of 80 microchips were recovered at the shaker, which resulted in a physical recovery rate of 57%. The microchip recorded the dynamic temperature profile of the entire wellbore including a long openhole section only a few hours before the well turned into total loss. The data downloaded from the microchip shows an excellent consistency throughout the three tests. The measured dynamic bottomhole temperature provides a correction of 10 deg F to the best practice of the industry in terms of downhole thermal simulation, offering valuable measured input for the optimization of thermal activated LCMs or cementing job. To our best knowledge, it is the industry's first successful attempt in logging an openhole section in a highly loss zone. The microchip recorded the dynamic temperature profile of a long open hole only a few hours before the well turned into a total loss. Due to the lack of industrial solutions for downhole temperature measurement under such conditions, the microchip technology showed unique advantage for critical applications, especially in operations with highly valued assets.
Sustaining Remote Operations Adoption Post Pandemic: A Major Key to a Net Zero Future
Authors: Richard Yates Martin , Brahim Imed Ben , AlNofaily Shady Mohamed , Ernesti Klaus-Dieter 
Published: September 2021

Abstract
The North Sea has always been a pioneer for the adoption of remote operations services (ROS) in offshore drilling applications. Drilling services such as Measurement While Drilling (MWD), Logging While Drilling (LWD) and/or mud logging (ML) have been performed with an element of ROS for over the last two decades. Early adoption of these remote services delivered initial benefits to operators such as reducing HSE risks related to the travel and accommodation of field service employees at offshore rig sites. Meanwhile service companies were able to explore the added efficiencies gained by having multi-skilled employees providing a higher level of support to customers while also gaining additional agility to manage their personnel through tighter market cycles. The mutual benefit of this early adoption created a solid foundation for ROS to expand the scope of influence in drilling operations to include Directional Drilling (DD).
Despite the maturity of ROS within a select community of operators in the North Sea, the industry standard for service delivery in offshore operations has continued to require field service employees to perform DD, MWD, and LWD services at rig sites until this past year. With the COVID-19 pandemic in 2020, operators and service companies were quickly and abruptly confronted with the challenges of new HSE regulations, travel restrictions, and increased financial scrutiny. ROS presented a tailored solution to not only sustain business continuity but also create added efficiency, consistency, and risk management.
Over the course of 2020, adoption of ROS rapidly accelerated across offshore operations in the North Sea and reached up to 100% penetration in key sectors. This tremendous achievement has made a significant impact on project performance and HSE efficiencies by ensuring on time service delivery while reducing personnel on board (POB). In addition, as more operators and services companies explore ways of reducing their carbon footprints and achieving a net zero future, ROS has proven to be a way to significantly reduce carbon emissions associated with transportation and utilities of offshore personnel.
This paper discusses the methods that enabled a record high adoption rate for ROS and explores the critical components of its success. It illustrates the management of change in service delivery processes, the introduction of new technology to unlock greater productivity and synergies, and the new approach to design the core competencies needed to support ROS. It also describes the need for flexible ROS service models to meet the specific project needs of various operators. The paper concludes with the numerous benefits realized through ROS such as improved performance and consistently reliable service delivery. The paper also examines the resulting carbon emission reduction, how to quantify it, and the role ROS plays in achieving a net zero emissions future.
Natural Language Processing and Text Mining Approaches in Production Shortfalls Analytics: Methodology, Case-Study and Value in the North Sea
Authors: Bernier Edgar , Perrier Sebastien 
Published: September 2021

Abstract
Maximizing operational efficiency is a critical challenge in oil and gas production, particularly important for mature assets in the North Sea. The causes of production shortfalls are numerous, distributed across a wide range of disciplines, technical and non-technical causes.
The primary reason to apply Natural Language Processing (NLP) and text mining on several years of shortfall history was the need to support efficiently the evaluation of digital transformation use-case screenings and value mapping exercises, through a proper mapping of the issues faced. Obviously, this mapping contributed as well to reflect on operational surveillance and maintenance strategies to reduce the production shortfalls.
This paper presents a methodology where the historical records of descriptions, comments and results of investigation regarding production shortfalls are revisited, adding to existing shortfall classifications and statistics, in particular in two domains: richer first root-cause mapping, and a series of advanced visualizations and analytics.
The methodology put in place uses natural-language pre-processing techniques, combined with keyword-based text-mining and classification techniques. The limitations associated to the size and quality of these language datasets will be described, and the results discussed, highlighting the value of reaching high level of data granularity while defeating the ‘more information, less attention’ bias.
At the same time, visual designs are introduced to display efficiently the different dimensions of this data (impact, frequency evolution through time, location in term of field and affected systems, root causes and other cause-related categories). The ambition in the domain of visualization is to create User Experience-friendly shortfall analytics, that can be displayed in smart rooms and collaborative rooms, where display's efficiency is higher when user-interactions are kept minimal, number of charts is limited and multiple dimensions do not collide.
The paper is based on several applications across the North Sea. This case study and the associated lessons learned regarding natural language processing and text mining applied to similar technical concise data are answering several frequently asked questions on the value of the textual data records gathered over years.
Complete EOS Thermal Formulation for Simulation of CO2 Storage
Authors: Moncorgé Arthur , Petitfrère Martin , Thibeau Sylvain 
Published: September 2021

Abstract
Storage of CO2 in depleted gas reservoirs or large aquifers is one of the available solutions to reduce anthropogenic greenhouse gas emissions. Numerical modeling of these processes requires the use of large geological models with several orders of magnitude of variations in the porous media properties. Moreover, modeling the injection of highly concentrated and cold CO2 in large reservoirs with the correct physics is introducing numerical challenges that conventional reservoir simulators cannot handle. We propose a thermal formulation based on a full equation of state formalism to model pure CO2 and CO2 mixtures with the residual gas of depleted reservoirs. Most of the reservoir simulators model the phase-equilibriums with a pressure-temperature based formulation. With this usual framework, it is not possible to exhibit two phases with pure CO2 contents. Moreover, in this classical framework, the crossing of the phase envelope is associated with a large discontinuity in the enthalpy computation which can prevent the convergence of the energy conservation equation. In this work, accurate and continuous phase properties are obtained basing our formulation on enthalpy as a primary variable. We first implement a new phase-split algorithm with input variables as pressure and enthalpy instead of the usual pressure and temperature and we validate it on several test cases. This algorithm can model situations where the mixture can change rapidly from one phase to the other at constant pressure and temperature. Then treating enthalpy instead of temperature as a primary variable in both the reservoir and the well modeling algorithms, our reservoir simulator can model situations with pure or near pure components as well as crossing of the phase envelope that usual formulations implemented in reservoir simulators cannot handle. We first validate our new formulation against the usual formulation on a problem where both formulations can correctly represent the physics. Then we show situations where the usual formulations fail to represent the correct physics and that are simulated well with our new formulation. Finally, we apply our new model for the simulation of pure and cold CO2 injection in a real depleted gas reservoir from the Netherlands.
Mooring Integrity in Electrification Projects - Learning from the Floating Production Sector in Early Years of Harsh Environment Operations
Authors: Robinson Nigel J. , Rosie Scott M. 
Published: September 2021

Abstract
There is a growing interest in electrification of offshore production assets using renewable energy devices. Typical configurations have arrays of power generating devices, located a short distance away from the platform and linked by subsea cables. To date, floating offshore wind turbines and wave energy converters have been trialed as sources of electrical power. With hulls, moorings and cables set out in close proximity, there is a unique station keeping risk profile that needs to be managed, to ensure fitness for service through the life of the field.
When developing electrification schemes, there is a body of relevant research that can be accessed to help identify and control mooring risks. In the early days of North Sea floating production, the industry encountered multiple instances of accelerated mooring line degradation. The causes and mitigations were researched through joint industry projects, leading to the publication of operating guidance through OGUK, the HSE, ISO and others. This guidance, along with a series of milestone papers, offers a valuable body of learning that is available to electrification projects.
In this paper, the authors survey the integrity threats and challenges affecting moorings in electrification schemes that are based around small arrays of wind turbines or wave energy converters. Through qualitative risk assessment, comparison is made to mooring integrity issues in the floating production sector. The relevance of guidance from oil and gas is reviewed. Consideration is given to the consequences of a line failure, and how in some cases these may represent intolerable risks. It is argued that a through-life integrity risk assessment is essential for successful deployments; also, that existing OGUK guidance on mooring integrity be extended, to reflect the specifics of electrification projects.
Application of Monte Carlo Analysis and Self-Organizing Maps to De-Risk Compressor Re-Wheeling
Authors: Nelson Greg Michael , Barrie Robert 
Published: September 2021

Abstract

Objectives / Scope
Re-wheeling compressors to match late-life field conditions gives significant benefits in operational efficiency and carbon reduction. But changing the compressor wheels and increasing shaft speeds also introduces a risk in terms of the rotor-dynamic stability of the system. API assessments use deterministic methods to assess the design change, but give less information in terms of the key risks and how to control them. This paper outlines new methods for assessing rotor dynamic risks to compressors during re-wheeling and their value over traditional methods.

Methods
New methods were developed to extend beyond the API requirements in order to assess and manage the rotor-dynamic risk as part of a peer review process of re-wheeling a compressor train. A combination of sensitivity studies on key parameters and Self Organizing Maps (SOMs - a machine learning technique) was used to identify the factors which present the greatest risk to the re-wheeling, and a Monte Carlo analysis was used to identify the change in risk of rotor-dynamic problems when compared with the existing machine.

Results
The Monte Carlo analysis used random distributions of factors on key input parameters, and the same factors were applied to the existing and re-wheeled designs. It identified that although the re-wheeled design was nominally more stable than the existing design according to the API analysis, it actually presented a greater risk of instability. This is because the distribution of resulting stability values had a higher mean but a greater spread than the existing machine when subject to uncertainty in input parameters. Since the existing machine is free from dynamics problems, the parameter combinations which resulted in an unstable existing machine could be discounted, but the resulting subset of factors when applied to the re-wheeled design still gave some unstable cases. Therefore, the fact that the existing machine is free from dynamics problems does not in itself discount the possibility of problems following the re-wheel.
SOMs were used to identify the components which posed the greatest risk to the re-wheeled design. This highlighted that low stiffness in two particular bearings along the high speed shaft would pose the greatest risk to shaft stability, meaning that close attention can be paid by the operators and OEMs to this to manage the risks as the re-wheel progresses.

Novel Information
This work shows that probabilistic and machine learning techniques have significant value in managing risks during compressor re-wheeling, highlighting risks which would not be identified using standard deterministic methods and focusing attention on the aspects which are most important to manage them.
Are There Potentially Significant Long-Term Health Consequences of Exposure to Fine Airborne Particulate Matter PM10 to Personnel on the United Kingdom's Offshore Drilling Rigs?
Authors: Grainger Samuel Thomas 
Published: September 2021

Abstract
Drilling mud is a slurry comprising oil, water, and chemical additives. Mud is critical to drilling a modern well as it is circulated down a wellbore to remove rock cuttings and to power the drill bit. A significant volume of this drilling mud is used and later recirculated. The drilling mud warms at depth, creating steam, which holds suspended PM and dissolved chemicals. Many of the pieces of equipment are open or only partially enclosed, allowing for steam generation, while other processes generate aerosolised sprays. There is a significant potential for petroleum workers to become exposed and potentially suffer health effects because of drilling mud exposure. This study aims to find the major sources of PM10 on petroleum wells and quantify the levels of exposure and health hazard associated with drilling mud on petroleum rigs. A literature search was performed, which included all available materials which contained static or mobile concentrations of PM10 or oil mist within the UK or international petroleum drilling sites with a preference for North Sea operations. The study predicts the total PM10 by estimating the combined impact of both solid PM and oil mist. Using this conversion, it is also possible to estimate PM10 concentrations when using water-based muds. The work designates and discusses the expected health ramifications of excess exposure. A quantitative assessment of the risk of silicosis 15 years post-exposure is also calculated, predicting dire consequences to petroleum personnel in the long term. The exposure assessment methods, hygienic standards, and preventive measures are also addressed briefly.
Ensuring That Fatigue is Managed in Oil and Gas Operations
Authors: Holmes Alexandra , Ruscitto Cristina , Booth Sarah 
Published: September 2021

Abstract
As in any shiftworking industry, fatigue poses a hazard to workers within the oil and gas industry, irrespective of job role and site location. In order to demonstrate adherence to health and safety regulations, operators can be expected to manage shift patterns and hours of work, such that they are appropriate and do not result in unnecessary levels of fatigue that may reduce the safety of the operation. Reliance on hours of work limitations (for example the European Working Time Directive) or industry normalised working patterns may no longer be considered sufficient to ensure that the risk posed by fatigue is appropriately managed.
This paper presents how a scientific approach can be applied and adapted to suit the context and the populations being studied in order to answer specific operational questions and provide tailored fatigue risk mitigations. It describes a method by which site and job role fatigue levels can be assessed, in order for appropriate controls to be implemented. It will use case studies to illustrate how data collection methods are tailored to reflect specific operational environments. Data collection is particularly important in parts of the industry where common shift arrangements differ from those which have historically been studied.
The method outlines an approach to rigorously assess contributors to fatigue and fatigue levels in an organisation following appropriate scientific methods. Both subjective and objective data are collected, using methods such as fatigue and sleepiness scales, sleep diaries and collection of objective sleep data using validated sleep tracking devices. The approach is specifically tailored to the population of interest – reflecting their shift pattern, and collecting further data on workload, task demand, and operational or location-specific factors (for example travel to site, onsite sleeping facilities, or sea sickness on floating platforms). The method also allows for inferences to be made about the impact of circadian misalignment and shift timing on sleep, performance and mood.
The method presented in this paper has been used in field data collection in two very different environments. These studies are used as case studies to examine how the methodology can be tailored to ensure that the collected data are appropriate to the operation being studied, and lessons learned to improve the methods in the future.
Industry First AI-Powered Fully Automated Safety Observation System Deployed to Global Offshore Fleet
Authors: Brown Nassima , Brown Adrian , Degupta Abhijeet , Quinn Barry , Stringer Dustin , Yankov Bozhidar 
Published: September 2021

Abstract
As the oil and gas industry is facing tumultuous challenges, adoption of cutting-edge digital technologies has been accelerated to deliver safer, more efficient operations with less impact on the environment.
While advanced AI and other digital technologies have been rapidly evolving in many fields in the industry, the HSE sector is playing catch-up. With the increasing complexity of risks and safety management processes, the effective application of data-driven technologies has become significantly harder, particularly for international organizations with varying levels of digital readiness across diverse global operations. Leaders are more cautious to implement solutions that are not fit-for purpose, due to concerns over inconsistencies in rolling out the program across international markets and the impact this may have on ongoing operations.
This paper describes how the effective application of Artificial intelligence (AI) and Machine Learning (ML) technologies have been used to engineer a solution that fully digitizes and automates the end-to-end offshore behavior-based safety program across a global offshore fleet; optimizing a critical safety process used by many leading oil & gas organization to drive positive workplace safety culture. The complex safety program has been transformed into clear, efficient and automated workflow, with real-time analytics and live transparent dashboards which detail critical safety indicators in real time, aiding decision-making and improving operational performance.
The novel behavior-based safety digital solution, referred to as 3C observation tool within Noble drilling, has been built to be fully aligned with the organization's safety management system requirements and procedures, using modern and agile tools and applications for fully scalability and easy deployment. It has been critical in sharpening the offshore safety observation program across global operations, resulting in a boost of the workforce engagement by 30%, and subsequently increasing safety awareness skill set attainment; improving overall offshore safety culture, all while reducing operating costs by up to 70% and cutting carbon footprint through the elimination of 15,000 manhours and half a million paper cards each year, when compared to previously used methods and workflows