bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transforming with Our People – Leading Business Transformation Through People Development
Authors: Ho Boon Keng , Paul Christelle Saint , Daniseviciute Giedre 
Published: October 2023

Abstract
TotalEnergies plays an active role in the transformation that is underway in the energy industry and the vision is to be a Carbon-Neutral Company by 2050. To attain this goal, strategic investment policy was designed to accelerate access to low-carbon solutions (electricity and renewable energies, biogas and biofuels, low carbon fuels, CCS) while continuing to meet the world's current energy demand.
As TotalEnergies enter a significant phase (Year 2020 to 2030) in the transformation into a multi-energy company, a clear business transformation roadmap has been created to transform TotalEnergies to reinvent energy. This includes the element of transforming our workforce to meet the new challenges and environment.
In 2019, "Better Together", a worldwide People Ambition project was launched, in response to employees’ expectations. The ambition has three workstreams: to develop every employee, promoting the manager-coach and to promote a positive work environment by listening to employees’ concerns. The workstreams have been translated into concrete projects, to quickly anchor the changes in the daily lives of employees. More than 400 talent developers (TDs) are actively working closely with employees, to provide each employee individualized support and career coaching. There is also a revamp in the internal mobility process to provide more support and transparency for employees in the internal recruitment process. More than 35 000 vacancies have been published on an internal Job Posting platform since the introduction of Better Together in 2019.
Job mobility (functional and geographical) and continuous training are essential levers to develop everyone's skills and employability. These allowed our talents to develop the skills that is relevant to the changing business challenges. Managers are also empowered and trained in their role as manager-coaches, to support team development. This is a significant step to changing the managerial culture as we move to a more collaborative approach of management.
To support employees in this transformation, Transforming with our People program was launched in 2022 as part of the evolution of Better Together ambition. A program for supporting our teams through the transformation, it has three major workstreams: "Listening", "Informing", "Learning" and an additional workstream for young people at TotalEnergies, providing everybody with an opportunity to learn about and buy into TotalEnergies’ ambition, to develop and learn new skills, establishing a broad-energy culture. The goal is to be part of the energy transition, a fair transition for everyone.
TotalEnergies People Ambition encompasses a holistic approach covering topics like employee engagement, developing talents, promoting diversity & inclusion, and well-being of employees. This proposed paper focuses on the People Development principles and initiatives adopted to drive the transformation.
A Unique Development Model for Paraprofessionals at Engineering Organization
Authors: Al-Qarni Hassan Saeed 
Published: October 2023

Abstract
This paper presents a successful human resources development model for paraprofessional personnel in an engineering entity. A healthy organization must sustain reliable workforce through comprehensive and well-structured development. A development program for Engineering Aides (EA) and Assistant Engineers (AE) was mandated to ensure progressive development throughout paraprofessional career life. The main objective of the program is to foster knowledge of paraprofessionals through proper integration between soft skills and technical engineering concepts in a steady and efficient manner. In addition to the knowledge building process, an administrative structure has been introduced to ensure proper governance of the program as well as sustain progression and alignment to the organizational strategy towards human resources development.
Large Language Models Trained on Equipment Maintenance Text
Authors: Abijith P Y , Patidar Piyush , Nair Gaurav , Pandya Rohan 
Published: October 2023

Abstract
Work orders, equipment information, technical records and best practices documents contain within them a wealth of insights related to Equipment Maintenance which can be unlocked with Natural Language Processing tasks like classification, clustering, named entity recognition or part of speech tagging. But obtaining large enough labelled data sets in Equipment Maintenance domain manually is prohibitive and very expensive. This lack of labeled Equipment Maintenance data can be overcome with Large Language Models (LLMs) such as GPT-3, BERT that are pretrained transformer networks, considered state-of-the-art when it comes to Natural Language Processing (NLP) tasks. However, the vocabulary understood by these LLMs are mostly from English Language and need to be fine-tuned to understand industry and organization specific vocabulary and acronyms.
This paper explores the potential of a domain specific LLM model for oil and gas industries. Data that are of good quality and that provide a comprehensive overview of industry are collected. This corpus of text contains documents like work orders, equipment data and technical documents. A custom tokenizer is trained on this data to identify domain specific terminology. A comparative study is done with other off-the-shelf tokenizers: BERT and RoBERTa, to compare the effectiveness of the tokenization.
With millions of work orders and equipment documents, training pipelines had to parallelized so that training can occur on multiple GPUs. A comprehensive study of multiple training methods is done in this paper. Model and tokenizer developed were packaged and archived to be consumed in machine learning pipelines to specific use-cases across the organization. For an organization adopting digital transformation, the availability of an organization specific LLM is an enabler to extract insights from millions of documents containing free text. The applicability of such models spans across multiple disciplines like Maintenance, Reliability, Safety etc. and streamlines the development of highly accurate and robust text analytics.
Prescriptive Analytics of a Surface Safety Valve at the Edge
Authors: Gharib H. , Whitehead M. , Chin M. , Flesher H. 
Published: October 2023

Abstract
A Surface Safety Valve (SSV) on wellheads and flow lines is the last line of defense to protect personnel, environment, and assets from potential catastrophic damages. With the rising demand to reduce greenhouse gases (GHG), spills, and limit operational footprints, digital transformation combined with electrification and smart algorithms are critical steps towards achieving this goal.
This paper presents the recent development of a surface safety valve (SSV) controlled by an IOT-based smart emergency shutdown (ESD) system that integrates, at the wellhead, an electro-hydraulic power unit with control elements, sensors, edge computer/controller, and remote connectivity in a hybrid edge-cloud platform. Real-time data from position, pressure, and temperature sensors digitally transform the SSV and are used to feed the diagnostic algorithms. In addition to triggering an ESD event, these algorithms monitor the condition of the SSV and control circuit. This is conducted through coupling the real-time processed data with physics-based models to evaluate the SSV system health.
Evaluation of the SSV and smart ESD system performance was conducted using a laboratory test unit and the major components were field tested. Sensors and control elements data was processed into three primary sets of key performance indicators (KPIs): valve health, hydraulic circuit and actuation health, and hydraulic leakage detection. The effect of varying the process line pressure on the valve signature was evaluated and compared to the available physics-based models, which showed close correlation. Moreover, coupling of the experimental data with the physics-based models was able to solve for the individual thrust load components including valve gate drag, stem force, friction, and spring pre-load from the total measured pressure. The response time during SSV closing or partial stroking was evaluated at incremental time steps to identify the source of potential malfunction in the control circuit and SSV. An emulation of hydraulic leakage revealed distinctive pressure signatures between the low- and high-pressure circuits, thus identifying the location of the leakage in the hydraulic circuit. This allows locating the exact source of malfunction in the SSV, while running prescriptive analytics to suggest component specific corrective actions.
Most of the published SSV condition monitoring efforts focus on the process valve health with less attention to the hydraulic circuit, which is a critical element of the overall SSV reliability and performance. The presented smart ESD system bridges this gap by digitally transforming the SSV to provide prescriptive analytics not only to process valve components, but also the hydraulic circuit and actuator components.
De-Manning Offshore Operations and Reducing Carbon Emissions with Autonomous Subsea Drones
Authors: Cavallini F. , Massari G. , Nevoso C. , Quick A. , Alexander T. 
Published: October 2023

AbstractAt the conclusion of a long and exhaustive in-shore and field-testing programme, leading to commercial readiness, this paper presents the latest state-of-the-art developments in autonomous asset integrity inspection for subsea pipelines and structures by means of autonomous subsea drone. Further, the paper reviews and provides analysis of the benefits of such technology to operators in a shallow water scenario. The presented application case, specific for the water depths that characterize the Middle East area, shows that tangible benefits can be obtained both in terms of de-manning offshore operations, reduction in carbon emissions, improvement in data quality, improved weather workability and responsiveness to urgent callouts. In particular, the existing capabilities offered by battery technologies allow a subsea operational range adequate to cover typical field layouts, with launching/recovery from a platform. Furthermore, and with specific reference to shallow water applicability, the extensive testing at Saipem premises, in 8 to 20m water depth, truly represents the ideal de-risking prior to a potential deployment in the Arabian Gulf area, having autonomous inspections as main focus. This paper presents the very latest status update for subsea autonomous and resident drone operations with data quantifying and validating the safety, carbon emissions, time-savings and cost benefits compared to conventional pipeline and subsea inspection methods, specifically for the Middle East area.
An AI-Driven Multiwell Placement and Scheduling Method
Authors: Al Tandi A. A. , Tahir S. , Elfeel M. Ahmed , Belayouni N. , Gupta D. , Ayoubi R. , Shaik I. , Hernandez C. , Su S. , Gubbala M. , Majhi S. , Rat C. , Ramatullayev S. , Mustapha H. , Amri M. 
Published: October 2023

AbstractWell placement optimization is important for informed field investment decisions. Numerical simulation models are usually utilized for well placement optimization, as they offer a detailed account of subsurface reservoirs. However, a full loop optimization using numerical simulation models makes this process intractable. Instead, many assets and reservoir engineers follow heuristic or trial-and-error approaches, leading to suboptimal results. This paper introduces a novel method for well placement optimization and scheduling based on AI-proxy models.Our approach relies on generating an Ensemble of simulation runs (100s-1000s), varying field-level and well configuration parameters. We follow a loosely coupled process to solve the field control parameters and well placement optimization problems. Output data from the field, well and completion levels are collected automatically from the simulation models. Thereafter, several Machine Learning (ML) meta-models are trained to predict field-level production, well drilling and abandonment schedules, and the performance of production and injection wells. The outcome is selected well trajectories based on opportunity maps generated using the meta-models.The new workflow was first tested on a synthetic reservoir model. A sensitivity study of 275 simulation cases was performed varying reservoir management and well configuration parameters. It was observed that although the petrophysical properties such as rock quality and hydrocarbon saturations were not part of the ML model training, they were well reflected in the opportunity maps. This provided assurance that the model captures the basic static and dynamic controls on well performance. The model accuracy was improved further by adding features such as the perforation top depth. Additional drilling constraints, such as the presence of large faults, had to be considered to ensure the validity of the optimized well locations. The best simulation case from the sensitivity study was selected based on specific economic factors and adopted as a comparison base case for the new well optimization method. The original wells were replaced with an optimized set of well obtained with our deep learning and optimization workflow. This resulted in a 10% increase in total oil production combined with 50% decrease in the amount of water production.Modern cloud/local computational resources can expedite full-loop optimization processes. However, both computation efficiency and elasticity do not address the iterative nature of classic well optimization methods. The method presented in this paper attempts further speedup by leveraging ML.
Future Workforce Planning - Building a Future-Ready Organization Using a Prospective Study Approach
Authors: HO Boon Keng 
Published: October 2023

Abstract
To stay competitive in a rapidly changing world, it is imperative to rethink the business model, workforce, and skills with the future in mind. This is a forward-looking approach to imagine the different potential scenarios of the future and use that as a frame to design the how it will impact the business.
With this ‘future’ as the vision, a back casting approach was adopted to then anticipate the changes needed today to help to transform the business successfully into the desired Future-Ready organization.
This prospective study is done based mainly on qualitative research. An external "prospectivist" (Future analyst) was engaged to work with the HR team to conduct this study. A series of external and internal research, as well as internal interviews with business leaders were conducted to imagine the potential business scenarios of the future. A backcasting strategy was then adopted to compare the current workforce and to anticipate the required evolution of the business, workforce, and competencies.
An innovative approach was adopted using big data for external competency benchmarking. Business leaders from each business were involved in the early stages from choosing the external sectors to benchmark against and the internal key jobs to be benchmarked, to reflecting collectively on the results to design action plans.
Other than the direct interventions for each business, the results were reviewed at a macro level to create a Future Workforce Plan for the organization. Further interviews and discussions with management leaders to review the key findings had created specific manpower and HR action plans to develop the workforce.
These short and mid-term action plans provide the blueprint not only for workforce planning but to set the course on developing the workforce of the future that can steer the business transformations. The scope includes identifying job evolutions, workforce reskilling and upskilling needs, potential new jobs for the future (not existent in the company) and approaches to build the workforce (external recruitment - social communities, upskilling of employees, outsourcing - gig workforce).
Production Breakthrough from Channel Fracturing: A Seven-Year Journey in South Sulige Tight Gas Field
Authors: Fuyang Wu , Qian Ma , Libin Dai , Ting Jin , Tao He , Shengfang Yang , Yu Lei , Long Wang , Yifan Dong , Qiang Wang , Na Li , Yin Luo , Liang Zhao 
Published: October 2023

Abstract
South Sulige Operating Company (SSOC), a joint venture company between CNPC and TotalEnergies, has been the main operator in South Sulige gas field since 2011. After years of optimization on operation parameters, the annual post-fracturing production (absolute open flow, AOF) has reached a high plateau that outperforms offset blocks. Nevertheless, because of the fast-growing domestic gas demand, utilizing proper fracturing technology to further increase production has become crucial in recent years.
This paper describes the successful design evolution of channel fracturing technology in Sulige tight gas field through a 7-year trial. Unlike the homogeneous proppant pack created in a conventional fracturing job, channel fracturing makes open and highly conductive channels by forming proppant pillars in the fracture through pumping proppant-laden fluid and proppant-free fluid intermittently. These channels are beneficial for improving effective fracture length, facilitating fracturing fluid cleanup, minimizing pressure drawdown along the fracture, and thus improving post-fracturing production performance.
In 2016, the first channel fracturing trial was conducted on four wells in this field, with comparable treatment size to conventional jobs. The average 600-day cumulative production of target wells was 25% higher than that of offset wells, which demonstrated the enhancement to effective fracture half-length and thus more fracture-reservoir contact. In 2021, the design strategy was upgraded by incorporating a more aggressive pumping schedule to improve near-wellbore fracture conductivity while reducing fracturing fluid usage, with superior production results achieved from wells with mid to high reservoir quality. In 2022, the fracturing design strategy was set to maximize the production potential for high-quality wells with increased treatment size and further extended effective fracture length. This achieved production record of 87 MMscf/d AOF from a 2-layer vertical well. This result is even comparable to performance of offset horizontal wells. Besides the promising results, through the design evolution in these 7 years, a channel fracturing candidate selection workflow with associated design strategy has been established for a similar tight gas field.
Channel fracturing has been proved an effective tool to create extended effective fracture length and maximize single-well production in South Sulige tight gas field, with potential cost saving from needing fewer wells to deplete the same reservoir area. This technology, along with the properly designed workflow described in this paper, can also be widely adapted in other tight reservoirs.
Very Rich Laboratory Data Set Paves the Way for Miscible Gas Injection Evaluation Onshore Abu Dhabi
Authors: Larrouilh B. , Mogensen K. , Dehane A. 
Published: October 2023

AbstractPhase equilibrium calculations require experimental lab data to constrain component properties in an equation of state (EOS) model. In this work, we evaluated the miscibility behavior of four different injection gases for a reservoir onshore Abu Dhabi containing medium-light oil.The extensive experimental program included swelling and slimtube experiments using CO2 as well as three hydrocarbon gases, in addition to conventional black-oil experiments. Based on the large company PVT database, gas additions in the swelling experiment were carefully designed to reduce uncertainty on the transition point where the swollen fluid type changes from oil to gas. We developed several fully tuned EOS models by gradually lumping down while monitoring the match quality. Due to the richness of the injection gases, C2 (15 mol%), C3 (15 mol%), C4 (10 mol%) and CO2 were kept as separate components resulting in a 10-component final EOS description. The compositional data showed a distinct tail effect above C20, which we believe is a lab artifact. The plus fraction was therefore lumped back from C36 to C20 to characterize the composition from C20 up to C80. This step improved the match of the dead oil properties.While the swelling data were matched well, the slimtube recovery data yielded some uncertainty on the MMP estimate, especially for the CO2 test. We made use of the reported effluent gas composition to show that one of the points denoted as immiscible in the lab report is most likely miscible. We tested several EOS-based MMP algorithms and found them to give similar MMP estimates for the hydrocarbon gases whereas the CO2 MMP was predicted with a much larger spread. Our view is that for CO2 flooding, cell-based simulations capture the dynamic behavior of miscible displacement better than the key tie-line approach.The tuned EOS model reproduced the single-phase density very accurately, which is important for the saturation estimation in the transition zone. Several injection gases appear suitable for miscible flooding and further screening studies are now ongoing.
Excellent Engineering Solution in Compressor Area to Avoid Project Delay
Authors: AlQaydi Alya , Awadhi Ibrahim Al , Gongade Ashish Shivaji , Locharla Haribabu 
Published: October 2023

Abstract
Green field projects always have an advantage in terms of optimization because everything can be planned with a calculated time barrier. Whereas for brown filed projects, it is a difficult task due to multiple challenges such as limited shutdown time, limited layout space, accessibility for construction, and so on. This paper demonstrates an excellent engineering solution for overcoming these challenges while replacing the existing compressor without requiring major piping/civil modifications.
During a routine asset integrity assessment, it was decided to replace one of ADNOC GAS's existing compressors with a similar one that had the same footprint, nozzle sizes, and orientations. As a result, no significant engineering is required, and only minor adjustments may be required, which can be managed as residual engineering during construction. Consequently, the Purchase Order for the compressor was placed before any engineering activities based on the limited period of the shutdown window.
Since the existing compressor was very old and the design data was unknown, it was difficult to determine the nozzle loads. However, based on the new compressor design data/ nozzle loads, a stress analysis with existing piping configurations is required to ensure the piping system's integrity. It was identified that the new compressor nozzle's allowable loads are much lower as per the latest design code, API 661. The existing piping configuration is imposing more loads than expected due to the need for more flexibility in piping layout configuration. Moreover, the new compressor vendor denied the request to increase the nozzle loads. Consequently, the existing piping routing has to be modified to achieve flexibility in the piping system and meet the nozzle load requirements. With the available shutdown window, there was no time to obtain additional pipe material or change civil supports.
To overcome this situation, enhanced engineering with specialized stress analysis skills and advanced techniques were used to study various options for minimizing the piping/civil modifications as much as possible while meeting the nozzle loads. The best engineering techniques include utilizing the existing support locations, minimizing piping routing/support modifications, and minimizing civil and foundation modifications. By doing so, the compressor was installed without any changes to the vendor's technical requirements and with significant cost savings in implementation while adhering to the available shutdown window.